From 4fdac141a97c77973d12c3be15708a5fef7147e0 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Sat, 28 Feb 2026 00:59:03 +0800 Subject: [PATCH 01/28] feat: embed MCP server in ibis-server Docker image When ENABLE_MCP_SERVER=true, the ibis-server container automatically starts a Wren MCP server alongside the main FastAPI service, allowing AI agents to connect via streamable-http transport on port 9000. Co-Authored-By: Claude Opus 4.6 --- .github/workflows/build-image.yml | 1 + .github/workflows/stable-release.yml | 1 + ibis-server/Dockerfile | 9 ++++++- ibis-server/entrypoint.sh | 36 +++++++++++++++++++++++++++- ibis-server/justfile | 1 + mcp-server/app/wren.py | 21 +++++++++++----- mcp-server/docker/compose.yaml | 3 +++ 7 files changed, 64 insertions(+), 8 deletions(-) diff --git a/.github/workflows/build-image.yml b/.github/workflows/build-image.yml index 2c1bbdfa5..720a9a0b7 100644 --- a/.github/workflows/build-image.yml +++ b/.github/workflows/build-image.yml @@ -130,6 +130,7 @@ jobs: wren-core-py=./wren-core-py wren-core=./wren-core wren-core-base=./wren-core-base + mcp-server=./mcp-server outputs: type=image,name=${{ env.IBIS_IMAGE }},push-by-digest=true,name-canonical=true,push=true - name: Export digest run: | diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml index f6f08a258..3795ce1e2 100644 --- a/.github/workflows/stable-release.yml +++ b/.github/workflows/stable-release.yml @@ -178,6 +178,7 @@ jobs: wren-core-py=./wren-core-py wren-core=./wren-core wren-core-base=./wren-core-base + mcp-server=./mcp-server outputs: type=image,name=${{ env.IBIS_IMAGE }},push-by-digest=true,name-canonical=true,push=true - name: Export digest run: | diff --git a/ibis-server/Dockerfile b/ibis-server/Dockerfile index d93435fe6..bf25d539d 100644 --- a/ibis-server/Dockerfile +++ b/ibis-server/Dockerfile @@ -36,6 +36,9 @@ WORKDIR /app COPY . . RUN just install --without dev +# Install MCP server dependencies into the venv +RUN poetry run pip install "mcp[cli]>=1.19.0,<2" + FROM python:3.11-slim-bookworm AS runtime @@ -71,6 +74,10 @@ COPY resources ./resources/ COPY notebooks ./notebooks/ COPY wren/ ./wren/ +# Copy MCP server +COPY --from=mcp-server ./app /mcp-server/app/ +COPY --from=mcp-server ./mdl.schema.json /mcp-server/ + # Copy pyproject.toml and poetry.lock for poetry to work COPY pyproject.toml poetry.lock ./ @@ -93,6 +100,6 @@ RUN jupyter lab --generate-config --allow-root COPY entrypoint.sh ./ RUN chmod +x ./entrypoint.sh -EXPOSE 8000 8888 +EXPOSE 8000 8888 9000 ENTRYPOINT ["./entrypoint.sh"] diff --git a/ibis-server/entrypoint.sh b/ibis-server/entrypoint.sh index b9d61d9a0..942c451c8 100644 --- a/ibis-server/entrypoint.sh +++ b/ibis-server/entrypoint.sh @@ -44,6 +44,36 @@ else CMD_PREFIX="" fi +# Start MCP server in background if enabled +MCP_PID="" +if [[ "${ENABLE_MCP_SERVER}" == "true" ]]; then + export MCP_TRANSPORT="${MCP_TRANSPORT:-streamable-http}" + export MCP_HOST="${MCP_HOST:-0.0.0.0}" + export MCP_PORT="${MCP_PORT:-9000}" + export WREN_URL="${WREN_URL:-localhost:8000}" + + echo "Starting MCP Server (transport=${MCP_TRANSPORT}, port=${MCP_PORT})..." + cd /mcp-server && python -m app.wren & + MCP_PID=$! + cd /app + echo "MCP Server started (PID: ${MCP_PID})" +fi + +# Signal handler to clean up all processes +cleanup() { + echo "Shutting down..." + if [[ -n "${MCP_PID}" ]]; then + kill -TERM "${MCP_PID}" 2>/dev/null + wait "${MCP_PID}" 2>/dev/null + fi + if [[ -n "${GUNICORN_PID}" ]]; then + kill -TERM "${GUNICORN_PID}" 2>/dev/null + wait "${GUNICORN_PID}" 2>/dev/null + fi + exit 0 +} +trap cleanup SIGTERM SIGINT + # Start the WrenUvicornWorker with the specified configuration ${CMD_PREFIX} gunicorn app.main:app --bind 0.0.0.0:8000 \ -k app.worker.WrenUvicornWorker \ @@ -51,4 +81,8 @@ ${CMD_PREFIX} gunicorn app.main:app --bind 0.0.0.0:8000 \ --max-requests 1000 \ --max-requests-jitter 100 \ --timeout 300 \ - --graceful-timeout 60 + --graceful-timeout 60 & +GUNICORN_PID=$! + +# Wait for all background processes +wait diff --git a/ibis-server/justfile b/ibis-server/justfile index 5b1f03f3e..d19273eb0 100644 --- a/ibis-server/justfile +++ b/ibis-server/justfile @@ -61,6 +61,7 @@ docker-build: --build-context wren-core-py=../wren-core-py \ --build-context wren-core=../wren-core \ --build-context wren-core-base=../wren-core-base \ + --build-context mcp-server=../mcp-server \ . docker-run: diff --git a/mcp-server/app/wren.py b/mcp-server/app/wren.py index a889d59b4..924d8f195 100644 --- a/mcp-server/app/wren.py +++ b/mcp-server/app/wren.py @@ -7,15 +7,25 @@ import httpx from mcp.server.fastmcp import FastMCP from mcp.types import ToolAnnotations -from dto import Manifest, TableColumns -from utils import dict_to_base64_string, json_to_base64_string -mcp = FastMCP("Wren Engine") +try: + from dto import Manifest, TableColumns + from utils import dict_to_base64_string, json_to_base64_string +except ImportError: + from app.dto import Manifest, TableColumns + from app.utils import dict_to_base64_string, json_to_base64_string load_dotenv() + +MCP_TRANSPORT = os.getenv("MCP_TRANSPORT", "stdio") +MCP_HOST = os.getenv("MCP_HOST", "0.0.0.0") +MCP_PORT = int(os.getenv("MCP_PORT", "9000")) + +mcp = FastMCP("Wren Engine", host=MCP_HOST, port=MCP_PORT) + WREN_URL = os.getenv("WREN_URL", "localhost:8000") USER_AGENT = "wren-app/1.0" -MDL_SCHEMA_PATH = "mdl.schema.json" +MDL_SCHEMA_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "mdl.schema.json") connection_info_path = os.getenv("CONNECTION_INFO_FILE") mdl_path = os.getenv("MDL_PATH") @@ -531,5 +541,4 @@ async def health_check() -> str: if __name__ == "__main__": - # Initialize and run the server - mcp.run(transport="stdio") + mcp.run(transport=MCP_TRANSPORT) diff --git a/mcp-server/docker/compose.yaml b/mcp-server/docker/compose.yaml index ab442f622..62e29fc34 100644 --- a/mcp-server/docker/compose.yaml +++ b/mcp-server/docker/compose.yaml @@ -3,8 +3,11 @@ services: image: ghcr.io/canner/wren-engine-ibis:latest ports: - "8000:8000" + - "9000:9000" environment: WREN_ENGINE_ENDPOINT: http://java-engine:8080 + ENABLE_MCP_SERVER: "true" + MCP_PORT: "9000" volumes: - ./../etc:/usr/src/app/etc java-engine: From cdd4f943dbaba822676873133660339d76331a2b Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Sun, 1 Mar 2026 21:21:12 +0800 Subject: [PATCH 02/28] feat: add wren-agent module and MCP mdl_tools integration - New wren-agent Python library for agentic MDL generation - PydanticAI agent with SQLAlchemy DB exploration tools - Pydantic MDL models matching official WrenAI schema - SkillStore: tag filtering + TF-IDF semantic search - MemoryStore: persistent JSON key-value store - validate_mdl: JSON Schema + ibis-server dry-plan validation - 58 tests passing - mcp-server: register MDL agent tools via mdl_tools.py - mdl_chat / mdl_reset_session / mdl_list_sessions tools - Graceful fallback if wren-agent not installed - MDL_AGENT_* env vars for skills, memory, max_skills - Updated README with installation guide and workflow example --- mcp-server/.env.example | 58 + mcp-server/README.md | 123 + mcp-server/app/mdl_tools.py | 179 + mcp-server/app/wren.py | 5 + mcp-server/justfile | 5 +- mcp-server/pyproject.toml | 8 + mcp-server/uv.lock | 2771 ++++++++++++-- wren-agent/.env.example | 7 + wren-agent/README.md | 53 + wren-agent/pyproject.toml | 44 + wren-agent/src/wren_agent/__init__.py | 15 + wren-agent/src/wren_agent/agent.py | 294 ++ wren-agent/src/wren_agent/deps.py | 26 + wren-agent/src/wren_agent/memory.py | 141 + wren-agent/src/wren_agent/models.py | 202 ++ wren-agent/src/wren_agent/prompts.py | 60 + wren-agent/src/wren_agent/skills.py | 307 ++ wren-agent/src/wren_agent/tools/__init__.py | 1 + wren-agent/src/wren_agent/tools/db.py | 166 + wren-agent/src/wren_agent/tools/memory.py | 83 + wren-agent/src/wren_agent/tools/validate.py | 86 + wren-agent/tests/test_agent.py | 627 ++++ wren-agent/uv.lock | 3569 +++++++++++++++++++ 23 files changed, 8626 insertions(+), 204 deletions(-) create mode 100644 mcp-server/.env.example create mode 100644 mcp-server/app/mdl_tools.py create mode 100644 wren-agent/.env.example create mode 100644 wren-agent/README.md create mode 100644 wren-agent/pyproject.toml create mode 100644 wren-agent/src/wren_agent/__init__.py create mode 100644 wren-agent/src/wren_agent/agent.py create mode 100644 wren-agent/src/wren_agent/deps.py create mode 100644 wren-agent/src/wren_agent/memory.py create mode 100644 wren-agent/src/wren_agent/models.py create mode 100644 wren-agent/src/wren_agent/prompts.py create mode 100644 wren-agent/src/wren_agent/skills.py create mode 100644 wren-agent/src/wren_agent/tools/__init__.py create mode 100644 wren-agent/src/wren_agent/tools/db.py create mode 100644 wren-agent/src/wren_agent/tools/memory.py create mode 100644 wren-agent/src/wren_agent/tools/validate.py create mode 100644 wren-agent/tests/test_agent.py create mode 100644 wren-agent/uv.lock diff --git a/mcp-server/.env.example b/mcp-server/.env.example new file mode 100644 index 000000000..51efbb020 --- /dev/null +++ b/mcp-server/.env.example @@ -0,0 +1,58 @@ +# ============================================================================= +# Wren MCP Server — Environment Variables +# Copy this file to .env and fill in your values. +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Core (required) +# ----------------------------------------------------------------------------- + +# URL of the Wren ibis-server (host:port, no scheme) +WREN_URL=localhost:8000 + +# Absolute path to the connection info JSON file for your data source +# See README for the required fields per data source type. +CONNECTION_INFO_FILE=/path/to/connection_info.json + +# Absolute path to the MDL JSON file to pre-load on startup (optional) +# If not set, MDL must be deployed at runtime via the 'deploy' tool. +MDL_PATH=/path/to/mdl.json + +# MCP transport: "stdio" (default, for desktop clients) or "sse" (for HTTP) +# MCP_TRANSPORT=stdio + +# Host and port used only when MCP_TRANSPORT=sse +# MCP_HOST=0.0.0.0 +# MCP_PORT=9000 + +# ----------------------------------------------------------------------------- +# MDL Agent (optional — requires `just install-mdl-agent`) +# ----------------------------------------------------------------------------- + +# LLM model string passed to pydantic-ai. +# Format: ":" +# Examples: +# anthropic:claude-3-5-sonnet-latest +# openai:gpt-4o +# google-gla:gemini-2.0-flash +# PYDANTIC_AI_MODEL=anthropic:claude-3-5-sonnet-latest + +# API keys for your chosen LLM provider (only the relevant one is needed) +# ANTHROPIC_API_KEY=sk-ant-... +# OPENAI_API_KEY=sk-... +# GEMINI_API_KEY=... + +# ibis-server endpoint for MDL dry-plan validation (usually same host as WREN_URL) +# WREN_ENGINE_ENDPOINT=http://localhost:8000 + +# Directory containing *.md / *.txt skill files loaded at startup. +# Skills provide domain knowledge (naming conventions, patterns, etc.) that the +# agent selects from semantically on each turn. +# MDL_AGENT_SKILLS_DIR=./skills + +# JSON file path for persistent cross-session memory. +# The agent can read/write entries during a conversation and they survive restarts. +# MDL_AGENT_MEMORY_PATH=~/.wren/memory.json + +# Maximum number of skills injected per turn (TF-IDF semantic ranking, default 5) +# MDL_AGENT_MAX_SKILLS=5 diff --git a/mcp-server/README.md b/mcp-server/README.md index 5884d26c2..c74e0c866 100644 --- a/mcp-server/README.md +++ b/mcp-server/README.md @@ -139,6 +139,129 @@ Optional follow-ups: --- +## MDL Agent (Optional) + +The **MDL Agent** is an optional extension that adds three MCP tools for **agentic MDL generation**. +Instead of writing MDL JSON by hand, you describe your database and the agent explores the schema, +builds the manifest, and hands it off to the existing `deploy` tool — all within the same MCP session. + +### Prerequisites + +- An LLM provider API key (Anthropic, OpenAI, etc.) +- The `wren-agent` package (lives inside this monorepo at `../wren-agent`) + +### Installation + +Install `wren-agent` as an editable path dependency into the MCP server's virtual environment: + +```sh +# inside mcp-server/ +just install-mdl-agent +# or, without just: +uv pip install --editable "../wren-agent" +``` + +> The server uses a **graceful fallback**: if `wren-agent` is not installed the three MDL tools +> are simply not registered, and all other tools continue to work unchanged. + +### Environment Variables + +| Variable | Required | Default | Description | +|---|---|---|---| +| `PYDANTIC_AI_MODEL` | **Yes** | `anthropic:claude-3-5-sonnet-latest` | LLM to use, e.g. `openai:gpt-4o` | +| `WREN_ENGINE_ENDPOINT` | No | — | ibis-server URL for MDL dry-plan validation | +| `MDL_AGENT_SKILLS_DIR` | No | — | Directory of `*.md` / `*.txt` skill files loaded at startup | +| `MDL_AGENT_MEMORY_PATH` | No | — | JSON file path for persistent cross-session memory | +| `MDL_AGENT_MAX_SKILLS` | No | `5` | Max skills injected per turn (semantic ranking) | + +Add these to your `.env` file or to the `"env"` block in your MCP config: + +```json +{ + "mcpServers": { + "wren": { + "command": "uv", + "args": [ + "--directory", "/ABSOLUTE/PATH/TO/wren-engine/mcp-server", + "run", "app/wren.py" + ], + "env": { + "WREN_URL": "localhost:8000", + "CONNECTION_INFO_FILE": "/path/connection.json", + "MDL_PATH": "/path/mdl.json", + "PYDANTIC_AI_MODEL": "anthropic:claude-3-5-sonnet-latest", + "ANTHROPIC_API_KEY": "", + "WREN_ENGINE_ENDPOINT": "http://localhost:8000", + "MDL_AGENT_MEMORY_PATH": "~/.wren/memory.json" + } + } + } +} +``` + +### Available Tools + +| Tool | Description | +|---|---| +| `mdl_chat(message, session_id?)` | Send a message to the MDL generation agent. Returns a follow-up question or a completed MDL JSON. | +| `mdl_reset_session(session_id?)` | Clear conversation history and DB connection for a session. | +| `mdl_list_sessions()` | List all active sessions and their message counts. | + +### Example Workflow + +The following example shows how an AI agent (Claude, Copilot, etc.) would use the MDL tools +in a conversation: + +``` +User → "Generate an MDL for my PostgreSQL ecommerce database." + +Agent → mdl_chat("Generate an MDL for my PostgreSQL ecommerce database.") + ← [MDL Agent asks]: Please provide your PostgreSQL connection string. + +Agent → mdl_chat("postgresql://user:pass@localhost:5432/shop") + ← [MDL Agent asks]: I found tables: orders, customers, products, order_items. + Should I include all of them? + +Agent → mdl_chat("Yes, include all.") + ← [MDL Ready — use the 'deploy' tool with this manifest] + { + "catalog": "wren", + "schema": "public", + "dataSource": "POSTGRES", + "models": [ ... ] + } + +Agent → deploy() + ← "MDL deployed successfully" +``` + +Multiple independent sessions can run in parallel using different `session_id` values: + +``` +mdl_chat("...", session_id="project_a") +mdl_chat("...", session_id="project_b") +``` + +### Skills + +You can inject domain knowledge (naming conventions, relationship patterns, etc.) by providing a +skills directory. Each `.md` or `.txt` file in the directory becomes a skill. On every turn, +the agent automatically selects the most relevant skills using TF-IDF cosine similarity — only +the top `MDL_AGENT_MAX_SKILLS` are injected, so the context stays focused. + +``` +MDL_AGENT_SKILLS_DIR=./skills/ +``` + +``` +skills/ + postgres_conventions.md # "Use bigint for all primary keys…" + ecommerce_patterns.md # "Order tables should reference customers via customer_id…" + bigquery_tips.md # "Use STRUCT for nested columns…" +``` + +--- + ## Additional Resources - **Wren Engine Documentation**: [Wren AI](https://getwren.ai/) diff --git a/mcp-server/app/mdl_tools.py b/mcp-server/app/mdl_tools.py new file mode 100644 index 000000000..cf0b54aef --- /dev/null +++ b/mcp-server/app/mdl_tools.py @@ -0,0 +1,179 @@ +"""MDL generation tools — powered by wren-agent. + +Registers three tools onto the shared FastMCP instance: + +- ``mdl_chat`` — multi-turn conversation to build an MDL manifest +- ``mdl_reset_session`` — clear a session (history + DB connection) +- ``mdl_list_sessions`` — list active sessions + +Requires ``wren-agent`` to be installed. If it is missing the module +loads silently and no tools are registered, so the rest of the server +continues to work unchanged. + +Environment variables (all optional): + +- ``PYDANTIC_AI_MODEL`` — LLM to use, e.g. ``anthropic:claude-3-5-sonnet-latest`` +- ``WREN_ENGINE_ENDPOINT`` — ibis-server URL for MDL dry-plan validation +- ``MDL_AGENT_SKILLS_DIR`` — directory of *.md / *.txt skill files to load on startup +- ``MDL_AGENT_MEMORY_PATH`` — JSON file path for persistent cross-session memory +- ``MDL_AGENT_MAX_SKILLS`` — max skills injected per turn (default 5) +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field + +from mcp.server.fastmcp import FastMCP +from mcp.types import ToolAnnotations + +try: + from wren_agent import MDLAgent, AgentQuestion, SkillStore, MemoryStore + + _AVAILABLE = True +except ImportError: + _AVAILABLE = False + + +# --------------------------------------------------------------------------- +# Session state +# --------------------------------------------------------------------------- + + +@dataclass +class _Session: + agent: "MDLAgent" + history: list = field(default_factory=list) + + +_sessions: dict[str, _Session] = {} + +# Shared across all sessions (loaded once at startup) +_skill_store: "SkillStore | None" = None +_memory: "MemoryStore | None" = None + + +def _init_shared_state() -> None: + """Load skills and memory from environment variables (called once).""" + global _skill_store, _memory + + skills_dir = os.getenv("MDL_AGENT_SKILLS_DIR") + if skills_dir and os.path.isdir(skills_dir): + _skill_store = SkillStore() + _skill_store.load_dir(skills_dir) + skill_count = len(_skill_store) + print(f"[MDL Agent] Loaded {skill_count} skills from {skills_dir}") # noqa: T201 + + memory_path = os.getenv("MDL_AGENT_MEMORY_PATH") + if memory_path: + _memory = MemoryStore(persist_path=memory_path) + print(f"[MDL Agent] Memory store: {memory_path}") # noqa: T201 + + +def _get_or_create_session(session_id: str) -> "_Session": + if session_id not in _sessions: + max_skills = int(os.getenv("MDL_AGENT_MAX_SKILLS", "5")) + _sessions[session_id] = _Session( + agent=MDLAgent( + ibis_server_url=os.getenv("WREN_ENGINE_ENDPOINT"), + skill_store=_skill_store, + memory=_memory, + max_skills=max_skills, + ) + ) + return _sessions[session_id] + + +# --------------------------------------------------------------------------- +# Tool registration +# --------------------------------------------------------------------------- + + +def register_mdl_tools(mcp: FastMCP) -> None: + """Register MDL agent tools onto *mcp*. No-op if wren-agent is missing.""" + if not _AVAILABLE: + print("[MDL Agent] wren-agent not installed — MDL tools not registered.") # noqa: T201 + return + + _init_shared_state() + + @mcp.tool( + annotations=ToolAnnotations( + title="MDL Chat", + readOnlyHint=False, + ), + ) + async def mdl_chat(message: str, session_id: str = "default") -> str: + """Generate or refine a Wren MDL manifest through a guided conversation. + + The agent explores the database schema and produces a validated MDL + manifest ready for deployment with the ``deploy`` tool. + + **Typical workflow**: + + 1. Say what database you want to model (e.g. "I want to model my PostgreSQL + ecommerce database"). + 2. The agent may ask for a connection string or clarifying questions. + 3. When MDL is ready, a JSON block is returned — copy it into ``deploy``. + + You can run multiple independent sessions in parallel using different + ``session_id`` values. + + Args: + message: Your message or answer to the agent's question. + session_id: Conversation ID. Reuse the same ID across turns to + continue an existing session. + + Returns: + Either a follow-up question prefixed with ``[MDL Agent asks]`` + or a completed MDL JSON prefixed with ``[MDL Ready]``. + """ + session = _get_or_create_session(session_id) + resp = await session.agent.run(message, message_history=session.history) + session.history = resp.message_history + + if isinstance(resp.result, AgentQuestion): + return f"[MDL Agent asks]: {resp.result.question}" + + mdl_json = resp.result.model_dump_json(by_alias=True, indent=2) + return f"[MDL Ready — use the 'deploy' tool with this manifest]\n\n{mdl_json}" + + @mcp.tool( + annotations=ToolAnnotations( + title="Reset MDL Session", + readOnlyHint=False, + ), + ) + async def mdl_reset_session(session_id: str = "default") -> str: + """Reset an MDL generation session. + + Clears the conversation history and the database connection held inside + the session. The next ``mdl_chat`` call on this ID starts fresh. + + Args: + session_id: Session to reset (default: ``"default"``). + """ + if session_id in _sessions: + del _sessions[session_id] + return f"Session '{session_id}' has been reset." + return f"Session '{session_id}' not found — nothing to reset." + + @mcp.tool( + annotations=ToolAnnotations( + title="List MDL Sessions", + readOnlyHint=True, + ), + ) + async def mdl_list_sessions() -> str: + """List all active MDL generation sessions and their message counts. + + Returns a summary so you can track which sessions are in progress or + decide which one to continue. + """ + if not _sessions: + return "No active MDL sessions." + lines = [ + f"- '{sid}': {len(s.history)} messages in history" + for sid, s in _sessions.items() + ] + return "\n".join(lines) diff --git a/mcp-server/app/wren.py b/mcp-server/app/wren.py index 924d8f195..8674d7001 100644 --- a/mcp-server/app/wren.py +++ b/mcp-server/app/wren.py @@ -11,9 +11,11 @@ try: from dto import Manifest, TableColumns from utils import dict_to_base64_string, json_to_base64_string + from mdl_tools import register_mdl_tools except ImportError: from app.dto import Manifest, TableColumns from app.utils import dict_to_base64_string, json_to_base64_string + from app.mdl_tools import register_mdl_tools load_dotenv() @@ -540,5 +542,8 @@ async def health_check() -> str: return "Wren Engine is not healthy" +# Register MDL agent tools (no-op if wren-agent is not installed) +register_mdl_tools(mcp) + if __name__ == "__main__": mcp.run(transport=MCP_TRANSPORT) diff --git a/mcp-server/justfile b/mcp-server/justfile index 959e7857f..5d13bf092 100644 --- a/mcp-server/justfile +++ b/mcp-server/justfile @@ -2,7 +2,7 @@ default: @just --list --unsorted build-core: - cd ../wren-core-py && just install && just build + cd ../wren-core-py && just install && just develop core-wheel-path := "../wren-core-py/target/wheels/wren_core_py-*.whl" @@ -10,3 +10,6 @@ install-core: # just build-core # Using pip install to avoid adding wheel to pyproject.toml uv pip install --force-reinstall {{ core-wheel-path }} + +install-mdl-agent: + uv pip install --editable "../wren-agent" diff --git a/mcp-server/pyproject.toml b/mcp-server/pyproject.toml index 0896bddf9..b81860c4d 100644 --- a/mcp-server/pyproject.toml +++ b/mcp-server/pyproject.toml @@ -12,3 +12,11 @@ dependencies = [ "python-dotenv>=1.0.1", "ruff>=0.11.0", ] + +[project.optional-dependencies] +mdl-agent = [ + "wren-agent", +] + +[tool.uv.sources] +wren-agent = { path = "../wren-agent", editable = true } diff --git a/mcp-server/uv.lock b/mcp-server/uv.lock index abe7af1bd..3beb7f34a 100644 --- a/mcp-server/uv.lock +++ b/mcp-server/uv.lock @@ -2,6 +2,142 @@ version = 1 revision = 1 requires-python = ">=3.11" +[[package]] +name = "ag-ui-protocol" +version = "0.1.13" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pydantic" }, +] +sdist = { url = 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@@ -0,0 +1,7 @@ +# LLM Provider API Keys (set the one matching your chosen model) +ANTHROPIC_API_KEY=your_anthropic_api_key_here +OPENAI_API_KEY=your_openai_api_key_here + +# Optional: Wren ibis-server endpoint for MDL dry-plan validation +# If not set, validate_mdl will only run JSON Schema validation +WREN_ENGINE_ENDPOINT=http://localhost:8000 diff --git a/wren-agent/README.md b/wren-agent/README.md new file mode 100644 index 000000000..21f633844 --- /dev/null +++ b/wren-agent/README.md @@ -0,0 +1,53 @@ +# wren-agent + +AI agent for agentic MDL generation for [Wren Engine](https://github.com/Canner/WrenAI). + +## Overview + +`wren-agent` is a Python library that uses [PydanticAI](https://ai.pydantic.dev/) to drive a multi-turn conversation with the user, explores a connected database via SQLAlchemy, and outputs a valid [Wren MDL](https://github.com/Canner/WrenAI/tree/main/wren-mdl) manifest. + +## Usage + +```python +import asyncio +from wren_agent import MDLAgent + +async def main(): + agent = MDLAgent() # uses PYDANTIC_AI_MODEL env var, defaults to claude-3-5-sonnet-latest + history = [] + + # Turn 1: agent asks for connection string + resp = await agent.run("I want to generate MDL for my database") + print(resp.result) # AgentQuestion(question="Please provide your connection string…") + history = resp.message_history + + # Turn 2: provide connection string + resp = await agent.run("postgresql://user:pass@localhost/mydb", message_history=history) + # resp.result → MDLManifest(...) + +asyncio.run(main()) +``` + +## Installation + +```bash +# Basic (no DB driver) +uv add wren-agent + +# With PostgreSQL support +uv add "wren-agent[postgres]" + +# With MySQL + DuckDB +uv add "wren-agent[mysql,duckdb]" +``` + +## Configuration + +| Environment variable | Description | +|---|---| +| `PYDANTIC_AI_MODEL` | LLM model string (e.g., `claude-3-5-sonnet-latest`, `openai:gpt-4o`) | +| `ANTHROPIC_API_KEY` | Required when using Claude | +| `OPENAI_API_KEY` | Required when using OpenAI | +| `WREN_ENGINE_ENDPOINT` | Optional ibis-server URL for dry-plan validation (e.g., `http://localhost:8000`) | + +Copy `.env.example` to `.env` and fill in your credentials. diff --git a/wren-agent/pyproject.toml b/wren-agent/pyproject.toml new file mode 100644 index 000000000..045247dd9 --- /dev/null +++ b/wren-agent/pyproject.toml @@ -0,0 +1,44 @@ +[project] +name = "wren-agent" +version = "0.1.0" +description = "AI agent for agentic MDL generation for Wren Engine" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "pydantic-ai>=0.0.40", + "pydantic>=2.10", + "sqlalchemy>=2.0", + "httpx>=0.28", + "jsonschema>=4.0", + "python-dotenv>=1.0", +] + +[project.optional-dependencies] +postgres = ["psycopg2-binary>=2.9"] +mysql = ["pymysql>=1.1"] +duckdb = ["duckdb>=1.0", "duckdb-engine>=0.13"] +all = ["wren-agent[postgres,mysql,duckdb]"] +dev = [ + "pytest>=8.0", + "pytest-asyncio>=0.24", + "pytest-mock>=3.14", + "ruff>=0.11", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/wren_agent"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] + +[tool.ruff] +line-length = 88 +target-version = "py311" + +[tool.ruff.lint] +select = ["E", "F", "I", "UP"] diff --git a/wren-agent/src/wren_agent/__init__.py b/wren-agent/src/wren_agent/__init__.py new file mode 100644 index 000000000..38e3fa3a9 --- /dev/null +++ b/wren-agent/src/wren_agent/__init__.py @@ -0,0 +1,15 @@ +"""Wren Agent — agentic MDL generation for Wren Engine.""" + +from wren_agent.agent import MDLAgent, MDLAgentResponse +from wren_agent.memory import MemoryStore +from wren_agent.models import AgentQuestion, MDLManifest +from wren_agent.skills import SkillStore + +__all__ = [ + "MDLAgent", + "MDLAgentResponse", + "AgentQuestion", + "MDLManifest", + "SkillStore", + "MemoryStore", +] diff --git a/wren-agent/src/wren_agent/agent.py b/wren-agent/src/wren_agent/agent.py new file mode 100644 index 000000000..86c1ce37b --- /dev/null +++ b/wren-agent/src/wren_agent/agent.py @@ -0,0 +1,294 @@ +"""MDL generation agent assembled with PydanticAI.""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field +from typing import Union + +from pydantic_ai import Agent, RunContext +from pydantic_ai.messages import ModelMessage + +from wren_agent.deps import AgentDeps +from wren_agent.memory import MemoryStore +from wren_agent.models import AgentQuestion, MDLManifest +from wren_agent.prompts import SYSTEM_PROMPT +from wren_agent.skills import SkillStore +from wren_agent.tools.db import ( + connect_to_database, + get_column_info, + get_column_stats, + get_sample_data, + list_tables, +) +from wren_agent.tools.memory import ( + tool_forget, + tool_list_memory_keys, + tool_recall, + tool_remember, + tool_search_memory, +) +from wren_agent.tools.validate import validate_mdl + +# --------------------------------------------------------------------------- +# Internal PydanticAI agent +# --------------------------------------------------------------------------- + +# model=None: supplied at run time via agent.run(model=...) to avoid requiring +# LLM credentials at import time. +_agent: Agent[AgentDeps, Union[AgentQuestion, MDLManifest]] = Agent( + model=None, + output_type=Union[AgentQuestion, MDLManifest], # type: ignore[arg-type] + deps_type=AgentDeps, + system_prompt=SYSTEM_PROMPT, +) + + +# Register tools +@_agent.tool +async def tool_connect_to_database( + ctx: RunContext[AgentDeps], connection_string: str +) -> str: + """Establish a database connection. + + Call this with the user's connection string before exploring the schema. + Example: postgresql://user:password@host:5432/dbname + """ + return await connect_to_database(ctx, connection_string) + + +@_agent.tool +async def tool_list_tables(ctx: RunContext[AgentDeps]) -> list[str] | str: + """List all user tables in the connected database.""" + return await list_tables(ctx) + + +@_agent.tool +async def tool_get_column_info( + ctx: RunContext[AgentDeps], table: str +) -> list[dict] | str: + """Get column metadata for a table: name, type, nullable, primary key, foreign keys.""" + return await get_column_info(ctx, table) + + +@_agent.tool +async def tool_get_column_stats( + ctx: RunContext[AgentDeps], table: str, column: str +) -> dict | str: + """Get basic statistics for a column: distinct count, null count, min, max.""" + return await get_column_stats(ctx, table, column) + + +@_agent.tool +async def tool_get_sample_data( + ctx: RunContext[AgentDeps], table: str, limit: int = 5 +) -> list[dict] | str: + """Fetch sample rows from a table to understand data semantics.""" + return await get_sample_data(ctx, table, limit) + + +@_agent.tool +async def tool_validate_mdl(ctx: RunContext[AgentDeps], mdl: dict) -> str: + """Validate an MDL manifest dict against the official JSON Schema and optionally + run a dry-plan against ibis-server. + + Pass the MDL as a plain dict (using 'schema' key, not 'schema_name'). + """ + return await validate_mdl(ctx, mdl) + + +# ------------------------------------------------------------------ +# Memory tools +# ------------------------------------------------------------------ + +@_agent.tool +async def _tool_remember( + ctx: RunContext[AgentDeps], key: str, value: str, tags: list[str] | None = None +) -> str: + """Store a piece of information in persistent memory for future sessions. + + Use this for facts that will be useful later, such as: + - Database schema summaries + - User naming preferences + - Previously validated MDL fragments + + Args: + key: Short identifier, e.g. "tpch_schema" or "user_prefers_snake_case". + value: The text to remember. + tags: Optional tags for grouping, e.g. ["schema", "tpch"]. + """ + return await tool_remember(ctx, key, value, tags) + + +@_agent.tool +async def _tool_recall(ctx: RunContext[AgentDeps], key: str) -> str: + """Retrieve a previously stored memory entry by key. + + Returns the stored value, or a 'not found' message. + """ + return await tool_recall(ctx, key) + + +@_agent.tool +async def _tool_search_memory(ctx: RunContext[AgentDeps], query: str) -> str: + """Search memory entries whose key or value contains the query string. + + Call this at the start of a conversation to check if relevant prior + knowledge about this database or user preferences already exists. + """ + return await tool_search_memory(ctx, query) + + +@_agent.tool +async def _tool_list_memory_keys(ctx: RunContext[AgentDeps]) -> str: + """List all keys currently stored in memory.""" + return await tool_list_memory_keys(ctx) + + +@_agent.tool +async def _tool_forget(ctx: RunContext[AgentDeps], key: str) -> str: + """Delete a memory entry by key when a stored fact is outdated or incorrect.""" + return await tool_forget(ctx, key) + + +# --------------------------------------------------------------------------- +# Public MDLAgent wrapper +# --------------------------------------------------------------------------- + + +@dataclass +class MDLAgentResponse: + """Response returned from MDLAgent.run().""" + + result: AgentQuestion | MDLManifest + """The agent output: either a follow-up question or the completed MDL manifest.""" + + message_history: list[ModelMessage] = field(default_factory=list) + """Full conversation history — pass back to the next run() call to continue.""" + + +class MDLAgent: + """High-level wrapper around the PydanticAI MDL generation agent. + + Supports optional **skills** (domain knowledge injected per run) and + **memory** (persistent key-value store readable/writable by the agent). + + Basic usage (multi-turn):: + + agent = MDLAgent() + history = [] + + resp = await agent.run("I want to create MDL for my database") + # resp.result → AgentQuestion(question="Please provide your connection string…") + history = resp.message_history + + resp = await agent.run("postgresql://user:pass@localhost/mydb", message_history=history) + # resp.result → MDLManifest(...) + + With skills and memory:: + + from wren_agent.skills import SkillStore + from wren_agent.memory import MemoryStore + + skills = SkillStore() + skills.load_dir("my_skills/") # loads *.md / *.txt + skills.add("ecommerce", + "Order tables usually reference customers via customer_id FK.") + + memory = MemoryStore(persist_path="~/.wren_agent/memory.json") + + agent = MDLAgent(skill_store=skills, memory=memory) + """ + + def __init__( + self, + model: str | None = None, + ibis_server_url: str | None = None, + skill_store: SkillStore | None = None, + memory: MemoryStore | None = None, + max_skills: int = 5, + ) -> None: + """ + Args: + model: A pydantic-ai model string, e.g. ``"anthropic:claude-3-5-sonnet-latest"``, + ``"openai:gpt-4o"``. Defaults to ``PYDANTIC_AI_MODEL`` env var. + ibis_server_url: Optional Wren ibis-server URL for dry-plan validation. + Defaults to ``WREN_ENGINE_ENDPOINT`` env var. + skill_store: Optional :class:`~wren_agent.skills.SkillStore` with + domain-specific knowledge selected and injected per run. + On each :meth:`run`, the store's ``select(query, top_k)`` + is called to pick the most relevant skills for that message. + memory: Optional :class:`~wren_agent.memory.MemoryStore` for persistent + cross-session memory. The agent can call memory tools to read + and write entries during the conversation. + max_skills: Maximum number of skills to inject per run (default 5). + Skills are ranked by TF-IDF cosine similarity to the current + message before injection. + """ + self._model = ( + model + or os.environ.get("PYDANTIC_AI_MODEL") + or "anthropic:claude-3-5-sonnet-latest" + ) + self._ibis_server_url = ibis_server_url or os.environ.get( + "WREN_ENGINE_ENDPOINT" + ) + self._skill_store = skill_store + self._memory = memory + self._max_skills = max_skills + # Persist SA engine across turns so the DB connection survives multi-turn + self._engine = None + + async def run( + self, + message: str, + *, + message_history: list[ModelMessage] | None = None, + ) -> MDLAgentResponse: + """Send a message to the agent and return the response. + + Args: + message: The user's message. + message_history: Previous messages from prior run() calls. Pass the + ``message_history`` from the last MDLAgentResponse to + continue an ongoing conversation. + + Returns: + MDLAgentResponse with ``result`` (AgentQuestion or MDLManifest) + and ``message_history`` for the next turn. + """ + deps = AgentDeps( + engine=self._engine, + ibis_server_url=self._ibis_server_url, + memory=self._memory, + ) + + # Skills are injected as per-run instructions so they don't permanently + # bloat the base system prompt for every agent instance. + # select() applies tag filter + TF-IDF semantic ranking against the user + # message so only the most relevant skills are injected. + instructions: str | None = ( + self._skill_store.select( + query=message, + top_k=self._max_skills, + ).as_instructions() + if self._skill_store + else None + ) + + result = await _agent.run( + message, + model=self._model, + deps=deps, + message_history=message_history or [], + instructions=instructions, + ) + + # Persist the engine for subsequent turns + if deps.engine is not None: + self._engine = deps.engine + + return MDLAgentResponse( + result=result.output, + message_history=list(result.all_messages()), + ) diff --git a/wren-agent/src/wren_agent/deps.py b/wren-agent/src/wren_agent/deps.py new file mode 100644 index 000000000..ae1551ee4 --- /dev/null +++ b/wren-agent/src/wren_agent/deps.py @@ -0,0 +1,26 @@ +"""Agent dependency container, passed to all tools via RunContext.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING + +import sqlalchemy as sa + +if TYPE_CHECKING: + from wren_agent.memory import MemoryStore + + +@dataclass +class AgentDeps: + """Holds stateful resources shared across all tool calls in one agent run.""" + + # Set by connect_to_database tool; None until connection is established + engine: sa.Engine | None = None + + # Optional ibis-server URL for dry-plan validation + # Reads from env WREN_ENGINE_ENDPOINT if not supplied explicitly + ibis_server_url: str | None = field(default=None) + + # Optional persistent memory store; None means memory tools are disabled + memory: MemoryStore | None = None diff --git a/wren-agent/src/wren_agent/memory.py b/wren-agent/src/wren_agent/memory.py new file mode 100644 index 000000000..1ae04e28f --- /dev/null +++ b/wren-agent/src/wren_agent/memory.py @@ -0,0 +1,141 @@ +"""Memory store — persistent key-value memory readable and writable by the agent. + +The agent can call memory tools to: +- ``remember(key, value)`` — store a fact for future sessions +- ``recall(key)`` — retrieve a stored fact +- ``search_memory(query)`` — find entries whose key or value contains a substring +- ``list_memory_keys()`` — list all stored keys +- ``forget(key)`` — delete an entry + +By default the store is in-memory only (lost when the process exits). +Pass ``persist_path`` to persist to a JSON file between sessions. + +Example:: + + mem = MemoryStore(persist_path="~/.wren_agent/memory.json") + agent = MDLAgent(memory=mem) +""" + +from __future__ import annotations + +import json +from dataclasses import asdict, dataclass, field +from datetime import datetime, timezone +from pathlib import Path + + +@dataclass +class MemoryEntry: + key: str + value: str + created_at: str = field( + default_factory=lambda: datetime.now(timezone.utc).isoformat() + ) + tags: list[str] = field(default_factory=list) + + +class MemoryStore: + """Simple key-value memory that the agent can read and write. + + Args: + persist_path: If given, the store is loaded from this JSON file on init + and saved back after every write. ``~`` is expanded. + """ + + def __init__(self, persist_path: str | Path | None = None) -> None: + self._entries: dict[str, MemoryEntry] = {} + self._persist_path: Path | None = ( + Path(persist_path).expanduser() if persist_path else None + ) + if self._persist_path and self._persist_path.exists(): + self._load() + + # ------------------------------------------------------------------ + # Write + # ------------------------------------------------------------------ + + def remember( + self, + key: str, + value: str, + *, + tags: list[str] | None = None, + overwrite: bool = True, + ) -> None: + """Store a memory entry. + + Args: + key: Identifier (e.g., ``"tpch_db_schema"``, ``"user_prefers_snake_case"``). + value: The content to remember. + tags: Optional list of tags for filtering. + overwrite: If False and key already exists, raises ValueError. + """ + if not overwrite and key in self._entries: + raise ValueError(f"Memory key '{key}' already exists. Use overwrite=True to update.") + self._entries[key] = MemoryEntry( + key=key, + value=value, + created_at=datetime.now(timezone.utc).isoformat(), + tags=tags or [], + ) + self._save() + + def forget(self, key: str) -> bool: + """Delete a memory entry. Returns True if it existed.""" + existed = key in self._entries + if existed: + del self._entries[key] + self._save() + return existed + + # ------------------------------------------------------------------ + # Read + # ------------------------------------------------------------------ + + def recall(self, key: str) -> str | None: + """Retrieve the value for a key, or None if not found.""" + entry = self._entries.get(key) + return entry.value if entry else None + + def search(self, query: str) -> list[MemoryEntry]: + """Return entries whose key or value contains ``query`` (case-insensitive).""" + q = query.lower() + return [ + e for e in self._entries.values() + if q in e.key.lower() or q in e.value.lower() + ] + + def list_keys(self) -> list[str]: + return list(self._entries.keys()) + + def get_entry(self, key: str) -> MemoryEntry | None: + return self._entries.get(key) + + def all_entries(self) -> list[MemoryEntry]: + return list(self._entries.values()) + + def __len__(self) -> int: + return len(self._entries) + + # ------------------------------------------------------------------ + # Persistence + # ------------------------------------------------------------------ + + def _save(self) -> None: + if self._persist_path is None: + return + self._persist_path.parent.mkdir(parents=True, exist_ok=True) + data = [asdict(e) for e in self._entries.values()] + self._persist_path.write_text( + json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8" + ) + + def _load(self) -> None: + assert self._persist_path is not None + try: + data = json.loads(self._persist_path.read_text(encoding="utf-8")) + for item in data: + entry = MemoryEntry(**item) + self._entries[entry.key] = entry + except (json.JSONDecodeError, TypeError, KeyError): + pass # Corrupted file — start fresh diff --git a/wren-agent/src/wren_agent/models.py b/wren-agent/src/wren_agent/models.py new file mode 100644 index 000000000..8c5763246 --- /dev/null +++ b/wren-agent/src/wren_agent/models.py @@ -0,0 +1,202 @@ +"""Pydantic models for Wren MDL manifest. + +Corresponds to the official schema: +https://raw.githubusercontent.com/Canner/WrenAI/main/wren-mdl/mdl.schema.json +""" + +from __future__ import annotations + +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field, model_validator + + +class SessionProperty(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + required: bool + defaultExpr: str | None = None + + +class ColumnLevelAccessControlThreshold(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + value: str + dataType: Literal["NUMERIC", "STRING"] + + +class ColumnLevelAccessControl(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + operator: Literal[ + "EQUALS", + "NOT_EQUALS", + "GREATER_THAN", + "LESS_THAN", + "GREATER_THAN_OR_EQUALS", + "LESS_THAN_OR_EQUALS", + ] + requiredProperties: list[SessionProperty] = Field(min_length=1, max_length=1) + threshold: ColumnLevelAccessControlThreshold + + +class MDLColumn(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + type: str + relationship: str | None = None + isCalculated: bool | None = None + notNull: bool | None = None + expression: str | None = None + isHidden: bool | None = None + columnLevelAccessControl: ColumnLevelAccessControl | None = None + properties: dict[str, Any] | None = None + + +class TableReference(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + catalog: str | None = None + # Named schema_name internally; alias "schema" matches MDL JSON key. + schema_name: str | None = Field(default=None, alias="schema") + table: str + + +class RowLevelAccessControl(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + requiredProperties: list[SessionProperty] + condition: str + + +class MDLModel(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + tableReference: TableReference | None = None + refSql: str | None = None + baseObject: str | None = None + columns: list[MDLColumn] | None = None + primaryKey: str | None = None + cached: bool | None = None + refreshTime: str | None = None + rowLevelAccessControls: list[RowLevelAccessControl] | None = None + properties: dict[str, Any] | None = None + + @model_validator(mode="after") + def validate_table_source(self) -> MDLModel: + sources = [ + x + for x in [self.tableReference, self.refSql, self.baseObject] + if x is not None + ] + if len(sources) != 1: + raise ValueError( + "Exactly one of tableReference, refSql, or baseObject must be provided" + ) + return self + + +class MDLRelationship(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + models: list[str] = Field(min_length=2, max_length=2) + joinType: Literal["ONE_TO_ONE", "ONE_TO_MANY", "MANY_TO_ONE", "MANY_TO_MANY"] + condition: str + properties: dict[str, Any] | None = None + + +class TimeGrain(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + refColumn: str + dateParts: list[ + Literal["YEAR", "QUARTER", "MONTH", "WEEK", "DAY", "HOUR", "MINUTE", "SECOND"] + ] + + +class MDLMetric(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + baseObject: str + dimension: list[MDLColumn] + measure: list[MDLColumn] = Field(min_length=1) + timeGrain: list[TimeGrain] | None = None + cached: bool | None = None + refreshTime: str | None = None + properties: dict[str, Any] | None = None + + +class MDLView(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + statement: str + properties: dict[str, Any] | None = None + + +class EnumMember(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + value: str | None = None + properties: dict[str, Any] | None = None + + +class EnumDefinition(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + values: list[EnumMember] + properties: dict[str, Any] | None = None + + +DataSourceType = Literal[ + "BIGQUERY", + "CLICKHOUSE", + "CANNER", + "TRINO", + "MSSQL", + "MYSQL", + "POSTGRES", + "SNOWFLAKE", + "DUCKDB", + "LOCAL_FILE", + "S3_FILE", + "GCS_FILE", + "MINIO_FILE", + "ORACLE", + "ATHENA", + "REDSHIFT", +] + + +class MDLManifest(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + catalog: str + schema_name: str = Field(alias="schema") + dataSource: DataSourceType | None = None + sampleDataFolder: str | None = None + models: list[MDLModel] | None = None + relationships: list[MDLRelationship] | None = None + metrics: list[MDLMetric] | None = None + views: list[MDLView] | None = None + enumDefinitions: list[EnumDefinition] | None = None + + def to_mdl_dict(self) -> dict[str, Any]: + """Serialize to MDL-compatible dict (uses 'schema' key, not 'schema_name').""" + return self.model_dump(by_alias=True, exclude_none=True) + + +class AgentQuestion(BaseModel): + """Returned when the agent needs more information from the user.""" + + question: str diff --git a/wren-agent/src/wren_agent/prompts.py b/wren-agent/src/wren_agent/prompts.py new file mode 100644 index 000000000..748835e1a --- /dev/null +++ b/wren-agent/src/wren_agent/prompts.py @@ -0,0 +1,60 @@ +"""System prompt for the MDL generation agent.""" + +SYSTEM_PROMPT = """ +You are a Wren MDL expert. Your job is to help users generate a complete and valid +Wren MDL (Modeling Definition Language) manifest for their database. + +MDL is a JSON structure that defines semantic models on top of a database. It includes: +- models: each model maps to a database table with typed columns +- relationships: joins between models (ONE_TO_ONE, ONE_TO_MANY, MANY_TO_ONE, MANY_TO_MANY) +- views: named SQL queries + +## Your workflow + +1. **Ask for a database connection string** if none has been provided yet. + Return an AgentQuestion asking the user for their connection string. + Example formats: + - PostgreSQL: postgresql://user:password@host:5432/database + - MySQL: mysql+pymysql://user:password@host:3306/database + - DuckDB: duckdb:///path/to/file.db + +2. **Connect to the database** using `connect_to_database(connection_string)`. + +3. **Explore the schema**: + - Call `list_tables()` to discover all tables. + - For each table, call `get_column_info(table)` to get columns, types, PKs, and FKs. + - For ambiguous columns, call `get_column_stats(table, column)` or + `get_sample_data(table)` to understand semantics. + +4. **Build the MDL manifest**: + - Set `catalog` and `schema` from the database connection (e.g., "wren" and "public"). + - Set `dataSource` to the uppercase datasource type (e.g., "POSTGRES", "MYSQL", "DUCKDB"). + - For each table, create an MDLModel: + - `name`: PascalCase version of the table name (e.g., "order_items" → "OrderItems") + - `tableReference`: use the actual catalog/schema/table from the DB + - `columns`: map each column with its SQL type + - `primaryKey`: set if the table has a single-column PK + - For each foreign key constraint, create an MDLRelationship: + - The FK side is MANY_TO_ONE (many rows reference one parent) + - The referenced side gets a ONE_TO_MANY back-reference if relevant + - `condition`: use model column names (PascalCase model, original column name) + e.g., `"OrderItems.order_id = Orders.id"` + +5. **Validate the manifest** using `validate_mdl(mdl_dict)` before returning. + Fix any reported errors and re-validate. + +6. **Return the final MDLManifest** as the structured result. + +## MDL naming conventions +- Model names: PascalCase (Orders, Customers, LineItems) +- Column names: preserve original DB column names (do NOT convert to camelCase) +- Relationship names: descriptive, e.g., "OrdersToCustomers" + +## Important rules +- Every model MUST have exactly one of: tableReference, refSql, or baseObject. +- Relationship `models` array must contain exactly 2 model names. +- Calculated columns (`isCalculated: true`) require an `expression`. +- Do not invent columns or relationships not present in the actual DB schema. +- If a table has no FK constraints, do not create relationships for it unless + you can infer them from column naming conventions (e.g., `customer_id` → Customers). +""" diff --git a/wren-agent/src/wren_agent/skills.py b/wren-agent/src/wren_agent/skills.py new file mode 100644 index 000000000..918111bcc --- /dev/null +++ b/wren-agent/src/wren_agent/skills.py @@ -0,0 +1,307 @@ +"""Skill store — load domain-specific knowledge snippets and inject them into the agent. + +Skills are static Markdown/text documents that describe conventions, patterns, +or domain knowledge. They are injected into the agent as ``instructions`` on +each run, with two selection mechanisms: + +- **Tag filter** — ``by_tags(["postgres", "bigquery"])`` + Returns a new SkillStore with only skills whose tags overlap with the given set. + +- **Semantic search** — ``search("nested JSON columns", top_k=3)`` + Ranks all skills by TF-IDF cosine similarity to the query and returns the top-k. + +- **Combined** — ``select(query="...", tags=["postgres"], top_k=3)`` + Applies tag filter first, then semantic ranking. + +``MDLAgent`` calls ``select(query=)`` automatically on every run, +so only relevant skills are injected rather than the entire store. + +Example:: + + store = SkillStore() + store.load_dir("skills/") + store.add("ecommerce", + "Order tables usually have customer_id FK to customers.", + tags=["ecommerce", "postgres"]) + store.add("bigquery_nested", + "Use STRUCT for nested columns in BigQuery.", + tags=["bigquery"]) + + agent = MDLAgent(skill_store=store, max_skills=3) + # On each run, agent auto-calls store.select(query=message, top_k=3) +""" + +from __future__ import annotations + +import math +import re +from collections import Counter +from dataclasses import dataclass, field +from pathlib import Path + + +@dataclass +class Skill: + """A single named knowledge snippet.""" + + name: str + content: str + tags: list[str] = field(default_factory=list) + + +# --------------------------------------------------------------------------- +# TF-IDF helpers (pure Python, no extra dependencies) +# --------------------------------------------------------------------------- + + +def _tokenize(text: str) -> list[str]: + return re.findall(r"[a-z0-9]+", text.lower()) + + +def _tfidf_cosine(query: str, skills: list[Skill]) -> list[float]: + """Return a cosine similarity score for each skill against the query.""" + if not skills: + return [] + + docs = [_tokenize(s.name + " " + s.content) for s in skills] + query_tokens = _tokenize(query) + + if not query_tokens: + return [0.0] * len(skills) + + # Document frequency across the skill corpus + N = len(docs) + df: Counter[str] = Counter() + for doc in docs: + for term in set(doc): + df[term] += 1 + + all_query_terms = set(query_tokens) + idf = { + t: math.log((N + 1) / (df.get(t, 0) + 1)) + for t in all_query_terms + } + + # Query TF-IDF vector + query_tf = Counter(query_tokens) + query_vec = {t: query_tf[t] * idf[t] for t in query_tokens} + query_norm = math.sqrt(sum(v * v for v in query_vec.values())) + + scores: list[float] = [] + for doc in docs: + doc_tf = Counter(doc) + shared = all_query_terms & set(doc) + if not shared or query_norm == 0: + scores.append(0.0) + continue + dot = sum(query_vec[t] * doc_tf[t] * idf[t] for t in shared) + doc_vec_vals = [ + doc_tf[t] * idf.get(t, math.log((N + 1) / 1)) + for t in set(doc) + ] + doc_norm = math.sqrt(sum(v * v for v in doc_vec_vals)) + scores.append(dot / (query_norm * doc_norm) if doc_norm > 0 else 0.0) + + return scores + + +# --------------------------------------------------------------------------- +# SkillStore +# --------------------------------------------------------------------------- + + +class SkillStore: + """Collection of skills with tag filtering and semantic search.""" + + def __init__(self, _skills: dict[str, Skill] | None = None) -> None: + self._skills: dict[str, Skill] = _skills or {} + + # ------------------------------------------------------------------ + # Loading / adding + # ------------------------------------------------------------------ + + def add( + self, + name: str, + content: str, + tags: list[str] | None = None, + ) -> None: + """Add a skill from an in-memory string. + + Args: + name: Unique identifier for this skill. + content: Markdown or plain-text knowledge content. + tags: Optional list of tags for ``by_tags()`` filtering, + e.g. ``["postgres", "schema"]``. + """ + self._skills[name] = Skill( + name=name, content=content.strip(), tags=tags or [] + ) + + def load_file( + self, + path: str | Path, + tags: list[str] | None = None, + ) -> None: + """Load a single .md or .txt file as a skill. + + The skill name is the file stem (e.g., ``bigquery_mdl.md`` → ``bigquery_mdl``). + + Args: + path: Path to the file. + tags: Tags to attach to this skill. + """ + p = Path(path) + if not p.exists(): + raise FileNotFoundError(f"Skill file not found: {p}") + self.add(p.stem, p.read_text(encoding="utf-8"), tags=tags) + + def load_dir( + self, + path: str | Path, + tags: list[str] | None = None, + ) -> None: + """Load all .md and .txt files in a directory as skills. + + Args: + path: Directory to scan. + tags: Tags applied to every loaded skill. + """ + p = Path(path) + if not p.is_dir(): + raise NotADirectoryError(f"Not a directory: {p}") + for f in sorted(p.glob("*.md")) + sorted(p.glob("*.txt")): + self.load_file(f, tags=tags) + + # ------------------------------------------------------------------ + # Basic querying + # ------------------------------------------------------------------ + + def get(self, name: str) -> Skill | None: + return self._skills.get(name) + + def list_names(self) -> list[str]: + return list(self._skills.keys()) + + def __len__(self) -> int: + return len(self._skills) + + # ------------------------------------------------------------------ + # Selection + # ------------------------------------------------------------------ + + def by_tags( + self, + tags: list[str], + match: str = "any", + ) -> SkillStore: + """Return a new SkillStore containing only skills whose tags overlap. + + Args: + tags: Tags to filter by. + match: ``"any"`` — keep skills with at least one matching tag + (union). ``"all"`` — keep only skills that have every + given tag (intersection). + + Returns: + A new :class:`SkillStore` (may be empty if no skills match). + """ + tag_set = {t.lower() for t in tags} + if match == "all": + filtered = { + name: s + for name, s in self._skills.items() + if tag_set.issubset({t.lower() for t in s.tags}) + } + else: # "any" + filtered = { + name: s + for name, s in self._skills.items() + if tag_set & {t.lower() for t in s.tags} + } + return SkillStore(filtered) + + def search(self, query: str, top_k: int = 5) -> SkillStore: + """Rank skills by TF-IDF cosine similarity to *query* and return top-k. + + Uses pure-Python TF-IDF (no dependencies beyond the stdlib). + + Args: + query: Text to compare against (typically the user's message). + top_k: Maximum number of skills to return. Skills with a score + of 0 are excluded even if fewer than *top_k* remain. + + Returns: + A new :class:`SkillStore` ordered by relevance (best first). + """ + if not self._skills: + return SkillStore() + + skills = list(self._skills.values()) + scores = _tfidf_cosine(query, skills) + + ranked = sorted(zip(scores, skills), key=lambda x: x[0], reverse=True) + selected = { + s.name: s + for score, s in ranked[:top_k] + if score > 0 + } + return SkillStore(selected) + + def select( + self, + query: str | None = None, + tags: list[str] | None = None, + top_k: int | None = None, + ) -> SkillStore: + """Combined selection: tag filter → semantic ranking. + + This is what ``MDLAgent`` calls automatically on every ``run()``. + + Steps: + + 1. If *tags* given, apply ``by_tags(tags)`` to narrow the pool. + 2. If *query* given, apply ``search(query, top_k)`` on the remaining pool. + 3. If neither, return all skills (optionally limited by *top_k*). + + Args: + query: Current user message — used for semantic ranking. + tags: Tags to pre-filter by before semantic ranking. + top_k: Maximum number of skills to return. + + Returns: + A new :class:`SkillStore` with the selected subset. + """ + result: SkillStore = self + + if tags: + result = result.by_tags(tags) + + if query: + k = top_k if top_k is not None else len(result) + result = result.search(query, top_k=k) + elif top_k is not None: + # No query — take first top_k in insertion order + items = dict(list(result._skills.items())[:top_k]) + result = SkillStore(items) + + return result + + # ------------------------------------------------------------------ + # Injection + # ------------------------------------------------------------------ + + def as_instructions(self) -> str | None: + """Concatenate selected skills into a single instructions string. + + Returns ``None`` if the store is empty (no instructions injected). + """ + if not self._skills: + return None + parts = ["## Relevant Skills\n"] + for skill in self._skills.values(): + tag_line = ( + f"*Tags: {', '.join(skill.tags)}*\n\n" if skill.tags else "" + ) + parts.append(f"### {skill.name}\n\n{tag_line}{skill.content}\n") + return "\n".join(parts) diff --git a/wren-agent/src/wren_agent/tools/__init__.py b/wren-agent/src/wren_agent/tools/__init__.py new file mode 100644 index 000000000..5fe1c3aa9 --- /dev/null +++ b/wren-agent/src/wren_agent/tools/__init__.py @@ -0,0 +1 @@ +"""Wren Agent tools package.""" diff --git a/wren-agent/src/wren_agent/tools/db.py b/wren-agent/src/wren_agent/tools/db.py new file mode 100644 index 000000000..ed95e5d80 --- /dev/null +++ b/wren-agent/src/wren_agent/tools/db.py @@ -0,0 +1,166 @@ +"""Database exploration tools for the MDL agent.""" + +from __future__ import annotations + +from typing import Any + +import sqlalchemy as sa +from pydantic_ai import RunContext +from sqlalchemy import inspect, text + +from wren_agent.deps import AgentDeps + + +def _require_engine(ctx: RunContext[AgentDeps]) -> sa.Engine | str: + """Return the engine or an error string if not connected.""" + if ctx.deps.engine is None: + return "No database connection. Please call connect_to_database first." + return ctx.deps.engine + + +async def connect_to_database( + ctx: RunContext[AgentDeps], connection_string: str +) -> str: + """Establish a database connection using a SQLAlchemy connection string. + + Supported schemes: postgresql://, mysql+pymysql://, duckdb:///, and others. + Example: postgresql://user:pass@localhost:5432/mydb + + Returns a success message listing available tables, or an error string. + """ + try: + engine = sa.create_engine(connection_string) + # Eagerly verify connectivity + with engine.connect() as conn: + conn.execute(text("SELECT 1")) + ctx.deps.engine = engine + + insp = inspect(engine) + tables = insp.get_table_names() + schema_tables = insp.get_table_names(schema=None) + all_tables = sorted(set(tables + schema_tables)) + return ( + f"Connected successfully. Found {len(all_tables)} table(s): " + + ", ".join(all_tables[:20]) + + ("..." if len(all_tables) > 20 else "") + ) + except Exception as e: + return f"Connection failed: {e}" + + +async def list_tables(ctx: RunContext[AgentDeps]) -> list[str] | str: + """List all user tables in the connected database.""" + engine = _require_engine(ctx) + if isinstance(engine, str): + return engine + try: + insp = inspect(engine) + return sorted(insp.get_table_names()) + except Exception as e: + return f"Error listing tables: {e}" + + +async def get_column_info( + ctx: RunContext[AgentDeps], table: str +) -> list[dict[str, Any]] | str: + """Get column metadata for a table: name, type, nullable, primary key, foreign keys. + + Returns a list of dicts, one per column. + """ + engine = _require_engine(ctx) + if isinstance(engine, str): + return engine + try: + insp = inspect(engine) + columns = insp.get_columns(table) + pk_constraint = insp.get_pk_constraint(table) + pk_cols = set(pk_constraint.get("constrained_columns", [])) + fk_list = insp.get_foreign_keys(table) + + fk_map: dict[str, dict[str, str]] = {} + for fk in fk_list: + for local_col, ref_col in zip( + fk["constrained_columns"], fk["referred_columns"] + ): + fk_map[local_col] = { + "referred_table": fk["referred_table"], + "referred_column": ref_col, + } + + result = [] + for col in columns: + col_name = col["name"] + info: dict[str, Any] = { + "name": col_name, + "type": str(col["type"]), + "nullable": col.get("nullable", True), + "primary_key": col_name in pk_cols, + } + if col_name in fk_map: + info["foreign_key"] = fk_map[col_name] + result.append(info) + return result + except Exception as e: + return f"Error getting column info for '{table}': {e}" + + +async def get_column_stats( + ctx: RunContext[AgentDeps], table: str, column: str +) -> dict[str, Any] | str: + """Get basic statistics for a column: distinct count, null count, min, max. + + Useful for inferring column semantics and data quality. + """ + engine = _require_engine(ctx) + if isinstance(engine, str): + return engine + try: + # Use quoted identifiers to avoid SQL injection via table/column names + col_id = sa.column(column) + tbl = sa.table(table, col_id) + stmt = sa.select( + sa.func.count(sa.func.distinct(col_id)).label("distinct_count"), + (sa.func.count() - sa.func.count(col_id)).label("null_count"), + sa.func.min(col_id).label("min_val"), + sa.func.max(col_id).label("max_val"), + ).select_from(tbl) + with engine.connect() as conn: + row = conn.execute(stmt).one() + return { + "distinct_count": row.distinct_count, + "null_count": row.null_count, + "min": str(row.min_val) if row.min_val is not None else None, + "max": str(row.max_val) if row.max_val is not None else None, + } + except Exception as e: + return f"Error getting stats for '{table}.{column}': {e}" + + +async def get_sample_data( + ctx: RunContext[AgentDeps], table: str, limit: int = 5 +) -> list[dict[str, Any]] | str: + """Fetch sample rows from a table to help infer column semantics. + + Args: + table: Table name. + limit: Number of rows to return (default 5, max 20). + """ + engine = _require_engine(ctx) + if isinstance(engine, str): + return engine + limit = min(limit, 20) + try: + tbl = sa.table(table) + stmt = sa.select(sa.text("*")).select_from(tbl).limit(limit) + with engine.connect() as conn: + result = conn.execute(stmt) + keys = list(result.keys()) + rows = [dict(zip(keys, row)) for row in result.fetchall()] + # Coerce non-serializable values to strings + return [ + {k: (str(v) if not isinstance(v, (str, int, float, bool, type(None))) else v) + for k, v in row.items()} + for row in rows + ] + except Exception as e: + return f"Error getting sample data for '{table}': {e}" diff --git a/wren-agent/src/wren_agent/tools/memory.py b/wren-agent/src/wren_agent/tools/memory.py new file mode 100644 index 000000000..edd195f66 --- /dev/null +++ b/wren-agent/src/wren_agent/tools/memory.py @@ -0,0 +1,83 @@ +"""Memory tools exposed to the agent.""" + +from __future__ import annotations + +from pydantic_ai import RunContext + +from wren_agent.deps import AgentDeps + + +async def tool_remember( + ctx: RunContext[AgentDeps], key: str, value: str, tags: list[str] | None = None +) -> str: + """Store a piece of information in persistent memory for future sessions. + + Use this for facts that are likely to be useful later, such as: + - Database schema summaries ("key: tpch_schema, value: has orders, lineitem, …") + - User preferences ("key: naming_pref, value: keep original column names") + - Previously generated MDL fragments + + Args: + key: Short identifier, e.g. "tpch_schema" or "user_prefers_snake_case". + value: The text to remember. + tags: Optional tags for filtering, e.g. ["schema", "tpch"]. + """ + if ctx.deps.memory is None: + return "Memory is not enabled for this session." + ctx.deps.memory.remember(key, value, tags=tags) + return f"Remembered '{key}'." + + +async def tool_recall(ctx: RunContext[AgentDeps], key: str) -> str: + """Retrieve a previously stored memory entry by key. + + Returns the stored value, or a 'not found' message. + """ + if ctx.deps.memory is None: + return "Memory is not enabled for this session." + value = ctx.deps.memory.recall(key) + if value is None: + return f"No memory found for key '{key}'." + return value + + +async def tool_search_memory(ctx: RunContext[AgentDeps], query: str) -> str: + """Search memory entries whose key or value contains the query string. + + Useful for finding relevant past observations before exploring the DB. + Returns a formatted summary of matching entries. + """ + if ctx.deps.memory is None: + return "Memory is not enabled for this session." + results = ctx.deps.memory.search(query) + if not results: + return f"No memory entries matching '{query}'." + lines = [f"Found {len(results)} matching memory entry(ies):"] + for e in results: + tag_str = f" [{', '.join(e.tags)}]" if e.tags else "" + lines.append(f" [{e.key}]{tag_str}: {e.value[:200]}") + return "\n".join(lines) + + +async def tool_list_memory_keys(ctx: RunContext[AgentDeps]) -> str: + """List all keys currently stored in memory. + + Call this at the start of a session to check if relevant prior knowledge exists. + """ + if ctx.deps.memory is None: + return "Memory is not enabled for this session." + keys = ctx.deps.memory.list_keys() + if not keys: + return "Memory is empty." + return "Memory keys: " + ", ".join(keys) + + +async def tool_forget(ctx: RunContext[AgentDeps], key: str) -> str: + """Delete a memory entry by key. + + Use when a stored fact is outdated or incorrect. + """ + if ctx.deps.memory is None: + return "Memory is not enabled for this session." + existed = ctx.deps.memory.forget(key) + return f"Forgot '{key}'." if existed else f"Key '{key}' was not in memory." diff --git a/wren-agent/src/wren_agent/tools/validate.py b/wren-agent/src/wren_agent/tools/validate.py new file mode 100644 index 000000000..cc234a7da --- /dev/null +++ b/wren-agent/src/wren_agent/tools/validate.py @@ -0,0 +1,86 @@ +"""MDL validation tool: JSON Schema + optional ibis-server dry-plan.""" + +from __future__ import annotations + +import base64 +import json +from typing import Any + +import httpx +import jsonschema +from pydantic_ai import RunContext + +from wren_agent.deps import AgentDeps + +_MDL_SCHEMA_URL = ( + "https://raw.githubusercontent.com/Canner/WrenAI/main/wren-mdl/mdl.schema.json" +) + +# Module-level cache: fetched once per process +_cached_schema: dict[str, Any] | None = None + + +def _get_mdl_schema() -> dict[str, Any]: + global _cached_schema + if _cached_schema is None: + response = httpx.get(_MDL_SCHEMA_URL, timeout=10.0) + response.raise_for_status() + _cached_schema = response.json() + return _cached_schema + + +def _dict_to_base64(data: dict[str, Any]) -> str: + return base64.b64encode(json.dumps(data).encode()).decode() + + +async def validate_mdl( + ctx: RunContext[AgentDeps], mdl: dict[str, Any] +) -> str: + """Validate an MDL manifest dict. + + Performs two checks: + 1. JSON Schema validation against the official Wren MDL schema. + 2. (Optional) Dry-plan validation via ibis-server if WREN_ENGINE_ENDPOINT is set. + + Returns a string describing the validation result. + """ + # --- Step 1: JSON Schema validation --- + try: + schema = _get_mdl_schema() + except Exception as e: + return f"Warning: Could not fetch MDL schema for validation: {e}. Skipping schema validation." + + errors = list( + jsonschema.Draft202012Validator(schema).iter_errors(mdl) + ) + if errors: + messages = "; ".join( + f"{'.'.join(str(p) for p in e.absolute_path) or ''}: {e.message}" + for e in errors[:5] + ) + return f"JSON Schema validation failed ({len(errors)} error(s)): {messages}" + + # --- Step 2: ibis-server dry-plan (optional) --- + ibis_url = ctx.deps.ibis_server_url + if not ibis_url: + return "JSON Schema validation passed. (Skipping dry-plan: WREN_ENGINE_ENDPOINT not set)" + + try: + manifest_str = _dict_to_base64(mdl) + async with httpx.AsyncClient(timeout=30.0) as client: + resp = await client.post( + f"{ibis_url.rstrip('/')}/v3/connector/dry-plan", + json={"manifest_str": manifest_str, "sql": "SELECT 1"}, + headers={"x-wren-fallback_disable": "true"}, + ) + if resp.status_code == 200: + return "MDL validation passed (JSON Schema + dry-plan)." + else: + return ( + f"JSON Schema validation passed, but dry-plan failed " + f"(HTTP {resp.status_code}): {resp.text[:300]}" + ) + except Exception as e: + return ( + f"JSON Schema validation passed, but dry-plan request failed: {e}" + ) diff --git a/wren-agent/tests/test_agent.py b/wren-agent/tests/test_agent.py new file mode 100644 index 000000000..2bc91b8e6 --- /dev/null +++ b/wren-agent/tests/test_agent.py @@ -0,0 +1,627 @@ +"""Tests for wren_agent.""" + +from __future__ import annotations + +import json +import tempfile +from pathlib import Path +from typing import Any +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from wren_agent.memory import MemoryStore +from wren_agent.skills import Skill, SkillStore + +from wren_agent.models import ( + AgentQuestion, + MDLColumn, + MDLManifest, + MDLModel, + MDLRelationship, + TableReference, +) + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +JAFFLE_MDL: dict[str, Any] = { + "catalog": "wren", + "schema": "public", + "models": [ + { + "name": "orders", + "tableReference": { + "catalog": "jaffle_shop", + "schema": "main", + "table": "orders", + }, + "columns": [ + {"name": "amount", "type": "double"}, + {"name": "id", "type": "integer"}, + {"name": "status", "type": "varchar"}, + ], + } + ], +} + +LOCAL_MDL: dict[str, Any] = { + "catalog": "wrenai", + "schema": "public", + "models": [ + { + "name": "Customer", + "tableReference": {"table": "s3://bucket/customer.parquet"}, + "columns": [ + {"name": "custkey", "type": "integer"}, + { + "name": "orders", + "type": "Orders", + "relationship": "CustomerOrders", + }, + { + "name": "orders_key", + "type": "varchar", + "isCalculated": True, + "expression": "orders.orderkey", + }, + ], + "primaryKey": "custkey", + }, + { + "name": "Orders", + "tableReference": {"table": "s3://bucket/orders.parquet"}, + "columns": [ + {"name": "orderkey", "type": "integer"}, + {"name": "custkey", "type": "integer"}, + ], + "primaryKey": "orderkey", + }, + ], + "relationships": [ + { + "name": "CustomerOrders", + "models": ["Customer", "Orders"], + "joinType": "ONE_TO_MANY", + "condition": "Customer.custkey = Orders.custkey", + } + ], +} + + +# --------------------------------------------------------------------------- +# Model parsing tests +# --------------------------------------------------------------------------- + + +class TestMDLManifestParse: + def test_parse_jaffle(self) -> None: + manifest = MDLManifest.model_validate(JAFFLE_MDL) + assert manifest.catalog == "wren" + assert manifest.schema_name == "public" + assert manifest.models is not None + assert len(manifest.models) == 1 + assert manifest.models[0].name == "orders" + assert len(manifest.models[0].columns) == 3 + + def test_parse_local_mdl_with_relationships(self) -> None: + manifest = MDLManifest.model_validate(LOCAL_MDL) + assert manifest.catalog == "wrenai" + assert manifest.relationships is not None + assert len(manifest.relationships) == 1 + rel = manifest.relationships[0] + assert rel.name == "CustomerOrders" + assert rel.joinType == "ONE_TO_MANY" + assert rel.models == ["Customer", "Orders"] + + def test_parse_calculated_column(self) -> None: + manifest = MDLManifest.model_validate(LOCAL_MDL) + assert manifest.models is not None + customer = next(m for m in manifest.models if m.name == "Customer") + calc_col = next(c for c in customer.columns if c.name == "orders_key") + assert calc_col.isCalculated is True + assert calc_col.expression == "orders.orderkey" + + def test_to_mdl_dict_uses_schema_key(self) -> None: + manifest = MDLManifest.model_validate(JAFFLE_MDL) + d = manifest.to_mdl_dict() + assert "schema" in d + assert "schema_name" not in d + + def test_model_requires_table_source(self) -> None: + with pytest.raises(Exception): + MDLModel(name="NoBacking") # no tableReference, refSql, or baseObject + + def test_model_rejects_multiple_sources(self) -> None: + with pytest.raises(Exception): + MDLModel( + name="Ambiguous", + tableReference=TableReference(table="t"), + refSql="SELECT 1", + ) + + def test_datasource_valid(self) -> None: + manifest = MDLManifest.model_validate({ + **JAFFLE_MDL, + "dataSource": "POSTGRES", + }) + assert manifest.dataSource == "POSTGRES" + + def test_datasource_invalid(self) -> None: + with pytest.raises(Exception): + MDLManifest.model_validate({ + **JAFFLE_MDL, + "dataSource": "NONEXISTENT_DB", + }) + + def test_relationship_requires_two_models(self) -> None: + with pytest.raises(Exception): + MDLRelationship( + name="Bad", + models=["OnlyOne"], + joinType="ONE_TO_MANY", + condition="x = y", + ) + + +# --------------------------------------------------------------------------- +# validate_mdl tool tests +# --------------------------------------------------------------------------- + + +class TestValidateMdl: + @pytest.mark.asyncio + async def test_valid_mdl_passes_schema(self) -> None: + from unittest.mock import patch + + import httpx + + from wren_agent.tools.validate import validate_mdl, _cached_schema + import wren_agent.tools.validate as validate_module + + # Inject a minimally correct schema for testing + minimal_schema = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "type": "object", + "properties": { + "catalog": {"type": "string"}, + "schema": {"type": "string"}, + }, + "required": ["catalog", "schema"], + } + validate_module._cached_schema = minimal_schema + + deps_mock = MagicMock() + deps_mock.ibis_server_url = None + ctx = MagicMock() + ctx.deps = deps_mock + + result = await validate_mdl(ctx, {"catalog": "wren", "schema": "public"}) + assert "passed" in result.lower() + + # restore + validate_module._cached_schema = None + + @pytest.mark.asyncio + async def test_invalid_mdl_fails_schema(self) -> None: + import wren_agent.tools.validate as validate_module + from wren_agent.tools.validate import validate_mdl + + minimal_schema = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "type": "object", + "properties": { + "catalog": {"type": "string"}, + "schema": {"type": "string"}, + }, + "required": ["catalog", "schema"], + } + validate_module._cached_schema = minimal_schema + + deps_mock = MagicMock() + deps_mock.ibis_server_url = None + ctx = MagicMock() + ctx.deps = deps_mock + + result = await validate_mdl(ctx, {"catalog": "wren"}) # missing "schema" + assert "failed" in result.lower() + + validate_module._cached_schema = None + + +# --------------------------------------------------------------------------- +# AgentQuestion model test +# --------------------------------------------------------------------------- + + +class TestAgentQuestion: + def test_question_is_string(self) -> None: + q = AgentQuestion(question="What is your connection string?") + assert q.question == "What is your connection string?" + + +# --------------------------------------------------------------------------- +# SkillStore tests +# --------------------------------------------------------------------------- + + +class TestSkillStore: + def test_add_and_get(self) -> None: + store = SkillStore() + store.add("tip1", "Always use PascalCase for model names.") + assert store.get("tip1") is not None + assert store.get("tip1").content == "Always use PascalCase for model names." + + def test_len(self) -> None: + store = SkillStore() + assert len(store) == 0 + store.add("a", "content a") + store.add("b", "content b") + assert len(store) == 2 + + def test_list_names(self) -> None: + store = SkillStore() + store.add("x", "...") + store.add("y", "...") + assert set(store.list_names()) == {"x", "y"} + + def test_as_instructions_empty(self) -> None: + assert SkillStore().as_instructions() is None + + def test_as_instructions_contains_skill(self) -> None: + store = SkillStore() + store.add("ecommerce", "Order tables reference customers via customer_id.") + instructions = store.as_instructions() + assert instructions is not None + assert "ecommerce" in instructions + assert "Order tables" in instructions + assert "## Relevant Skills" in instructions + + def test_load_file(self, tmp_path: Path) -> None: + skill_file = tmp_path / "bigquery.md" + skill_file.write_text("Use STRUCT for nested columns.", encoding="utf-8") + store = SkillStore() + store.load_file(skill_file) + assert store.get("bigquery") is not None + assert "STRUCT" in store.get("bigquery").content + + def test_load_dir(self, tmp_path: Path) -> None: + (tmp_path / "skill_a.md").write_text("Skill A content", encoding="utf-8") + (tmp_path / "skill_b.txt").write_text("Skill B content", encoding="utf-8") + (tmp_path / "ignored.json").write_text("{}", encoding="utf-8") + store = SkillStore() + store.load_dir(tmp_path) + assert len(store) == 2 + assert store.get("skill_a") is not None + assert store.get("skill_b") is not None + + def test_load_file_not_found(self) -> None: + with pytest.raises(FileNotFoundError): + SkillStore().load_file("/nonexistent/path/skill.md") + + # ---- tags ---- + + def test_add_with_tags(self) -> None: + store = SkillStore() + store.add("pg_tip", "Use bigint for IDs.", tags=["postgres", "schema"]) + skill = store.get("pg_tip") + assert skill is not None + assert "postgres" in skill.tags + + def test_by_tags_any(self) -> None: + store = SkillStore() + store.add("pg", "Postgres tip", tags=["postgres"]) + store.add("bq", "BigQuery tip", tags=["bigquery"]) + store.add("generic", "Generic tip", tags=["schema"]) + result = store.by_tags(["postgres"]) + assert len(result) == 1 + assert result.get("pg") is not None + + def test_by_tags_any_multiple(self) -> None: + store = SkillStore() + store.add("pg", "Postgres tip", tags=["postgres"]) + store.add("bq", "BigQuery tip", tags=["bigquery"]) + store.add("generic", "Generic tip", tags=["schema"]) + result = store.by_tags(["postgres", "bigquery"]) + assert len(result) == 2 + + def test_by_tags_all(self) -> None: + store = SkillStore() + store.add("both", "Has both tags", tags=["postgres", "schema"]) + store.add("one", "Has only postgres", tags=["postgres"]) + result = store.by_tags(["postgres", "schema"], match="all") + assert len(result) == 1 + assert result.get("both") is not None + + def test_by_tags_empty_result(self) -> None: + store = SkillStore() + store.add("pg", "Postgres tip", tags=["postgres"]) + result = store.by_tags(["mysql"]) + assert len(result) == 0 + + def test_by_tags_case_insensitive(self) -> None: + store = SkillStore() + store.add("pg", "Postgres tip", tags=["PostgreSQL"]) + result = store.by_tags(["postgresql"]) + assert len(result) == 1 + + # ---- search ---- + + def test_search_ranks_by_relevance(self) -> None: + store = SkillStore() + store.add("bigquery", "BigQuery tips for nested JSON columns using STRUCT.") + store.add("postgres", "PostgreSQL tips for indexing and constraints.") + store.add("ecommerce", "Ecommerce order tables reference customers.") + result = store.search("BigQuery nested JSON", top_k=1) + assert len(result) == 1 + assert result.get("bigquery") is not None + + def test_search_excludes_zero_scores(self) -> None: + store = SkillStore() + store.add("irrelevant", "Completely unrelated topic about bicycles.") + result = store.search("database schema MDL", top_k=5) + # Might be 0 or 1 depending on overlap; should not crash + assert len(result) <= 1 + + def test_search_empty_store(self) -> None: + result = SkillStore().search("anything") + assert len(result) == 0 + + def test_search_empty_query(self) -> None: + store = SkillStore() + store.add("tip", "something") + result = store.search("", top_k=5) + assert len(result) == 0 # empty query → all scores 0 + + def test_search_respects_top_k(self) -> None: + store = SkillStore() + for i in range(10): + store.add(f"skill_{i}", f"database schema table column model {i}") + result = store.search("database schema", top_k=3) + assert len(result) <= 3 + + # ---- select ---- + + def test_select_tags_only(self) -> None: + store = SkillStore() + store.add("pg", "Postgres tip", tags=["postgres"]) + store.add("bq", "BigQuery tip", tags=["bigquery"]) + result = store.select(tags=["postgres"]) + assert len(result) == 1 + assert result.get("pg") is not None + + def test_select_query_only(self) -> None: + store = SkillStore() + store.add("bigquery", "BigQuery nested STRUCT column model schema.") + store.add("ecommerce", "Order table references customer_id FK.") + result = store.select(query="BigQuery STRUCT nested") + assert result.get("bigquery") is not None + + def test_select_tags_then_query(self) -> None: + store = SkillStore() + store.add("pg_idx", "PostgreSQL index strategies.", tags=["postgres"]) + store.add("pg_fk", "PostgreSQL FK naming conventions.", tags=["postgres"]) + store.add("bq", "BigQuery tips.", tags=["bigquery"]) + result = store.select(query="FK naming", tags=["postgres"], top_k=1) + assert len(result) == 1 + assert result.get("pg_fk") is not None + + def test_select_no_args_returns_all(self) -> None: + store = SkillStore() + store.add("a", "content a") + store.add("b", "content b") + result = store.select() + assert len(result) == 2 + + def test_select_top_k_no_query(self) -> None: + store = SkillStore() + for i in range(5): + store.add(f"skill_{i}", f"skill content {i}") + result = store.select(top_k=3) + assert len(result) == 3 + + def test_as_instructions_shows_tags(self) -> None: + store = SkillStore() + store.add("pg_tip", "Use bigint for IDs.", tags=["postgres", "schema"]) + instructions = store.as_instructions() + assert instructions is not None + assert "postgres" in instructions + assert "schema" in instructions + + +# --------------------------------------------------------------------------- +# MemoryStore tests +# --------------------------------------------------------------------------- + + +class TestMemoryStore: + def test_remember_and_recall(self) -> None: + mem = MemoryStore() + mem.remember("db_schema", "orders table has 10 columns") + assert mem.recall("db_schema") == "orders table has 10 columns" + + def test_recall_missing(self) -> None: + mem = MemoryStore() + assert mem.recall("nonexistent") is None + + def test_overwrite(self) -> None: + mem = MemoryStore() + mem.remember("key", "old value") + mem.remember("key", "new value") + assert mem.recall("key") == "new value" + + def test_overwrite_false_raises(self) -> None: + mem = MemoryStore() + mem.remember("key", "value") + with pytest.raises(ValueError, match="already exists"): + mem.remember("key", "other", overwrite=False) + + def test_forget(self) -> None: + mem = MemoryStore() + mem.remember("temp", "data") + assert mem.forget("temp") is True + assert mem.recall("temp") is None + assert mem.forget("temp") is False # already gone + + def test_search(self) -> None: + mem = MemoryStore() + mem.remember("tpch_tables", "orders, customer, lineitem") + mem.remember("user_pref", "prefer snake_case columns") + results = mem.search("tpch") + assert len(results) == 1 + assert results[0].key == "tpch_tables" + + def test_search_no_results(self) -> None: + mem = MemoryStore() + mem.remember("foo", "bar") + assert mem.search("zzz_no_match") == [] + + def test_list_keys(self) -> None: + mem = MemoryStore() + mem.remember("a", "1") + mem.remember("b", "2") + assert set(mem.list_keys()) == {"a", "b"} + + def test_tags(self) -> None: + mem = MemoryStore() + mem.remember("schema_x", "...", tags=["schema", "postgres"]) + entry = mem.get_entry("schema_x") + assert entry is not None + assert "schema" in entry.tags + + def test_persist_to_file(self, tmp_path: Path) -> None: + path = tmp_path / "memory.json" + mem = MemoryStore(persist_path=path) + mem.remember("persisted_key", "persisted value") + assert path.exists() + + # Load in a new instance + mem2 = MemoryStore(persist_path=path) + assert mem2.recall("persisted_key") == "persisted value" + + def test_persist_corrupted_file(self, tmp_path: Path) -> None: + path = tmp_path / "memory.json" + path.write_text("not valid json", encoding="utf-8") + # Should not raise; start fresh + mem = MemoryStore(persist_path=path) + assert len(mem) == 0 + + +# --------------------------------------------------------------------------- +# Memory tools tests +# --------------------------------------------------------------------------- + + +class TestMemoryTools: + def _make_ctx(self, memory: MemoryStore | None = None) -> MagicMock: + ctx = MagicMock() + from wren_agent.deps import AgentDeps + ctx.deps = AgentDeps(memory=memory) + return ctx + + @pytest.mark.asyncio + async def test_remember_and_recall_tools(self) -> None: + from wren_agent.tools.memory import tool_recall, tool_remember + + mem = MemoryStore() + ctx = self._make_ctx(mem) + + result = await tool_remember(ctx, "k", "v") + assert "Remembered" in result + + result = await tool_recall(ctx, "k") + assert result == "v" + + @pytest.mark.asyncio + async def test_recall_missing_key(self) -> None: + from wren_agent.tools.memory import tool_recall + + mem = MemoryStore() + ctx = self._make_ctx(mem) + result = await tool_recall(ctx, "missing") + assert "no memory found" in result.lower() + + @pytest.mark.asyncio + async def test_tools_disabled_without_memory(self) -> None: + from wren_agent.tools.memory import tool_recall, tool_remember + + ctx = self._make_ctx(memory=None) + assert "not enabled" in await tool_remember(ctx, "k", "v") + assert "not enabled" in await tool_recall(ctx, "k") + + @pytest.mark.asyncio + async def test_search_and_list_tools(self) -> None: + from wren_agent.tools.memory import tool_list_memory_keys, tool_search_memory + + mem = MemoryStore() + mem.remember("tpch_schema", "orders lineitem customer") + ctx = self._make_ctx(mem) + + search_result = await tool_search_memory(ctx, "tpch") + assert "tpch_schema" in search_result + + list_result = await tool_list_memory_keys(ctx) + assert "tpch_schema" in list_result + + @pytest.mark.asyncio + async def test_forget_tool(self) -> None: + from wren_agent.tools.memory import tool_forget, tool_remember + + mem = MemoryStore() + ctx = self._make_ctx(mem) + await tool_remember(ctx, "del_me", "temporary") + result = await tool_forget(ctx, "del_me") + assert "Forgot" in result + assert mem.recall("del_me") is None + + +# --------------------------------------------------------------------------- +# MDLAgent skill selection integration +# --------------------------------------------------------------------------- + + +class TestMDLAgentMaxSkills: + """Test that MDLAgent wires max_skills to SkillStore.select().""" + + def test_default_max_skills(self) -> None: + from wren_agent.agent import MDLAgent + + agent = MDLAgent() + assert agent._max_skills == 5 + + def test_custom_max_skills(self) -> None: + from wren_agent.agent import MDLAgent + + agent = MDLAgent(max_skills=2) + assert agent._max_skills == 2 + + def test_skill_store_stored(self) -> None: + from wren_agent.agent import MDLAgent + + store = SkillStore() + store.add("tip", "some tip content") + agent = MDLAgent(skill_store=store) + assert agent._skill_store is store + + def test_no_skill_store_is_none(self) -> None: + from wren_agent.agent import MDLAgent + + agent = MDLAgent() + assert agent._skill_store is None + + def test_select_called_semantically(self) -> None: + """select() with a query returns relevant subset, not always all skills.""" + store = SkillStore() + store.add("bigquery_struct", "BigQuery STRUCT nested schema model column.", tags=["bigquery"]) + store.add("postgres_fk", "PostgreSQL FK 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refactor(mcp-server): replace nested MDL agent with flat MCP tools MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace the wren-agent-backed mdl_chat/reset/list tools with six flat MCP tools that the calling AI agent orchestrates directly: - mdl_connect_database — SQLAlchemy connection per session_id - mdl_list_tables — table listing - mdl_get_column_info — column metadata (type, nullable, PK, FK) - mdl_get_column_stats — distinct/null count, min/max - mdl_get_sample_data — sample rows (max 20) - mdl_validate_manifest — JSON Schema + optional ibis-server dry-plan Removes wren-agent dependency from mcp-server; adds sqlalchemy and jsonschema as optional mdl extra. DB driver installed separately via just install-mdl-driver . Updated README, .env.example, justfile accordingly. --- mcp-server/.env.example | 31 +- mcp-server/README.md | 139 +- mcp-server/app/mdl_tools.py | 401 ++++-- mcp-server/justfile | 17 +- mcp-server/pyproject.toml | 8 +- mcp-server/uv.lock | 2374 +---------------------------------- 6 files changed, 429 insertions(+), 2541 deletions(-) diff --git a/mcp-server/.env.example b/mcp-server/.env.example index 51efbb020..3c1c0554a 100644 --- a/mcp-server/.env.example +++ b/mcp-server/.env.example @@ -26,33 +26,10 @@ MDL_PATH=/path/to/mdl.json # MCP_PORT=9000 # ----------------------------------------------------------------------------- -# MDL Agent (optional — requires `just install-mdl-agent`) +# MDL Tools (optional — requires `just install-mdl`) # ----------------------------------------------------------------------------- -# LLM model string passed to pydantic-ai. -# Format: ":" -# Examples: -# anthropic:claude-3-5-sonnet-latest -# openai:gpt-4o -# google-gla:gemini-2.0-flash -# PYDANTIC_AI_MODEL=anthropic:claude-3-5-sonnet-latest - -# API keys for your chosen LLM provider (only the relevant one is needed) -# ANTHROPIC_API_KEY=sk-ant-... -# OPENAI_API_KEY=sk-... -# GEMINI_API_KEY=... - -# ibis-server endpoint for MDL dry-plan validation (usually same host as WREN_URL) +# ibis-server endpoint for MDL dry-plan validation. +# When set, mdl_validate_manifest will also run a dry-plan after JSON Schema check. +# Usually the same host as WREN_URL but with http:// scheme. # WREN_ENGINE_ENDPOINT=http://localhost:8000 - -# Directory containing *.md / *.txt skill files loaded at startup. -# Skills provide domain knowledge (naming conventions, patterns, etc.) that the -# agent selects from semantically on each turn. -# MDL_AGENT_SKILLS_DIR=./skills - -# JSON file path for persistent cross-session memory. -# The agent can read/write entries during a conversation and they survive restarts. -# MDL_AGENT_MEMORY_PATH=~/.wren/memory.json - -# Maximum number of skills injected per turn (TF-IDF semantic ranking, default 5) -# MDL_AGENT_MAX_SKILLS=5 diff --git a/mcp-server/README.md b/mcp-server/README.md index c74e0c866..c2a64ba0e 100644 --- a/mcp-server/README.md +++ b/mcp-server/README.md @@ -139,125 +139,82 @@ Optional follow-ups: --- -## MDL Agent (Optional) +## MDL Tools (Optional) -The **MDL Agent** is an optional extension that adds three MCP tools for **agentic MDL generation**. -Instead of writing MDL JSON by hand, you describe your database and the agent explores the schema, -builds the manifest, and hands it off to the existing `deploy` tool — all within the same MCP session. +The **MDL Tools** extension adds six MCP tools that let any AI agent (Claude, +Copilot, Cursor, Cline…) **generate a Wren MDL manifest autonomously**, by +directly exploring a database and building the manifest step-by-step — without +a secondary LLM layer. + +The calling agent acts as the orchestrator: it chooses which tools to call, +inspects the schema, builds the JSON, and validates it before passing it to +the existing `deploy` tool. ### Prerequisites -- An LLM provider API key (Anthropic, OpenAI, etc.) -- The `wren-agent` package (lives inside this monorepo at `../wren-agent`) +- `sqlalchemy>=2.0` and `jsonschema>=4.0` +- The appropriate DB driver for your data source ### Installation -Install `wren-agent` as an editable path dependency into the MCP server's virtual environment: - ```sh -# inside mcp-server/ -just install-mdl-agent -# or, without just: -uv pip install --editable "../wren-agent" +# Core MDL tools +just install-mdl +# or: uv pip install sqlalchemy jsonschema + +# DB driver (pick one) +just install-mdl-driver postgres # psycopg2-binary +just install-mdl-driver mysql # pymysql +just install-mdl-driver duckdb # duckdb-engine +# for other drivers: uv pip install ``` -> The server uses a **graceful fallback**: if `wren-agent` is not installed the three MDL tools -> are simply not registered, and all other tools continue to work unchanged. +> If `sqlalchemy` or `jsonschema` are missing the tools are silently skipped and +> all other server functionality continues to work. ### Environment Variables -| Variable | Required | Default | Description | -|---|---|---|---| -| `PYDANTIC_AI_MODEL` | **Yes** | `anthropic:claude-3-5-sonnet-latest` | LLM to use, e.g. `openai:gpt-4o` | -| `WREN_ENGINE_ENDPOINT` | No | — | ibis-server URL for MDL dry-plan validation | -| `MDL_AGENT_SKILLS_DIR` | No | — | Directory of `*.md` / `*.txt` skill files loaded at startup | -| `MDL_AGENT_MEMORY_PATH` | No | — | JSON file path for persistent cross-session memory | -| `MDL_AGENT_MAX_SKILLS` | No | `5` | Max skills injected per turn (semantic ranking) | - -Add these to your `.env` file or to the `"env"` block in your MCP config: - -```json -{ - "mcpServers": { - "wren": { - "command": "uv", - "args": [ - "--directory", "/ABSOLUTE/PATH/TO/wren-engine/mcp-server", - "run", "app/wren.py" - ], - "env": { - "WREN_URL": "localhost:8000", - "CONNECTION_INFO_FILE": "/path/connection.json", - "MDL_PATH": "/path/mdl.json", - "PYDANTIC_AI_MODEL": "anthropic:claude-3-5-sonnet-latest", - "ANTHROPIC_API_KEY": "", - "WREN_ENGINE_ENDPOINT": "http://localhost:8000", - "MDL_AGENT_MEMORY_PATH": "~/.wren/memory.json" - } - } - } -} -``` +| Variable | Required | Description | +|---|---|---| +| `WREN_ENGINE_ENDPOINT` | No | ibis-server URL — enables dry-plan check in `mdl_validate_manifest` | ### Available Tools | Tool | Description | |---|---| -| `mdl_chat(message, session_id?)` | Send a message to the MDL generation agent. Returns a follow-up question or a completed MDL JSON. | -| `mdl_reset_session(session_id?)` | Clear conversation history and DB connection for a session. | -| `mdl_list_sessions()` | List all active sessions and their message counts. | +| `mdl_connect_database(connection_string, session_id?)` | Connect to a DB via SQLAlchemy URL. Stores the connection under `session_id`. | +| `mdl_list_tables(session_id?)` | List all user tables in the connected database. | +| `mdl_get_column_info(table, session_id?)` | Column metadata: name, type, nullable, PK, FK references. | +| `mdl_get_column_stats(table, column, session_id?)` | Distinct count, null count, min, max for a column. | +| `mdl_get_sample_data(table, limit?, session_id?)` | Fetch sample rows (max 20) to understand data semantics. | +| `mdl_validate_manifest(mdl)` | Validate an MDL dict against the official JSON Schema, plus optional dry-plan. | + +Multiple concurrent sessions are supported via the `session_id` parameter (default `"default"`). ### Example Workflow -The following example shows how an AI agent (Claude, Copilot, etc.) would use the MDL tools -in a conversation: +The AI agent calls the tools in sequence — no additional configuration needed +beyond providing a connection string: ``` -User → "Generate an MDL for my PostgreSQL ecommerce database." - -Agent → mdl_chat("Generate an MDL for my PostgreSQL ecommerce database.") - ← [MDL Agent asks]: Please provide your PostgreSQL connection string. - -Agent → mdl_chat("postgresql://user:pass@localhost:5432/shop") - ← [MDL Agent asks]: I found tables: orders, customers, products, order_items. - Should I include all of them? - -Agent → mdl_chat("Yes, include all.") - ← [MDL Ready — use the 'deploy' tool with this manifest] - { - "catalog": "wren", - "schema": "public", - "dataSource": "POSTGRES", - "models": [ ... ] - } +User → "Generate an MDL for my PostgreSQL ecommerce database at + postgresql://user:pass@localhost:5432/shop" -Agent → deploy() - ← "MDL deployed successfully" -``` +Agent → mdl_connect_database("postgresql://user:pass@localhost:5432/shop") + ← "Connected. Found 5 table(s): customers, order_items, orders, products, reviews" -Multiple independent sessions can run in parallel using different `session_id` values: +Agent → mdl_get_column_info("orders") + ← [{"name":"id","type":"INTEGER","primary_key":true}, {"name":"customer_id", + "type":"INTEGER","foreign_key":{"table":"customers","column":"id"}}, ...] -``` -mdl_chat("...", session_id="project_a") -mdl_chat("...", session_id="project_b") -``` - -### Skills +Agent → mdl_get_column_info("customers") # repeats for each table... -You can inject domain knowledge (naming conventions, relationship patterns, etc.) by providing a -skills directory. Each `.md` or `.txt` file in the directory becomes a skill. On every turn, -the agent automatically selects the most relevant skills using TF-IDF cosine similarity — only -the top `MDL_AGENT_MAX_SKILLS` are injected, so the context stays focused. +Agent → mdl_validate_manifest({"catalog":"wren","schema":"public", + "dataSource":"POSTGRES","models":[...]}) + ← "MDL validation passed (JSON Schema + dry-plan)." -``` -MDL_AGENT_SKILLS_DIR=./skills/ -``` - -``` -skills/ - postgres_conventions.md # "Use bigint for all primary keys…" - ecommerce_patterns.md # "Order tables should reference customers via customer_id…" - bigquery_tips.md # "Use STRUCT for nested columns…" +Agent → deploy({"catalog":"wren","schema":"public","dataSource":"POSTGRES","models":[...]}) + ← "MDL deployed successfully" ``` --- diff --git a/mcp-server/app/mdl_tools.py b/mcp-server/app/mdl_tools.py index cf0b54aef..3eb1686ee 100644 --- a/mcp-server/app/mdl_tools.py +++ b/mcp-server/app/mdl_tools.py @@ -1,87 +1,81 @@ -"""MDL generation tools — powered by wren-agent. +"""MDL generation tools — flat MCP tools for agentic MDL creation. -Registers three tools onto the shared FastMCP instance: +Registers six tools onto the shared FastMCP instance. The calling AI agent +acts as the orchestrator: it connects to a database, explores the schema, +builds an MDL manifest, and validates it — all via direct tool calls, without +a secondary LLM layer. -- ``mdl_chat`` — multi-turn conversation to build an MDL manifest -- ``mdl_reset_session`` — clear a session (history + DB connection) -- ``mdl_list_sessions`` — list active sessions +Tools registered: -Requires ``wren-agent`` to be installed. If it is missing the module -loads silently and no tools are registered, so the rest of the server +- ``mdl_connect_database`` — establish a SQLAlchemy connection for a session +- ``mdl_list_tables`` — list user tables in the connected database +- ``mdl_get_column_info`` — column metadata (type, nullable, PK, FK) +- ``mdl_get_column_stats`` — distinct count, null count, min/max for a column +- ``mdl_get_sample_data`` — fetch sample rows to understand data semantics +- ``mdl_validate_manifest`` — validate an MDL dict (JSON Schema + dry-plan) + +Requires ``sqlalchemy`` and ``jsonschema`` to be installed:: + + uv pip install sqlalchemy jsonschema + +Plus the appropriate DB driver, e.g.: + uv pip install psycopg2-binary # PostgreSQL + uv pip install pymysql # MySQL + uv pip install duckdb-engine # DuckDB + +If ``sqlalchemy`` or ``jsonschema`` are missing the module loads silently and +no tools are registered, so the rest of the server continues to work unchanged. -Environment variables (all optional): +Environment variables: -- ``PYDANTIC_AI_MODEL`` — LLM to use, e.g. ``anthropic:claude-3-5-sonnet-latest`` -- ``WREN_ENGINE_ENDPOINT`` — ibis-server URL for MDL dry-plan validation -- ``MDL_AGENT_SKILLS_DIR`` — directory of *.md / *.txt skill files to load on startup -- ``MDL_AGENT_MEMORY_PATH`` — JSON file path for persistent cross-session memory -- ``MDL_AGENT_MAX_SKILLS`` — max skills injected per turn (default 5) +- ``WREN_ENGINE_ENDPOINT`` — ibis-server URL for MDL dry-plan validation """ from __future__ import annotations +import base64 +import json import os -from dataclasses import dataclass, field +from typing import Any from mcp.server.fastmcp import FastMCP from mcp.types import ToolAnnotations try: - from wren_agent import MDLAgent, AgentQuestion, SkillStore, MemoryStore + import sqlalchemy as sa + from sqlalchemy import inspect as sa_inspect, text + import jsonschema _AVAILABLE = True except ImportError: _AVAILABLE = False - # --------------------------------------------------------------------------- -# Session state +# Module-level session state: session_id → SA Engine # --------------------------------------------------------------------------- +_engines: dict[str, "sa.Engine"] = {} -@dataclass -class _Session: - agent: "MDLAgent" - history: list = field(default_factory=list) - - -_sessions: dict[str, _Session] = {} +# MDL JSON Schema — fetched once per process +_MDL_SCHEMA_URL = ( + "https://raw.githubusercontent.com/Canner/WrenAI/main/wren-mdl/mdl.schema.json" +) +_cached_schema: dict[str, Any] | None = None -# Shared across all sessions (loaded once at startup) -_skill_store: "SkillStore | None" = None -_memory: "MemoryStore | None" = None +def _get_mdl_schema() -> dict[str, Any]: + global _cached_schema + if _cached_schema is None: + import httpx # already in core deps + resp = httpx.get(_MDL_SCHEMA_URL, timeout=10.0) + resp.raise_for_status() + _cached_schema = resp.json() + return _cached_schema -def _init_shared_state() -> None: - """Load skills and memory from environment variables (called once).""" - global _skill_store, _memory - skills_dir = os.getenv("MDL_AGENT_SKILLS_DIR") - if skills_dir and os.path.isdir(skills_dir): - _skill_store = SkillStore() - _skill_store.load_dir(skills_dir) - skill_count = len(_skill_store) - print(f"[MDL Agent] Loaded {skill_count} skills from {skills_dir}") # noqa: T201 - - memory_path = os.getenv("MDL_AGENT_MEMORY_PATH") - if memory_path: - _memory = MemoryStore(persist_path=memory_path) - print(f"[MDL Agent] Memory store: {memory_path}") # noqa: T201 - - -def _get_or_create_session(session_id: str) -> "_Session": - if session_id not in _sessions: - max_skills = int(os.getenv("MDL_AGENT_MAX_SKILLS", "5")) - _sessions[session_id] = _Session( - agent=MDLAgent( - ibis_server_url=os.getenv("WREN_ENGINE_ENDPOINT"), - skill_store=_skill_store, - memory=_memory, - max_skills=max_skills, - ) - ) - return _sessions[session_id] +def _engine(session_id: str) -> "sa.Engine | None": + return _engines.get(session_id) # --------------------------------------------------------------------------- @@ -90,90 +84,287 @@ def _get_or_create_session(session_id: str) -> "_Session": def register_mdl_tools(mcp: FastMCP) -> None: - """Register MDL agent tools onto *mcp*. No-op if wren-agent is missing.""" + """Register flat MDL tools onto *mcp*. No-op if sqlalchemy/jsonschema missing.""" if not _AVAILABLE: - print("[MDL Agent] wren-agent not installed — MDL tools not registered.") # noqa: T201 + print( # noqa: T201 + "[MDL Tools] sqlalchemy or jsonschema not installed — " + "MDL tools not registered. Run: uv pip install sqlalchemy jsonschema" + ) return - _init_shared_state() + # ------------------------------------------------------------------ + # 1. Connect to database + # ------------------------------------------------------------------ @mcp.tool( annotations=ToolAnnotations( - title="MDL Chat", + title="Connect to Database", readOnlyHint=False, ), ) - async def mdl_chat(message: str, session_id: str = "default") -> str: - """Generate or refine a Wren MDL manifest through a guided conversation. + async def mdl_connect_database( + connection_string: str, + session_id: str = "default", + ) -> str: + """Connect to a database using a SQLAlchemy connection string. + + Must be called before any other ``mdl_*`` tool that needs schema access. + Each ``session_id`` maintains its own independent connection. - The agent explores the database schema and produces a validated MDL - manifest ready for deployment with the ``deploy`` tool. + Supported schemes (driver must be installed separately): + - ``postgresql://user:pass@host:5432/db`` (needs psycopg2-binary) + - ``mysql+pymysql://user:pass@host:3306/db`` (needs pymysql) + - ``duckdb:///path/to/file.db`` (needs duckdb-engine) + - ``sqlite:///path/to/file.db`` (built-in) - **Typical workflow**: + Args: + connection_string: SQLAlchemy URL. + session_id: Connection identifier, allows multiple parallel sessions. + + Returns: + Success message with table count, or an error description. + """ + try: + engine = sa.create_engine(connection_string) + with engine.connect() as conn: + conn.execute(text("SELECT 1")) + _engines[session_id] = engine + tables = sorted(sa_inspect(engine).get_table_names()) + preview = ", ".join(tables[:20]) + ("…" if len(tables) > 20 else "") + return f"Connected. Found {len(tables)} table(s): {preview}" + except Exception as e: + return f"Connection failed: {e}" + + # ------------------------------------------------------------------ + # 2. List tables + # ------------------------------------------------------------------ - 1. Say what database you want to model (e.g. "I want to model my PostgreSQL - ecommerce database"). - 2. The agent may ask for a connection string or clarifying questions. - 3. When MDL is ready, a JSON block is returned — copy it into ``deploy``. + @mcp.tool( + annotations=ToolAnnotations( + title="List Database Tables", + readOnlyHint=True, + ), + ) + async def mdl_list_tables(session_id: str = "default") -> str: + """List all user tables in the connected database. - You can run multiple independent sessions in parallel using different - ``session_id`` values. + Call ``mdl_connect_database`` first. Args: - message: Your message or answer to the agent's question. - session_id: Conversation ID. Reuse the same ID across turns to - continue an existing session. + session_id: Session to query. Returns: - Either a follow-up question prefixed with ``[MDL Agent asks]`` - or a completed MDL JSON prefixed with ``[MDL Ready]``. + JSON array of table names, or an error string. """ - session = _get_or_create_session(session_id) - resp = await session.agent.run(message, message_history=session.history) - session.history = resp.message_history + engine = _engine(session_id) + if engine is None: + return f"No connection for session '{session_id}'. Call mdl_connect_database first." + try: + tables = sorted(sa_inspect(engine).get_table_names()) + return json.dumps(tables) + except Exception as e: + return f"Error listing tables: {e}" + + # ------------------------------------------------------------------ + # 3. Column info + # ------------------------------------------------------------------ - if isinstance(resp.result, AgentQuestion): - return f"[MDL Agent asks]: {resp.result.question}" + @mcp.tool( + annotations=ToolAnnotations( + title="Get Column Info", + readOnlyHint=True, + ), + ) + async def mdl_get_column_info( + table: str, + session_id: str = "default", + ) -> str: + """Get column metadata for a table. - mdl_json = resp.result.model_dump_json(by_alias=True, indent=2) - return f"[MDL Ready — use the 'deploy' tool with this manifest]\n\n{mdl_json}" + Returns name, data type, nullable flag, whether it is a primary key, + and foreign key references (if any). + + Args: + table: Table name. + session_id: Session to query. + + Returns: + JSON array of column objects, or an error string. + """ + engine = _engine(session_id) + if engine is None: + return f"No connection for session '{session_id}'. Call mdl_connect_database first." + try: + insp = sa_inspect(engine) + columns = insp.get_columns(table) + pk_cols = set(insp.get_pk_constraint(table).get("constrained_columns", [])) + fk_map: dict[str, dict] = {} + for fk in insp.get_foreign_keys(table): + for local, ref in zip(fk["constrained_columns"], fk["referred_columns"]): + fk_map[local] = {"table": fk["referred_table"], "column": ref} + + result = [] + for col in columns: + name = col["name"] + entry: dict[str, Any] = { + "name": name, + "type": str(col["type"]), + "nullable": col.get("nullable", True), + "primary_key": name in pk_cols, + } + if name in fk_map: + entry["foreign_key"] = fk_map[name] + result.append(entry) + return json.dumps(result, default=str) + except Exception as e: + return f"Error getting column info for '{table}': {e}" + + # ------------------------------------------------------------------ + # 4. Column stats + # ------------------------------------------------------------------ @mcp.tool( annotations=ToolAnnotations( - title="Reset MDL Session", - readOnlyHint=False, + title="Get Column Statistics", + readOnlyHint=True, ), ) - async def mdl_reset_session(session_id: str = "default") -> str: - """Reset an MDL generation session. + async def mdl_get_column_stats( + table: str, + column: str, + session_id: str = "default", + ) -> str: + """Get basic statistics for a column: distinct count, null count, min, max. - Clears the conversation history and the database connection held inside - the session. The next ``mdl_chat`` call on this ID starts fresh. + Useful for inferring column semantics (e.g., ID vs. enum vs. metric). Args: - session_id: Session to reset (default: ``"default"``). + table: Table name. + column: Column name. + session_id: Session to query. + + Returns: + JSON object with stats, or an error string. """ - if session_id in _sessions: - del _sessions[session_id] - return f"Session '{session_id}' has been reset." - return f"Session '{session_id}' not found — nothing to reset." + engine = _engine(session_id) + if engine is None: + return f"No connection for session '{session_id}'. Call mdl_connect_database first." + try: + col_id = sa.column(column) + tbl = sa.table(table, col_id) + stmt = sa.select( + sa.func.count(sa.func.distinct(col_id)).label("distinct_count"), + (sa.func.count() - sa.func.count(col_id)).label("null_count"), + sa.func.min(col_id).label("min_val"), + sa.func.max(col_id).label("max_val"), + ).select_from(tbl) + with engine.connect() as conn: + row = conn.execute(stmt).one() + return json.dumps({ + "distinct_count": row.distinct_count, + "null_count": row.null_count, + "min": str(row.min_val) if row.min_val is not None else None, + "max": str(row.max_val) if row.max_val is not None else None, + }, default=str) + except Exception as e: + return f"Error getting stats for '{table}.{column}': {e}" + + # ------------------------------------------------------------------ + # 5. Sample data + # ------------------------------------------------------------------ @mcp.tool( annotations=ToolAnnotations( - title="List MDL Sessions", + title="Get Sample Data", readOnlyHint=True, ), ) - async def mdl_list_sessions() -> str: - """List all active MDL generation sessions and their message counts. + async def mdl_get_sample_data( + table: str, + limit: int = 5, + session_id: str = "default", + ) -> str: + """Fetch sample rows from a table to understand data semantics. + + Args: + table: Table name. + limit: Number of rows (default 5, capped at 20). + session_id: Session to query. - Returns a summary so you can track which sessions are in progress or - decide which one to continue. + Returns: + JSON array of row objects, or an error string. """ - if not _sessions: - return "No active MDL sessions." - lines = [ - f"- '{sid}': {len(s.history)} messages in history" - for sid, s in _sessions.items() - ] - return "\n".join(lines) + engine = _engine(session_id) + if engine is None: + return f"No connection for session '{session_id}'. Call mdl_connect_database first." + try: + limit = min(int(limit), 20) + tbl = sa.table(table) + stmt = sa.select(text("*")).select_from(tbl).limit(limit) + with engine.connect() as conn: + rows = conn.execute(stmt).mappings().all() + return json.dumps([dict(r) for r in rows], default=str) + except Exception as e: + return f"Error sampling '{table}': {e}" + + # ------------------------------------------------------------------ + # 6. Validate MDL manifest + # ------------------------------------------------------------------ + + @mcp.tool( + annotations=ToolAnnotations( + title="Validate MDL Manifest", + readOnlyHint=True, + ), + ) + async def mdl_validate_manifest(mdl: dict) -> str: + """Validate an MDL manifest dict against the official Wren JSON Schema. + + Optionally runs a dry-plan against ibis-server when + ``WREN_ENGINE_ENDPOINT`` is set. + + Pass the MDL as a plain dict (use ``"schema"`` key, not ``"schema_name"``). + + Args: + mdl: The MDL manifest as a dictionary. + + Returns: + A string describing the validation result. + """ + # Step 1: JSON Schema + try: + schema = _get_mdl_schema() + except Exception as e: + return f"Warning: could not fetch MDL schema: {e}. Skipping schema check." + + errors = list(jsonschema.Draft202012Validator(schema).iter_errors(mdl)) + if errors: + messages = "; ".join( + f"{'.'.join(str(p) for p in e.absolute_path) or ''}: {e.message}" + for e in errors[:5] + ) + return f"JSON Schema validation failed ({len(errors)} error(s)): {messages}" + + # Step 2: ibis-server dry-plan (optional) + import httpx # already in core deps + + ibis_url = os.getenv("WREN_ENGINE_ENDPOINT") + if not ibis_url: + return "JSON Schema validation passed. (Skipping dry-plan: WREN_ENGINE_ENDPOINT not set)" + + try: + manifest_str = base64.b64encode(json.dumps(mdl).encode()).decode() + async with httpx.AsyncClient(timeout=30.0) as client: + resp = await client.post( + f"{ibis_url.rstrip('/')}/v3/connector/dry-plan", + json={"manifest_str": manifest_str, "sql": "SELECT 1"}, + headers={"x-wren-fallback_disable": "true"}, + ) + if resp.status_code == 200: + return "MDL validation passed (JSON Schema + dry-plan)." + return ( + f"JSON Schema passed, but dry-plan failed " + f"(HTTP {resp.status_code}): {resp.text[:300]}" + ) + except Exception as e: + return f"JSON Schema passed, but dry-plan request failed: {e}" diff --git a/mcp-server/justfile b/mcp-server/justfile index 5d13bf092..142cf47aa 100644 --- a/mcp-server/justfile +++ b/mcp-server/justfile @@ -11,5 +11,18 @@ install-core: # Using pip install to avoid adding wheel to pyproject.toml uv pip install --force-reinstall {{ core-wheel-path }} -install-mdl-agent: - uv pip install --editable "../wren-agent" +install-mdl: + uv pip install "sqlalchemy>=2.0" "jsonschema>=4.0" + +# Install a database driver alongside the MDL tools: +# just install-mdl-driver postgres → psycopg2-binary +# just install-mdl-driver mysql → pymysql +# just install-mdl-driver duckdb → duckdb-engine +install-mdl-driver driver: + #!/usr/bin/env bash + case "{{driver}}" in + postgres) uv pip install psycopg2-binary ;; + mysql) uv pip install pymysql ;; + duckdb) uv pip install duckdb-engine ;; + *) echo "Unknown driver: {{driver}}. Supported: postgres, mysql, duckdb" && exit 1 ;; + esac diff --git a/mcp-server/pyproject.toml b/mcp-server/pyproject.toml index b81860c4d..c9d1e050b 100644 --- a/mcp-server/pyproject.toml +++ b/mcp-server/pyproject.toml @@ -14,9 +14,7 @@ dependencies = [ ] [project.optional-dependencies] -mdl-agent = [ - "wren-agent", +mdl = [ + "sqlalchemy>=2.0", + "jsonschema>=4.0", ] - -[tool.uv.sources] -wren-agent = { path = "../wren-agent", editable = true } diff --git a/mcp-server/uv.lock b/mcp-server/uv.lock index 3beb7f34a..5299afabf 100644 --- a/mcp-server/uv.lock +++ b/mcp-server/uv.lock @@ -2,142 +2,6 @@ version = 1 revision = 1 requires-python = ">=3.11" -[[package]] -name = "ag-ui-protocol" -version = "0.1.13" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "pydantic" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/04/b5/fc0b65b561d00d88811c8a7d98ee735833f81554be244340950e7b65820c/ag_ui_protocol-0.1.13.tar.gz", hash = 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feat(mcp-server): add generate_mdl prompt and next-step hints to MDL tools MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Register @mcp.prompt('generate_mdl') with optional connection_string arg Claude Desktop exposes this as a slash command /generate_mdl Defines the 6-step workflow: connect → explore → build → validate → deploy - Each tool return value now includes a '→ Next:' hint: mdl_connect_database → mdl_get_column_info + mdl_get_sample_data mdl_get_column_info → mdl_get_sample_data mdl_get_sample_data → mdl_validate_manifest mdl_validate_manifest → deploy --- mcp-server/app/mdl_tools.py | 97 ++++++++++++++++++++++++++++++++++--- 1 file changed, 90 insertions(+), 7 deletions(-) diff --git a/mcp-server/app/mdl_tools.py b/mcp-server/app/mdl_tools.py index 3eb1686ee..52d53283b 100644 --- a/mcp-server/app/mdl_tools.py +++ b/mcp-server/app/mdl_tools.py @@ -84,7 +84,7 @@ def _engine(session_id: str) -> "sa.Engine | None": def register_mdl_tools(mcp: FastMCP) -> None: - """Register flat MDL tools onto *mcp*. No-op if sqlalchemy/jsonschema missing.""" + """Register flat MDL tools and the generate_mdl prompt onto *mcp*.""" if not _AVAILABLE: print( # noqa: T201 "[MDL Tools] sqlalchemy or jsonschema not installed — " @@ -92,6 +92,67 @@ def register_mdl_tools(mcp: FastMCP) -> None: ) return + # ------------------------------------------------------------------ + # Prompt: defines the MDL generation workflow as a reusable starting point. + # Claude Desktop exposes this as a slash command: /generate_mdl + # Other clients can invoke it via prompts/get. + # ------------------------------------------------------------------ + + @mcp.prompt() + def generate_mdl(connection_string: str = "") -> str: + """Guided workflow for generating a Wren MDL manifest from a database. + + Invoke this prompt to start the MDL generation process. The agent + will follow the steps below automatically. + + Args: + connection_string: Optional SQLAlchemy URL to skip the first question. + e.g. ``postgresql://user:pass@host:5432/db`` + """ + conn_step = ( + f'You already have the connection string: `{connection_string}`\n' + f'Start at Step 2.' + if connection_string + else 'Ask the user for their database connection string (SQLAlchemy URL).' + ) + return f"""You are generating a Wren MDL manifest for a user's database. +Follow these steps in order — do not skip steps or ask unnecessary questions. + +## Step 1 — Get connection string +{conn_step} + +## Step 2 — Connect +Call `mdl_connect_database` with the connection string. +If it fails, report the error and ask the user to correct it. + +## Step 3 — Explore schema +For EVERY table returned: + a. Call `mdl_get_column_info()` to get column types, PKs, FKs. + b. Call `mdl_get_sample_data(
, limit=3)` to understand data semantics. + c. For columns where purpose is unclear, call `mdl_get_column_stats(
, )`. + +## Step 4 — Build MDL manifest +Construct the MDL JSON following these rules: +- `catalog`: use "wren" unless user specifies otherwise. +- `schema`: use the database's default schema (e.g. "public" for PostgreSQL). +- `dataSource`: set to the correct enum value (POSTGRES, MYSQL, DUCKDB, BIGQUERY, etc.). +- Each table → one `Model`. Set `tableReference.table` to the table name. +- Each column → one `Column`. Use the exact DB column name for `name`. +- Mark primary key columns with `"isHidden": false` and note them in model `primaryKey`. +- For FK columns, add a `Relationship` entry between the two models. +- Calculated columns and metrics can be added later — omit them for now. + +## Step 5 — Validate +Call `mdl_validate_manifest` with the constructed manifest dict. +If validation fails, fix the reported errors and validate again. + +## Step 6 — Deploy +Call `deploy` with the validated manifest. +Confirm success to the user. + +Do not ask for confirmation between steps unless you encounter an error. +""" + # ------------------------------------------------------------------ # 1. Connect to database # ------------------------------------------------------------------ @@ -131,7 +192,11 @@ async def mdl_connect_database( _engines[session_id] = engine tables = sorted(sa_inspect(engine).get_table_names()) preview = ", ".join(tables[:20]) + ("…" if len(tables) > 20 else "") - return f"Connected. Found {len(tables)} table(s): {preview}" + return ( + f"Connected. Found {len(tables)} table(s): {preview}\n" + f"→ Next: call `mdl_get_column_info` for each table, " + f"then `mdl_get_sample_data` to understand the data." + ) except Exception as e: return f"Connection failed: {e}" @@ -161,7 +226,10 @@ async def mdl_list_tables(session_id: str = "default") -> str: return f"No connection for session '{session_id}'. Call mdl_connect_database first." try: tables = sorted(sa_inspect(engine).get_table_names()) - return json.dumps(tables) + return ( + json.dumps(tables) + "\n" + "→ Next: call `mdl_get_column_info(
)` for each table." + ) except Exception as e: return f"Error listing tables: {e}" @@ -215,7 +283,11 @@ async def mdl_get_column_info( if name in fk_map: entry["foreign_key"] = fk_map[name] result.append(entry) - return json.dumps(result, default=str) + return ( + json.dumps(result, default=str) + "\n" + f"→ Next: call `mdl_get_sample_data('{table}', limit=3)` " + f"to understand data semantics." + ) except Exception as e: return f"Error getting column info for '{table}': {e}" @@ -303,7 +375,11 @@ async def mdl_get_sample_data( stmt = sa.select(text("*")).select_from(tbl).limit(limit) with engine.connect() as conn: rows = conn.execute(stmt).mappings().all() - return json.dumps([dict(r) for r in rows], default=str) + return ( + json.dumps([dict(r) for r in rows], default=str) + "\n" + "→ After exploring all tables: build the MDL manifest, " + "then call `mdl_validate_manifest`." + ) except Exception as e: return f"Error sampling '{table}': {e}" @@ -350,7 +426,11 @@ async def mdl_validate_manifest(mdl: dict) -> str: ibis_url = os.getenv("WREN_ENGINE_ENDPOINT") if not ibis_url: - return "JSON Schema validation passed. (Skipping dry-plan: WREN_ENGINE_ENDPOINT not set)" + return ( + "JSON Schema validation passed. " + "(Skipping dry-plan: WREN_ENGINE_ENDPOINT not set)\n" + "→ Next: call `deploy` with this manifest." + ) try: manifest_str = base64.b64encode(json.dumps(mdl).encode()).decode() @@ -361,7 +441,10 @@ async def mdl_validate_manifest(mdl: dict) -> str: headers={"x-wren-fallback_disable": "true"}, ) if resp.status_code == 200: - return "MDL validation passed (JSON Schema + dry-plan)." + return ( + "MDL validation passed (JSON Schema + dry-plan).\n" + "→ Next: call `deploy` with this manifest." + ) return ( f"JSON Schema passed, but dry-plan failed " f"(HTTP {resp.status_code}): {resp.text[:300]}" From 73e045c188771c6798fa8fbeb13c76bfa3e1ea67 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Sun, 1 Mar 2026 22:16:36 +0800 Subject: [PATCH 05/28] fix(mcp-server): fix Manifest dto causing deploy to fail silently Root causes of 'Sorry, no response was returned': 1. Manifest missing dataSource field - Pydantic silently dropped it 2. relationships/views had no defaults - ValidationError on empty views 3. Relationship used join_type/join_condition instead of joinType/condition Fixes: add dataSource field, default all list fields to [], fix Relationship field names to match official MDL schema, add populate_by_name=True, add missing fields (notNull, isHidden, refSql etc) --- mcp-server/app/dto.py | 40 ++++++++++++++++++++++++++++++++-------- 1 file changed, 32 insertions(+), 8 deletions(-) diff --git a/mcp-server/app/dto.py b/mcp-server/app/dto.py index 60e7eacf1..e33840d80 100644 --- a/mcp-server/app/dto.py +++ b/mcp-server/app/dto.py @@ -1,49 +1,73 @@ -from pydantic import BaseModel +from pydantic import BaseModel, ConfigDict from pydantic.fields import Field class Column(BaseModel): + model_config = ConfigDict(populate_by_name=True) + name: str type: str expression: str = None isCalculated: bool = False relationship: str = None + notNull: bool = False + isHidden: bool = False description: str = None class TableReference(BaseModel): + model_config = ConfigDict(populate_by_name=True) + catalog: str = None mdl_schema: str = Field(alias="schema", default=None) table: str class Model(BaseModel): + model_config = ConfigDict(populate_by_name=True) + name: str - tableReference: TableReference - columns: list[Column] + tableReference: TableReference = None + refSql: str = None + baseObject: str = None + columns: list[Column] = [] primaryKey: str = None + cached: bool = False + refreshTime: str = None + properties: dict = None description: str = None class Relationship(BaseModel): + model_config = ConfigDict(populate_by_name=True) + name: str models: list[str] - join_type: str - join_condition: str + joinType: str = Field(alias="joinType", default="") + condition: str = "" + description: str = None class View(BaseModel): + model_config = ConfigDict(populate_by_name=True) + name: str statement: str description: str = None + properties: dict = None class Manifest(BaseModel): + model_config = ConfigDict(populate_by_name=True) + catalog: str = "wren" mdl_schema: str = Field(alias="schema", default="public") - models: list[Model] - relationships: list[Relationship] - views: list[View] + dataSource: str = "" + models: list[Model] = [] + relationships: list[Relationship] = [] + views: list[View] = [] + metrics: list[dict] = [] + enumDefinitions: list[dict] = [] description: str = None From 09ccdc38293d9c2e38530e2537995967377f02d8 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Sun, 1 Mar 2026 22:58:04 +0800 Subject: [PATCH 06/28] feat(mcp-server): add mdl_save_project and mdl_load_project tools Save/load MDL as a dbt-like YAML project directory: wren_project.yml + models/*.yml + relationships.yml + views/metrics.yml Update generate_mdl prompt to include Step 6 (save project). Add pyyaml>=6.0 to optional mdl deps. --- mcp-server/app/mdl_tools.py | 202 +++++++++++++++++++++++++++++++++++- mcp-server/justfile | 2 +- mcp-server/pyproject.toml | 1 + 3 files changed, 199 insertions(+), 6 deletions(-) diff --git a/mcp-server/app/mdl_tools.py b/mcp-server/app/mdl_tools.py index 52d53283b..c09c11259 100644 --- a/mcp-server/app/mdl_tools.py +++ b/mcp-server/app/mdl_tools.py @@ -46,6 +46,7 @@ import sqlalchemy as sa from sqlalchemy import inspect as sa_inspect, text import jsonschema + import yaml _AVAILABLE = True except ImportError: @@ -87,8 +88,8 @@ def register_mdl_tools(mcp: FastMCP) -> None: """Register flat MDL tools and the generate_mdl prompt onto *mcp*.""" if not _AVAILABLE: print( # noqa: T201 - "[MDL Tools] sqlalchemy or jsonschema not installed — " - "MDL tools not registered. Run: uv pip install sqlalchemy jsonschema" + "[MDL Tools] sqlalchemy, jsonschema, or pyyaml not installed — " + "MDL tools not registered. Run: uv pip install sqlalchemy jsonschema pyyaml" ) return @@ -146,7 +147,11 @@ def generate_mdl(connection_string: str = "") -> str: Call `mdl_validate_manifest` with the constructed manifest dict. If validation fails, fix the reported errors and validate again. -## Step 6 — Deploy +## Step 6 — Save Project (optional) +Call `mdl_save_project(mdl=, project_dir="./my_project")` to persist the MDL +as YAML files in a local project directory for version control. + +## Step 7 — Deploy Call `deploy` with the validated manifest. Confirm success to the user. @@ -429,7 +434,8 @@ async def mdl_validate_manifest(mdl: dict) -> str: return ( "JSON Schema validation passed. " "(Skipping dry-plan: WREN_ENGINE_ENDPOINT not set)\n" - "→ Next: call `deploy` with this manifest." + "→ Next: optionally call `mdl_save_project(mdl=, project_dir=\"./my_project\")` " + "to save as YAML files, then call `deploy` with this manifest." ) try: @@ -443,7 +449,8 @@ async def mdl_validate_manifest(mdl: dict) -> str: if resp.status_code == 200: return ( "MDL validation passed (JSON Schema + dry-plan).\n" - "→ Next: call `deploy` with this manifest." + "→ Next: optionally call `mdl_save_project(mdl=, project_dir=\"./my_project\")` " + "to save as YAML files, then call `deploy` with this manifest." ) return ( f"JSON Schema passed, but dry-plan failed " @@ -451,3 +458,188 @@ async def mdl_validate_manifest(mdl: dict) -> str: ) except Exception as e: return f"JSON Schema passed, but dry-plan request failed: {e}" + + # ------------------------------------------------------------------ + # 7. Save MDL project as YAML + # ------------------------------------------------------------------ + + @mcp.tool( + annotations=ToolAnnotations( + title="Save MDL Project", + readOnlyHint=False, + ), + ) + def mdl_save_project(mdl: str, project_dir: str) -> str: + """Save an MDL JSON as a Wren project directory (dbt-like YAML structure). + + Creates: + /wren_project.yml — project metadata + /models/.yml — one file per model + /relationships.yml — all relationships + /views.yml — all views + /metrics.yml — all metrics + + Args: + mdl: The MDL JSON string (output from mdl_validate_manifest or manual assembly). + project_dir: Path to the directory to create (will be created if missing). + + Returns: + Summary of files written. + """ + try: + manifest = json.loads(mdl) + except json.JSONDecodeError as e: + return f"ERROR: Invalid JSON — {e}" + + project_dir = os.path.expanduser(project_dir) + os.makedirs(os.path.join(project_dir, "models"), exist_ok=True) + + files_written = [] + + # wren_project.yml + project_meta = { + "name": os.path.basename(project_dir), + "version": "1.0", + "catalog": manifest.get("catalog", ""), + "schema": manifest.get("schema", ""), + "dataSource": manifest.get("dataSource", ""), + } + project_yml = os.path.join(project_dir, "wren_project.yml") + with open(project_yml, "w") as f: + yaml.dump(project_meta, f, default_flow_style=False, allow_unicode=True) + files_written.append(project_yml) + + # models/.yml — one file per model + for model in manifest.get("models", []): + model_name = model.get("name", "unknown") + model_yml = os.path.join(project_dir, "models", f"{model_name}.yml") + with open(model_yml, "w") as f: + yaml.dump(model, f, default_flow_style=False, allow_unicode=True) + files_written.append(model_yml) + + # relationships.yml + rels_yml = os.path.join(project_dir, "relationships.yml") + with open(rels_yml, "w") as f: + yaml.dump( + {"relationships": manifest.get("relationships", [])}, + f, default_flow_style=False, allow_unicode=True, + ) + files_written.append(rels_yml) + + # views.yml + views_yml = os.path.join(project_dir, "views.yml") + with open(views_yml, "w") as f: + yaml.dump( + {"views": manifest.get("views", [])}, + f, default_flow_style=False, allow_unicode=True, + ) + files_written.append(views_yml) + + # metrics.yml + metrics_yml = os.path.join(project_dir, "metrics.yml") + with open(metrics_yml, "w") as f: + yaml.dump( + {"metrics": manifest.get("metrics", [])}, + f, default_flow_style=False, allow_unicode=True, + ) + files_written.append(metrics_yml) + + summary = "\n".join(f" ✓ {p}" for p in files_written) + return ( + f"Project saved to: {project_dir}\n\n" + f"Files written:\n{summary}\n\n" + f"→ Next: call `deploy(mdl={json.dumps(manifest)})` to deploy to Wren Engine, " + f"or commit {project_dir} to version control." + ) + + # ------------------------------------------------------------------ + # 8. Load MDL project from YAML + # ------------------------------------------------------------------ + + @mcp.tool( + annotations=ToolAnnotations( + title="Load MDL Project", + readOnlyHint=True, + ), + ) + def mdl_load_project(project_dir: str) -> str: + """Load a Wren project directory (YAML structure) and return the MDL JSON. + + Reads: + /wren_project.yml + /models/*.yml + /relationships.yml + /views.yml + /metrics.yml + + Args: + project_dir: Path to the Wren project directory. + + Returns: + The assembled MDL as a JSON string, ready to pass to `deploy`. + """ + import glob + + project_dir = os.path.expanduser(project_dir) + + if not os.path.isdir(project_dir): + return f"ERROR: Directory not found: {project_dir}" + + # wren_project.yml + project_yml = os.path.join(project_dir, "wren_project.yml") + if not os.path.isfile(project_yml): + return f"ERROR: wren_project.yml not found in {project_dir}" + + with open(project_yml) as f: + project_meta = yaml.safe_load(f) or {} + + manifest: dict[str, Any] = { + "catalog": project_meta.get("catalog", ""), + "schema": project_meta.get("schema", ""), + "dataSource": project_meta.get("dataSource", ""), + "models": [], + "relationships": [], + "views": [], + "metrics": [], + } + + # models/*.yml + model_files = sorted(glob.glob(os.path.join(project_dir, "models", "*.yml"))) + for mf in model_files: + with open(mf) as f: + model = yaml.safe_load(f) + if model: + manifest["models"].append(model) + + # relationships.yml + rels_yml = os.path.join(project_dir, "relationships.yml") + if os.path.isfile(rels_yml): + with open(rels_yml) as f: + data = yaml.safe_load(f) or {} + manifest["relationships"] = data.get("relationships", []) + + # views.yml + views_yml = os.path.join(project_dir, "views.yml") + if os.path.isfile(views_yml): + with open(views_yml) as f: + data = yaml.safe_load(f) or {} + manifest["views"] = data.get("views", []) + + # metrics.yml + metrics_yml = os.path.join(project_dir, "metrics.yml") + if os.path.isfile(metrics_yml): + with open(metrics_yml) as f: + data = yaml.safe_load(f) or {} + manifest["metrics"] = data.get("metrics", []) + + mdl_json = json.dumps(manifest, indent=2, ensure_ascii=False) + model_count = len(manifest["models"]) + rel_count = len(manifest["relationships"]) + + return ( + f"Project loaded from: {project_dir}\n" + f" Models: {model_count}, Relationships: {rel_count}\n\n" + f"MDL JSON:\n```json\n{mdl_json}\n```\n\n" + f"→ Next: call `mdl_validate_manifest(mdl=)` to validate, " + f"then `deploy(mdl=)` to deploy." + ) diff --git a/mcp-server/justfile b/mcp-server/justfile index 142cf47aa..c687ce6f0 100644 --- a/mcp-server/justfile +++ b/mcp-server/justfile @@ -12,7 +12,7 @@ install-core: uv pip install --force-reinstall {{ core-wheel-path }} install-mdl: - uv pip install "sqlalchemy>=2.0" "jsonschema>=4.0" + uv pip install "sqlalchemy>=2.0" "jsonschema>=4.0" "pyyaml>=6.0" # Install a database driver alongside the MDL tools: # just install-mdl-driver postgres → psycopg2-binary diff --git a/mcp-server/pyproject.toml b/mcp-server/pyproject.toml index c9d1e050b..3d436f688 100644 --- a/mcp-server/pyproject.toml +++ b/mcp-server/pyproject.toml @@ -17,4 +17,5 @@ dependencies = [ mdl = [ "sqlalchemy>=2.0", "jsonschema>=4.0", + "pyyaml>=6.0", ] From 9ff417e9986af5945a7c6d508dde05ddb7c138bf Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Sun, 1 Mar 2026 23:09:48 +0800 Subject: [PATCH 07/28] feat(mcp-server): parameterize project_dir and deploy in generate_mdl prompt --- mcp-server/app/mdl_tools.py | 229 ++++++------------------------------ 1 file changed, 38 insertions(+), 191 deletions(-) diff --git a/mcp-server/app/mdl_tools.py b/mcp-server/app/mdl_tools.py index c09c11259..437b70fe5 100644 --- a/mcp-server/app/mdl_tools.py +++ b/mcp-server/app/mdl_tools.py @@ -100,7 +100,11 @@ def register_mdl_tools(mcp: FastMCP) -> None: # ------------------------------------------------------------------ @mcp.prompt() - def generate_mdl(connection_string: str = "") -> str: + def generate_mdl( + connection_string: str = "", + project_dir: str = "", + deploy: bool = True, + ) -> str: """Guided workflow for generating a Wren MDL manifest from a database. Invoke this prompt to start the MDL generation process. The agent @@ -109,6 +113,11 @@ def generate_mdl(connection_string: str = "") -> str: Args: connection_string: Optional SQLAlchemy URL to skip the first question. e.g. ``postgresql://user:pass@host:5432/db`` + project_dir: Optional path to save the MDL as a YAML project directory. + If empty, Step 6 (save) is skipped. + e.g. ``./my_project`` + deploy: Whether to deploy the MDL to Wren Engine after validation. + Set to False to stop after saving the project. Default: True. """ conn_step = ( f'You already have the connection string: `{connection_string}`\n' @@ -116,6 +125,31 @@ def generate_mdl(connection_string: str = "") -> str: if connection_string else 'Ask the user for their database connection string (SQLAlchemy URL).' ) + + if project_dir: + save_step = ( + f'Call `mdl_save_project(mdl=, project_dir="{project_dir}")` ' + f'to persist the MDL as YAML files for version control.' + ) + else: + save_step = ( + 'Ask the user if they want to save the MDL as a YAML project directory.\n' + 'If yes, ask for the path and call ' + '`mdl_save_project(mdl=, project_dir=)`.\n' + 'If no, skip to the next step.' + ) + + if deploy: + deploy_step = ( + 'Call `deploy` with the validated manifest.\n' + 'Confirm success to the user.' + ) + else: + deploy_step = ( + 'Deployment was not requested — skip this step.\n' + 'Inform the user that the MDL is ready and saved (if a project_dir was used).' + ) + return f"""You are generating a Wren MDL manifest for a user's database. Follow these steps in order — do not skip steps or ask unnecessary questions. @@ -147,13 +181,11 @@ def generate_mdl(connection_string: str = "") -> str: Call `mdl_validate_manifest` with the constructed manifest dict. If validation fails, fix the reported errors and validate again. -## Step 6 — Save Project (optional) -Call `mdl_save_project(mdl=, project_dir="./my_project")` to persist the MDL -as YAML files in a local project directory for version control. +## Step 6 — Save Project +{save_step} ## Step 7 — Deploy -Call `deploy` with the validated manifest. -Confirm success to the user. +{deploy_step} Do not ask for confirmation between steps unless you encounter an error. """ @@ -458,188 +490,3 @@ async def mdl_validate_manifest(mdl: dict) -> str: ) except Exception as e: return f"JSON Schema passed, but dry-plan request failed: {e}" - - # ------------------------------------------------------------------ - # 7. Save MDL project as YAML - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Save MDL Project", - readOnlyHint=False, - ), - ) - def mdl_save_project(mdl: str, project_dir: str) -> str: - """Save an MDL JSON as a Wren project directory (dbt-like YAML structure). - - Creates: - /wren_project.yml — project metadata - /models/.yml — one file per model - /relationships.yml — all relationships - /views.yml — all views - /metrics.yml — all metrics - - Args: - mdl: The MDL JSON string (output from mdl_validate_manifest or manual assembly). - project_dir: Path to the directory to create (will be created if missing). - - Returns: - Summary of files written. - """ - try: - manifest = json.loads(mdl) - except json.JSONDecodeError as e: - return f"ERROR: Invalid JSON — {e}" - - project_dir = os.path.expanduser(project_dir) - os.makedirs(os.path.join(project_dir, "models"), exist_ok=True) - - files_written = [] - - # wren_project.yml - project_meta = { - "name": os.path.basename(project_dir), - "version": "1.0", - "catalog": manifest.get("catalog", ""), - "schema": manifest.get("schema", ""), - "dataSource": manifest.get("dataSource", ""), - } - project_yml = os.path.join(project_dir, "wren_project.yml") - with open(project_yml, "w") as f: - yaml.dump(project_meta, f, default_flow_style=False, allow_unicode=True) - files_written.append(project_yml) - - # models/.yml — one file per model - for model in manifest.get("models", []): - model_name = model.get("name", "unknown") - model_yml = os.path.join(project_dir, "models", f"{model_name}.yml") - with open(model_yml, "w") as f: - yaml.dump(model, f, default_flow_style=False, allow_unicode=True) - files_written.append(model_yml) - - # relationships.yml - rels_yml = os.path.join(project_dir, "relationships.yml") - with open(rels_yml, "w") as f: - yaml.dump( - {"relationships": manifest.get("relationships", [])}, - f, default_flow_style=False, allow_unicode=True, - ) - files_written.append(rels_yml) - - # views.yml - views_yml = os.path.join(project_dir, "views.yml") - with open(views_yml, "w") as f: - yaml.dump( - {"views": manifest.get("views", [])}, - f, default_flow_style=False, allow_unicode=True, - ) - files_written.append(views_yml) - - # metrics.yml - metrics_yml = os.path.join(project_dir, "metrics.yml") - with open(metrics_yml, "w") as f: - yaml.dump( - {"metrics": manifest.get("metrics", [])}, - f, default_flow_style=False, allow_unicode=True, - ) - files_written.append(metrics_yml) - - summary = "\n".join(f" ✓ {p}" for p in files_written) - return ( - f"Project saved to: {project_dir}\n\n" - f"Files written:\n{summary}\n\n" - f"→ Next: call `deploy(mdl={json.dumps(manifest)})` to deploy to Wren Engine, " - f"or commit {project_dir} to version control." - ) - - # ------------------------------------------------------------------ - # 8. Load MDL project from YAML - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Load MDL Project", - readOnlyHint=True, - ), - ) - def mdl_load_project(project_dir: str) -> str: - """Load a Wren project directory (YAML structure) and return the MDL JSON. - - Reads: - /wren_project.yml - /models/*.yml - /relationships.yml - /views.yml - /metrics.yml - - Args: - project_dir: Path to the Wren project directory. - - Returns: - The assembled MDL as a JSON string, ready to pass to `deploy`. - """ - import glob - - project_dir = os.path.expanduser(project_dir) - - if not os.path.isdir(project_dir): - return f"ERROR: Directory not found: {project_dir}" - - # wren_project.yml - project_yml = os.path.join(project_dir, "wren_project.yml") - if not os.path.isfile(project_yml): - return f"ERROR: wren_project.yml not found in {project_dir}" - - with open(project_yml) as f: - project_meta = yaml.safe_load(f) or {} - - manifest: dict[str, Any] = { - "catalog": project_meta.get("catalog", ""), - "schema": project_meta.get("schema", ""), - "dataSource": project_meta.get("dataSource", ""), - "models": [], - "relationships": [], - "views": [], - "metrics": [], - } - - # models/*.yml - model_files = sorted(glob.glob(os.path.join(project_dir, "models", "*.yml"))) - for mf in model_files: - with open(mf) as f: - model = yaml.safe_load(f) - if model: - manifest["models"].append(model) - - # relationships.yml - rels_yml = os.path.join(project_dir, "relationships.yml") - if os.path.isfile(rels_yml): - with open(rels_yml) as f: - data = yaml.safe_load(f) or {} - manifest["relationships"] = data.get("relationships", []) - - # views.yml - views_yml = os.path.join(project_dir, "views.yml") - if os.path.isfile(views_yml): - with open(views_yml) as f: - data = yaml.safe_load(f) or {} - manifest["views"] = data.get("views", []) - - # metrics.yml - metrics_yml = os.path.join(project_dir, "metrics.yml") - if os.path.isfile(metrics_yml): - with open(metrics_yml) as f: - data = yaml.safe_load(f) or {} - manifest["metrics"] = data.get("metrics", []) - - mdl_json = json.dumps(manifest, indent=2, ensure_ascii=False) - model_count = len(manifest["models"]) - rel_count = len(manifest["relationships"]) - - return ( - f"Project loaded from: {project_dir}\n" - f" Models: {model_count}, Relationships: {rel_count}\n\n" - f"MDL JSON:\n```json\n{mdl_json}\n```\n\n" - f"→ Next: call `mdl_validate_manifest(mdl=)` to validate, " - f"then `deploy(mdl=)` to deploy." - ) From 5e9a95479eaa9ffa0fcd2a9712663bc688f745a8 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Sun, 1 Mar 2026 23:25:22 +0800 Subject: [PATCH 08/28] chore(mcp-server): set deploy default to False, ask user at end of workflow --- mcp-server/app/mdl_tools.py | 5 ++-- mcp-server/uv.lock | 57 +++++++++++++++++++++++++++++++++++++ 2 files changed, 60 insertions(+), 2 deletions(-) diff --git a/mcp-server/app/mdl_tools.py b/mcp-server/app/mdl_tools.py index 437b70fe5..e7becc443 100644 --- a/mcp-server/app/mdl_tools.py +++ b/mcp-server/app/mdl_tools.py @@ -103,7 +103,7 @@ def register_mdl_tools(mcp: FastMCP) -> None: def generate_mdl( connection_string: str = "", project_dir: str = "", - deploy: bool = True, + deploy: bool = False, ) -> str: """Guided workflow for generating a Wren MDL manifest from a database. @@ -147,7 +147,8 @@ def generate_mdl( else: deploy_step = ( 'Deployment was not requested — skip this step.\n' - 'Inform the user that the MDL is ready and saved (if a project_dir was used).' + 'Inform the user that the MDL is ready and saved (if a project_dir was used).\n' + 'Ask if they want to deploy now or later.' ) return f"""You are generating a Wren MDL manifest for a user's database. diff --git a/mcp-server/uv.lock b/mcp-server/uv.lock index 5299afabf..04bcdb12a 100644 --- a/mcp-server/uv.lock +++ b/mcp-server/uv.lock @@ -389,6 +389,7 @@ dependencies = [ [package.optional-dependencies] mdl = [ { name = "jsonschema" }, 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a YAML project directory and return MDL JSON Requires ``sqlalchemy`` and ``jsonschema`` to be installed:: @@ -491,3 +493,188 @@ async def mdl_validate_manifest(mdl: dict) -> str: ) except Exception as e: return f"JSON Schema passed, but dry-plan request failed: {e}" + + # ------------------------------------------------------------------ + # 7. Save MDL project as YAML + # ------------------------------------------------------------------ + + @mcp.tool( + annotations=ToolAnnotations( + title="Save MDL Project", + readOnlyHint=False, + ), + ) + def mdl_save_project(mdl: str, project_dir: str) -> str: + """Save an MDL JSON as a Wren project directory (dbt-like YAML structure). + + Creates: + /wren_project.yml — project metadata + /models/.yml — one file per model + /relationships.yml — all relationships + /views.yml — all views + /metrics.yml — all metrics + + Args: + mdl: The MDL JSON string (output from mdl_validate_manifest or manual assembly). + project_dir: Path to the directory to create (will be created if missing). + + Returns: + Summary of files written. + """ + try: + manifest = json.loads(mdl) + except json.JSONDecodeError as e: + return f"ERROR: Invalid JSON — {e}" + + project_dir = os.path.expanduser(project_dir) + os.makedirs(os.path.join(project_dir, "models"), exist_ok=True) + + files_written = [] + + # wren_project.yml + project_meta = { + "name": os.path.basename(project_dir), + "version": "1.0", + "catalog": manifest.get("catalog", ""), + "schema": manifest.get("schema", ""), + "dataSource": manifest.get("dataSource", ""), + } + project_yml = os.path.join(project_dir, "wren_project.yml") + with open(project_yml, "w") as f: + yaml.dump(project_meta, f, default_flow_style=False, allow_unicode=True) + files_written.append(project_yml) + + # models/.yml — one file per model + for model in manifest.get("models", []): + model_name = model.get("name", "unknown") + model_yml = os.path.join(project_dir, "models", f"{model_name}.yml") + with open(model_yml, "w") as f: + yaml.dump(model, f, default_flow_style=False, allow_unicode=True) + files_written.append(model_yml) + + # relationships.yml + rels_yml = os.path.join(project_dir, "relationships.yml") + with open(rels_yml, "w") as f: + yaml.dump( + {"relationships": manifest.get("relationships", [])}, + f, default_flow_style=False, allow_unicode=True, + ) + files_written.append(rels_yml) + + # views.yml + views_yml = os.path.join(project_dir, "views.yml") + with open(views_yml, "w") as f: + yaml.dump( + {"views": manifest.get("views", [])}, + f, default_flow_style=False, allow_unicode=True, + ) + files_written.append(views_yml) + + # metrics.yml + metrics_yml = os.path.join(project_dir, "metrics.yml") + with open(metrics_yml, "w") as f: + yaml.dump( + {"metrics": manifest.get("metrics", [])}, + f, default_flow_style=False, allow_unicode=True, + ) + files_written.append(metrics_yml) + + summary = "\n".join(f" ✓ {p}" for p in files_written) + return ( + f"Project saved to: {project_dir}\n\n" + f"Files written:\n{summary}\n\n" + f"→ Next: call `deploy(mdl={json.dumps(manifest)})` to deploy to Wren Engine, " + f"or commit {project_dir} to version control." + ) + + # ------------------------------------------------------------------ + # 8. Load MDL project from YAML + # ------------------------------------------------------------------ + + @mcp.tool( + annotations=ToolAnnotations( + title="Load MDL Project", + readOnlyHint=True, + ), + ) + def mdl_load_project(project_dir: str) -> str: + """Load a Wren project directory (YAML structure) and return the MDL JSON. + + Reads: + /wren_project.yml + /models/*.yml + /relationships.yml + /views.yml + /metrics.yml + + Args: + project_dir: Path to the Wren project directory. + + Returns: + The assembled MDL as a JSON string, ready to pass to `deploy`. + """ + import glob + + project_dir = os.path.expanduser(project_dir) + + if not os.path.isdir(project_dir): + return f"ERROR: Directory not found: {project_dir}" + + # wren_project.yml + project_yml = os.path.join(project_dir, "wren_project.yml") + if not os.path.isfile(project_yml): + return f"ERROR: wren_project.yml not found in {project_dir}" + + with open(project_yml) as f: + project_meta = yaml.safe_load(f) or {} + + manifest: dict[str, Any] = { + "catalog": project_meta.get("catalog", ""), + "schema": project_meta.get("schema", ""), + "dataSource": project_meta.get("dataSource", ""), + "models": [], + "relationships": [], + "views": [], + "metrics": [], + } + + # models/*.yml + model_files = sorted(glob.glob(os.path.join(project_dir, "models", "*.yml"))) + for mf in model_files: + with open(mf) as f: + model = yaml.safe_load(f) + if model: + manifest["models"].append(model) + + # relationships.yml + rels_yml = os.path.join(project_dir, "relationships.yml") + if os.path.isfile(rels_yml): + with open(rels_yml) as f: + data = yaml.safe_load(f) or {} + manifest["relationships"] = data.get("relationships", []) + + # views.yml + views_yml = os.path.join(project_dir, "views.yml") + if os.path.isfile(views_yml): + with open(views_yml) as f: + data = yaml.safe_load(f) or {} + manifest["views"] = data.get("views", []) + + # metrics.yml + metrics_yml = os.path.join(project_dir, "metrics.yml") + if os.path.isfile(metrics_yml): + with open(metrics_yml) as f: + data = yaml.safe_load(f) or {} + manifest["metrics"] = data.get("metrics", []) + + mdl_json = json.dumps(manifest, indent=2, ensure_ascii=False) + model_count = len(manifest["models"]) + rel_count = len(manifest["relationships"]) + + return ( + f"Project loaded from: {project_dir}\n" + f" Models: {model_count}, Relationships: {rel_count}\n\n" + f"MDL JSON:\n```json\n{mdl_json}\n```\n\n" + f"→ Next: call `mdl_validate_manifest(mdl=)` to validate, " + f"then `deploy(mdl=)` to deploy." + ) From b4f892e6568c5e78f4a05e620cae9715e7cb8e79 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 11:21:43 +0800 Subject: [PATCH 10/28] chore: remove wren-agent --- wren-agent/.env.example | 7 - wren-agent/README.md | 53 - wren-agent/pyproject.toml | 44 - wren-agent/src/wren_agent/__init__.py | 15 - wren-agent/src/wren_agent/agent.py | 294 -- wren-agent/src/wren_agent/deps.py | 26 - wren-agent/src/wren_agent/memory.py | 141 - wren-agent/src/wren_agent/models.py | 202 -- wren-agent/src/wren_agent/prompts.py | 60 - wren-agent/src/wren_agent/skills.py | 307 -- wren-agent/src/wren_agent/tools/__init__.py | 1 - wren-agent/src/wren_agent/tools/db.py | 166 - wren-agent/src/wren_agent/tools/memory.py | 83 - wren-agent/src/wren_agent/tools/validate.py | 86 - wren-agent/tests/test_agent.py | 627 ---- wren-agent/uv.lock | 3569 ------------------- 16 files changed, 5681 deletions(-) delete mode 100644 wren-agent/.env.example delete mode 100644 wren-agent/README.md delete mode 100644 wren-agent/pyproject.toml delete mode 100644 wren-agent/src/wren_agent/__init__.py delete mode 100644 wren-agent/src/wren_agent/agent.py delete mode 100644 wren-agent/src/wren_agent/deps.py delete mode 100644 wren-agent/src/wren_agent/memory.py delete mode 100644 wren-agent/src/wren_agent/models.py delete mode 100644 wren-agent/src/wren_agent/prompts.py delete mode 100644 wren-agent/src/wren_agent/skills.py delete mode 100644 wren-agent/src/wren_agent/tools/__init__.py delete mode 100644 wren-agent/src/wren_agent/tools/db.py delete mode 100644 wren-agent/src/wren_agent/tools/memory.py delete mode 100644 wren-agent/src/wren_agent/tools/validate.py delete mode 100644 wren-agent/tests/test_agent.py delete mode 100644 wren-agent/uv.lock diff --git a/wren-agent/.env.example b/wren-agent/.env.example deleted file mode 100644 index 67349f9c2..000000000 --- a/wren-agent/.env.example +++ /dev/null @@ -1,7 +0,0 @@ -# LLM Provider API Keys (set the one matching your chosen model) -ANTHROPIC_API_KEY=your_anthropic_api_key_here -OPENAI_API_KEY=your_openai_api_key_here - -# Optional: Wren ibis-server endpoint for MDL dry-plan validation -# If not set, validate_mdl will only run JSON Schema validation -WREN_ENGINE_ENDPOINT=http://localhost:8000 diff --git a/wren-agent/README.md b/wren-agent/README.md deleted file mode 100644 index 21f633844..000000000 --- a/wren-agent/README.md +++ /dev/null @@ -1,53 +0,0 @@ -# wren-agent - -AI agent for agentic MDL generation for [Wren Engine](https://github.com/Canner/WrenAI). - -## Overview - -`wren-agent` is a Python library that uses [PydanticAI](https://ai.pydantic.dev/) to drive a multi-turn conversation with the user, explores a connected database via SQLAlchemy, and outputs a valid [Wren MDL](https://github.com/Canner/WrenAI/tree/main/wren-mdl) manifest. - -## Usage - -```python -import asyncio -from wren_agent import MDLAgent - -async def main(): - agent = MDLAgent() # uses PYDANTIC_AI_MODEL env var, defaults to claude-3-5-sonnet-latest - history = [] - - # Turn 1: agent asks for connection string - resp = await agent.run("I want to generate MDL for my database") - print(resp.result) # AgentQuestion(question="Please provide your connection string…") - history = resp.message_history - - # Turn 2: provide connection string - resp = await agent.run("postgresql://user:pass@localhost/mydb", message_history=history) - # resp.result → MDLManifest(...) - -asyncio.run(main()) -``` - -## Installation - -```bash -# Basic (no DB driver) -uv add wren-agent - -# With PostgreSQL support -uv add "wren-agent[postgres]" - -# With MySQL + DuckDB -uv add "wren-agent[mysql,duckdb]" -``` - -## Configuration - -| Environment variable | Description | -|---|---| -| `PYDANTIC_AI_MODEL` | LLM model string (e.g., `claude-3-5-sonnet-latest`, `openai:gpt-4o`) | -| `ANTHROPIC_API_KEY` | Required when using Claude | -| `OPENAI_API_KEY` | Required when using OpenAI | -| `WREN_ENGINE_ENDPOINT` | Optional ibis-server URL for dry-plan validation (e.g., `http://localhost:8000`) | - -Copy `.env.example` to `.env` and fill in your credentials. diff --git a/wren-agent/pyproject.toml b/wren-agent/pyproject.toml deleted file mode 100644 index 045247dd9..000000000 --- a/wren-agent/pyproject.toml +++ /dev/null @@ -1,44 +0,0 @@ -[project] -name = "wren-agent" -version = "0.1.0" -description = "AI agent for agentic MDL generation for Wren Engine" -readme = "README.md" -requires-python = ">=3.11" -dependencies = [ - "pydantic-ai>=0.0.40", - "pydantic>=2.10", - "sqlalchemy>=2.0", - "httpx>=0.28", - "jsonschema>=4.0", - "python-dotenv>=1.0", -] - -[project.optional-dependencies] -postgres = ["psycopg2-binary>=2.9"] -mysql = ["pymysql>=1.1"] -duckdb = ["duckdb>=1.0", "duckdb-engine>=0.13"] -all = ["wren-agent[postgres,mysql,duckdb]"] -dev = [ - "pytest>=8.0", - "pytest-asyncio>=0.24", - "pytest-mock>=3.14", - "ruff>=0.11", -] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.wheel] -packages = ["src/wren_agent"] - -[tool.pytest.ini_options] -asyncio_mode = "auto" -testpaths = ["tests"] - -[tool.ruff] -line-length = 88 -target-version = "py311" - -[tool.ruff.lint] -select = ["E", "F", "I", "UP"] diff --git a/wren-agent/src/wren_agent/__init__.py b/wren-agent/src/wren_agent/__init__.py deleted file mode 100644 index 38e3fa3a9..000000000 --- a/wren-agent/src/wren_agent/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Wren Agent — agentic MDL generation for Wren Engine.""" - -from wren_agent.agent import MDLAgent, MDLAgentResponse -from wren_agent.memory import MemoryStore -from wren_agent.models import AgentQuestion, MDLManifest -from wren_agent.skills import SkillStore - -__all__ = [ - "MDLAgent", - "MDLAgentResponse", - "AgentQuestion", - "MDLManifest", - "SkillStore", - "MemoryStore", -] diff --git a/wren-agent/src/wren_agent/agent.py b/wren-agent/src/wren_agent/agent.py deleted file mode 100644 index 86c1ce37b..000000000 --- a/wren-agent/src/wren_agent/agent.py +++ /dev/null @@ -1,294 +0,0 @@ -"""MDL generation agent assembled with PydanticAI.""" - -from __future__ import annotations - -import os -from dataclasses import dataclass, field -from typing import Union - -from pydantic_ai import Agent, RunContext -from pydantic_ai.messages import ModelMessage - -from wren_agent.deps import AgentDeps -from wren_agent.memory import MemoryStore -from wren_agent.models import AgentQuestion, MDLManifest -from wren_agent.prompts import SYSTEM_PROMPT -from wren_agent.skills import SkillStore -from wren_agent.tools.db import ( - connect_to_database, - get_column_info, - get_column_stats, - get_sample_data, - list_tables, -) -from wren_agent.tools.memory import ( - tool_forget, - tool_list_memory_keys, - tool_recall, - tool_remember, - tool_search_memory, -) -from wren_agent.tools.validate import validate_mdl - -# --------------------------------------------------------------------------- -# Internal PydanticAI agent -# --------------------------------------------------------------------------- - -# model=None: supplied at run time via agent.run(model=...) to avoid requiring -# LLM credentials at import time. -_agent: Agent[AgentDeps, Union[AgentQuestion, MDLManifest]] = Agent( - model=None, - output_type=Union[AgentQuestion, MDLManifest], # type: ignore[arg-type] - deps_type=AgentDeps, - system_prompt=SYSTEM_PROMPT, -) - - -# Register tools -@_agent.tool -async def tool_connect_to_database( - ctx: RunContext[AgentDeps], connection_string: str -) -> str: - """Establish a database connection. - - Call this with the user's connection string before exploring the schema. - Example: postgresql://user:password@host:5432/dbname - """ - return await connect_to_database(ctx, connection_string) - - -@_agent.tool -async def tool_list_tables(ctx: RunContext[AgentDeps]) -> list[str] | str: - """List all user tables in the connected database.""" - return await list_tables(ctx) - - -@_agent.tool -async def tool_get_column_info( - ctx: RunContext[AgentDeps], table: str -) -> list[dict] | str: - """Get column metadata for a table: name, type, nullable, primary key, foreign keys.""" - return await get_column_info(ctx, table) - - -@_agent.tool -async def tool_get_column_stats( - ctx: RunContext[AgentDeps], table: str, column: str -) -> dict | str: - """Get basic statistics for a column: distinct count, null count, min, max.""" - return await get_column_stats(ctx, table, column) - - -@_agent.tool -async def tool_get_sample_data( - ctx: RunContext[AgentDeps], table: str, limit: int = 5 -) -> list[dict] | str: - """Fetch sample rows from a table to understand data semantics.""" - return await get_sample_data(ctx, table, limit) - - -@_agent.tool -async def tool_validate_mdl(ctx: RunContext[AgentDeps], mdl: dict) -> str: - """Validate an MDL manifest dict against the official JSON Schema and optionally - run a dry-plan against ibis-server. - - Pass the MDL as a plain dict (using 'schema' key, not 'schema_name'). - """ - return await validate_mdl(ctx, mdl) - - -# ------------------------------------------------------------------ -# Memory tools -# ------------------------------------------------------------------ - -@_agent.tool -async def _tool_remember( - ctx: RunContext[AgentDeps], key: str, value: str, tags: list[str] | None = None -) -> str: - """Store a piece of information in persistent memory for future sessions. - - Use this for facts that will be useful later, such as: - - Database schema summaries - - User naming preferences - - Previously validated MDL fragments - - Args: - key: Short identifier, e.g. "tpch_schema" or "user_prefers_snake_case". - value: The text to remember. - tags: Optional tags for grouping, e.g. ["schema", "tpch"]. - """ - return await tool_remember(ctx, key, value, tags) - - -@_agent.tool -async def _tool_recall(ctx: RunContext[AgentDeps], key: str) -> str: - """Retrieve a previously stored memory entry by key. - - Returns the stored value, or a 'not found' message. - """ - return await tool_recall(ctx, key) - - -@_agent.tool -async def _tool_search_memory(ctx: RunContext[AgentDeps], query: str) -> str: - """Search memory entries whose key or value contains the query string. - - Call this at the start of a conversation to check if relevant prior - knowledge about this database or user preferences already exists. - """ - return await tool_search_memory(ctx, query) - - -@_agent.tool -async def _tool_list_memory_keys(ctx: RunContext[AgentDeps]) -> str: - """List all keys currently stored in memory.""" - return await tool_list_memory_keys(ctx) - - -@_agent.tool -async def _tool_forget(ctx: RunContext[AgentDeps], key: str) -> str: - """Delete a memory entry by key when a stored fact is outdated or incorrect.""" - return await tool_forget(ctx, key) - - -# --------------------------------------------------------------------------- -# Public MDLAgent wrapper -# --------------------------------------------------------------------------- - - -@dataclass -class MDLAgentResponse: - """Response returned from MDLAgent.run().""" - - result: AgentQuestion | MDLManifest - """The agent output: either a follow-up question or the completed MDL manifest.""" - - message_history: list[ModelMessage] = field(default_factory=list) - """Full conversation history — pass back to the next run() call to continue.""" - - -class MDLAgent: - """High-level wrapper around the PydanticAI MDL generation agent. - - Supports optional **skills** (domain knowledge injected per run) and - **memory** (persistent key-value store readable/writable by the agent). - - Basic usage (multi-turn):: - - agent = MDLAgent() - history = [] - - resp = await agent.run("I want to create MDL for my database") - # resp.result → AgentQuestion(question="Please provide your connection string…") - history = resp.message_history - - resp = await agent.run("postgresql://user:pass@localhost/mydb", message_history=history) - # resp.result → MDLManifest(...) - - With skills and memory:: - - from wren_agent.skills import SkillStore - from wren_agent.memory import MemoryStore - - skills = SkillStore() - skills.load_dir("my_skills/") # loads *.md / *.txt - skills.add("ecommerce", - "Order tables usually reference customers via customer_id FK.") - - memory = MemoryStore(persist_path="~/.wren_agent/memory.json") - - agent = MDLAgent(skill_store=skills, memory=memory) - """ - - def __init__( - self, - model: str | None = None, - ibis_server_url: str | None = None, - skill_store: SkillStore | None = None, - memory: MemoryStore | None = None, - max_skills: int = 5, - ) -> None: - """ - Args: - model: A pydantic-ai model string, e.g. ``"anthropic:claude-3-5-sonnet-latest"``, - ``"openai:gpt-4o"``. Defaults to ``PYDANTIC_AI_MODEL`` env var. - ibis_server_url: Optional Wren ibis-server URL for dry-plan validation. - Defaults to ``WREN_ENGINE_ENDPOINT`` env var. - skill_store: Optional :class:`~wren_agent.skills.SkillStore` with - domain-specific knowledge selected and injected per run. - On each :meth:`run`, the store's ``select(query, top_k)`` - is called to pick the most relevant skills for that message. - memory: Optional :class:`~wren_agent.memory.MemoryStore` for persistent - cross-session memory. The agent can call memory tools to read - and write entries during the conversation. - max_skills: Maximum number of skills to inject per run (default 5). - Skills are ranked by TF-IDF cosine similarity to the current - message before injection. - """ - self._model = ( - model - or os.environ.get("PYDANTIC_AI_MODEL") - or "anthropic:claude-3-5-sonnet-latest" - ) - self._ibis_server_url = ibis_server_url or os.environ.get( - "WREN_ENGINE_ENDPOINT" - ) - self._skill_store = skill_store - self._memory = memory - self._max_skills = max_skills - # Persist SA engine across turns so the DB connection survives multi-turn - self._engine = None - - async def run( - self, - message: str, - *, - message_history: list[ModelMessage] | None = None, - ) -> MDLAgentResponse: - """Send a message to the agent and return the response. - - Args: - message: The user's message. - message_history: Previous messages from prior run() calls. Pass the - ``message_history`` from the last MDLAgentResponse to - continue an ongoing conversation. - - Returns: - MDLAgentResponse with ``result`` (AgentQuestion or MDLManifest) - and ``message_history`` for the next turn. - """ - deps = AgentDeps( - engine=self._engine, - ibis_server_url=self._ibis_server_url, - memory=self._memory, - ) - - # Skills are injected as per-run instructions so they don't permanently - # bloat the base system prompt for every agent instance. - # select() applies tag filter + TF-IDF semantic ranking against the user - # message so only the most relevant skills are injected. - instructions: str | None = ( - self._skill_store.select( - query=message, - top_k=self._max_skills, - ).as_instructions() - if self._skill_store - else None - ) - - result = await _agent.run( - message, - model=self._model, - deps=deps, - message_history=message_history or [], - instructions=instructions, - ) - - # Persist the engine for subsequent turns - if deps.engine is not None: - self._engine = deps.engine - - return MDLAgentResponse( - result=result.output, - message_history=list(result.all_messages()), - ) diff --git a/wren-agent/src/wren_agent/deps.py b/wren-agent/src/wren_agent/deps.py deleted file mode 100644 index ae1551ee4..000000000 --- a/wren-agent/src/wren_agent/deps.py +++ /dev/null @@ -1,26 +0,0 @@ -"""Agent dependency container, passed to all tools via RunContext.""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import TYPE_CHECKING - -import sqlalchemy as sa - -if TYPE_CHECKING: - from wren_agent.memory import MemoryStore - - -@dataclass -class AgentDeps: - """Holds stateful resources shared across all tool calls in one agent run.""" - - # Set by connect_to_database tool; None until connection is established - engine: sa.Engine | None = None - - # Optional ibis-server URL for dry-plan validation - # Reads from env WREN_ENGINE_ENDPOINT if not supplied explicitly - ibis_server_url: str | None = field(default=None) - - # Optional persistent memory store; None means memory tools are disabled - memory: MemoryStore | None = None diff --git a/wren-agent/src/wren_agent/memory.py b/wren-agent/src/wren_agent/memory.py deleted file mode 100644 index 1ae04e28f..000000000 --- a/wren-agent/src/wren_agent/memory.py +++ /dev/null @@ -1,141 +0,0 @@ -"""Memory store — persistent key-value memory readable and writable by the agent. - -The agent can call memory tools to: -- ``remember(key, value)`` — store a fact for future sessions -- ``recall(key)`` — retrieve a stored fact -- ``search_memory(query)`` — find entries whose key or value contains a substring -- ``list_memory_keys()`` — list all stored keys -- ``forget(key)`` — delete an entry - -By default the store is in-memory only (lost when the process exits). -Pass ``persist_path`` to persist to a JSON file between sessions. - -Example:: - - mem = MemoryStore(persist_path="~/.wren_agent/memory.json") - agent = MDLAgent(memory=mem) -""" - -from __future__ import annotations - -import json -from dataclasses import asdict, dataclass, field -from datetime import datetime, timezone -from pathlib import Path - - -@dataclass -class MemoryEntry: - key: str - value: str - created_at: str = field( - default_factory=lambda: datetime.now(timezone.utc).isoformat() - ) - tags: list[str] = field(default_factory=list) - - -class MemoryStore: - """Simple key-value memory that the agent can read and write. - - Args: - persist_path: If given, the store is loaded from this JSON file on init - and saved back after every write. ``~`` is expanded. - """ - - def __init__(self, persist_path: str | Path | None = None) -> None: - self._entries: dict[str, MemoryEntry] = {} - self._persist_path: Path | None = ( - Path(persist_path).expanduser() if persist_path else None - ) - if self._persist_path and self._persist_path.exists(): - self._load() - - # ------------------------------------------------------------------ - # Write - # ------------------------------------------------------------------ - - def remember( - self, - key: str, - value: str, - *, - tags: list[str] | None = None, - overwrite: bool = True, - ) -> None: - """Store a memory entry. - - Args: - key: Identifier (e.g., ``"tpch_db_schema"``, ``"user_prefers_snake_case"``). - value: The content to remember. - tags: Optional list of tags for filtering. - overwrite: If False and key already exists, raises ValueError. - """ - if not overwrite and key in self._entries: - raise ValueError(f"Memory key '{key}' already exists. Use overwrite=True to update.") - self._entries[key] = MemoryEntry( - key=key, - value=value, - created_at=datetime.now(timezone.utc).isoformat(), - tags=tags or [], - ) - self._save() - - def forget(self, key: str) -> bool: - """Delete a memory entry. Returns True if it existed.""" - existed = key in self._entries - if existed: - del self._entries[key] - self._save() - return existed - - # ------------------------------------------------------------------ - # Read - # ------------------------------------------------------------------ - - def recall(self, key: str) -> str | None: - """Retrieve the value for a key, or None if not found.""" - entry = self._entries.get(key) - return entry.value if entry else None - - def search(self, query: str) -> list[MemoryEntry]: - """Return entries whose key or value contains ``query`` (case-insensitive).""" - q = query.lower() - return [ - e for e in self._entries.values() - if q in e.key.lower() or q in e.value.lower() - ] - - def list_keys(self) -> list[str]: - return list(self._entries.keys()) - - def get_entry(self, key: str) -> MemoryEntry | None: - return self._entries.get(key) - - def all_entries(self) -> list[MemoryEntry]: - return list(self._entries.values()) - - def __len__(self) -> int: - return len(self._entries) - - # ------------------------------------------------------------------ - # Persistence - # ------------------------------------------------------------------ - - def _save(self) -> None: - if self._persist_path is None: - return - self._persist_path.parent.mkdir(parents=True, exist_ok=True) - data = [asdict(e) for e in self._entries.values()] - self._persist_path.write_text( - json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8" - ) - - def _load(self) -> None: - assert self._persist_path is not None - try: - data = json.loads(self._persist_path.read_text(encoding="utf-8")) - for item in data: - entry = MemoryEntry(**item) - self._entries[entry.key] = entry - except (json.JSONDecodeError, TypeError, KeyError): - pass # Corrupted file — start fresh diff --git a/wren-agent/src/wren_agent/models.py b/wren-agent/src/wren_agent/models.py deleted file mode 100644 index 8c5763246..000000000 --- a/wren-agent/src/wren_agent/models.py +++ /dev/null @@ -1,202 +0,0 @@ -"""Pydantic models for Wren MDL manifest. - -Corresponds to the official schema: -https://raw.githubusercontent.com/Canner/WrenAI/main/wren-mdl/mdl.schema.json -""" - -from __future__ import annotations - -from typing import Any, Literal - -from pydantic import BaseModel, ConfigDict, Field, model_validator - - -class SessionProperty(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - required: bool - defaultExpr: str | None = None - - -class ColumnLevelAccessControlThreshold(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - value: str - dataType: Literal["NUMERIC", "STRING"] - - -class ColumnLevelAccessControl(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - operator: Literal[ - "EQUALS", - "NOT_EQUALS", - "GREATER_THAN", - "LESS_THAN", - "GREATER_THAN_OR_EQUALS", - "LESS_THAN_OR_EQUALS", - ] - requiredProperties: list[SessionProperty] = Field(min_length=1, max_length=1) - threshold: ColumnLevelAccessControlThreshold - - -class MDLColumn(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - type: str - relationship: str | None = None - isCalculated: bool | None = None - notNull: bool | None = None - expression: str | None = None - isHidden: bool | None = None - columnLevelAccessControl: ColumnLevelAccessControl | None = None - properties: dict[str, Any] | None = None - - -class TableReference(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - catalog: str | None = None - # Named schema_name internally; alias "schema" matches MDL JSON key. - schema_name: str | None = Field(default=None, alias="schema") - table: str - - -class RowLevelAccessControl(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - requiredProperties: list[SessionProperty] - condition: str - - -class MDLModel(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - tableReference: TableReference | None = None - refSql: str | None = None - baseObject: str | None = None - columns: list[MDLColumn] | None = None - primaryKey: str | None = None - cached: bool | None = None - refreshTime: str | None = None - rowLevelAccessControls: list[RowLevelAccessControl] | None = None - properties: dict[str, Any] | None = None - - @model_validator(mode="after") - def validate_table_source(self) -> MDLModel: - sources = [ - x - for x in [self.tableReference, self.refSql, self.baseObject] - if x is not None - ] - if len(sources) != 1: - raise ValueError( - "Exactly one of tableReference, refSql, or baseObject must be provided" - ) - return self - - -class MDLRelationship(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - models: list[str] = Field(min_length=2, max_length=2) - joinType: Literal["ONE_TO_ONE", "ONE_TO_MANY", "MANY_TO_ONE", "MANY_TO_MANY"] - condition: str - properties: dict[str, Any] | None = None - - -class TimeGrain(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - refColumn: str - dateParts: list[ - Literal["YEAR", "QUARTER", "MONTH", "WEEK", "DAY", "HOUR", "MINUTE", "SECOND"] - ] - - -class MDLMetric(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - baseObject: str - dimension: list[MDLColumn] - measure: list[MDLColumn] = Field(min_length=1) - timeGrain: list[TimeGrain] | None = None - cached: bool | None = None - refreshTime: str | None = None - properties: dict[str, Any] | None = None - - -class MDLView(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - statement: str - properties: dict[str, Any] | None = None - - -class EnumMember(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - value: str | None = None - properties: dict[str, Any] | None = None - - -class EnumDefinition(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - name: str - values: list[EnumMember] - properties: dict[str, Any] | None = None - - -DataSourceType = Literal[ - "BIGQUERY", - "CLICKHOUSE", - "CANNER", - "TRINO", - "MSSQL", - "MYSQL", - "POSTGRES", - "SNOWFLAKE", - "DUCKDB", - "LOCAL_FILE", - "S3_FILE", - "GCS_FILE", - "MINIO_FILE", - "ORACLE", - "ATHENA", - "REDSHIFT", -] - - -class MDLManifest(BaseModel): - model_config = ConfigDict(populate_by_name=True) - - catalog: str - schema_name: str = Field(alias="schema") - dataSource: DataSourceType | None = None - sampleDataFolder: str | None = None - models: list[MDLModel] | None = None - relationships: list[MDLRelationship] | None = None - metrics: list[MDLMetric] | None = None - views: list[MDLView] | None = None - enumDefinitions: list[EnumDefinition] | None = None - - def to_mdl_dict(self) -> dict[str, Any]: - """Serialize to MDL-compatible dict (uses 'schema' key, not 'schema_name').""" - return self.model_dump(by_alias=True, exclude_none=True) - - -class AgentQuestion(BaseModel): - """Returned when the agent needs more information from the user.""" - - question: str diff --git a/wren-agent/src/wren_agent/prompts.py b/wren-agent/src/wren_agent/prompts.py deleted file mode 100644 index 748835e1a..000000000 --- a/wren-agent/src/wren_agent/prompts.py +++ /dev/null @@ -1,60 +0,0 @@ -"""System prompt for the MDL generation agent.""" - -SYSTEM_PROMPT = """ -You are a Wren MDL expert. Your job is to help users generate a complete and valid -Wren MDL (Modeling Definition Language) manifest for their database. - -MDL is a JSON structure that defines semantic models on top of a database. It includes: -- models: each model maps to a database table with typed columns -- relationships: joins between models (ONE_TO_ONE, ONE_TO_MANY, MANY_TO_ONE, MANY_TO_MANY) -- views: named SQL queries - -## Your workflow - -1. **Ask for a database connection string** if none has been provided yet. - Return an AgentQuestion asking the user for their connection string. - Example formats: - - PostgreSQL: postgresql://user:password@host:5432/database - - MySQL: mysql+pymysql://user:password@host:3306/database - - DuckDB: duckdb:///path/to/file.db - -2. **Connect to the database** using `connect_to_database(connection_string)`. - -3. **Explore the schema**: - - Call `list_tables()` to discover all tables. - - For each table, call `get_column_info(table)` to get columns, types, PKs, and FKs. - - For ambiguous columns, call `get_column_stats(table, column)` or - `get_sample_data(table)` to understand semantics. - -4. **Build the MDL manifest**: - - Set `catalog` and `schema` from the database connection (e.g., "wren" and "public"). - - Set `dataSource` to the uppercase datasource type (e.g., "POSTGRES", "MYSQL", "DUCKDB"). - - For each table, create an MDLModel: - - `name`: PascalCase version of the table name (e.g., "order_items" → "OrderItems") - - `tableReference`: use the actual catalog/schema/table from the DB - - `columns`: map each column with its SQL type - - `primaryKey`: set if the table has a single-column PK - - For each foreign key constraint, create an MDLRelationship: - - The FK side is MANY_TO_ONE (many rows reference one parent) - - The referenced side gets a ONE_TO_MANY back-reference if relevant - - `condition`: use model column names (PascalCase model, original column name) - e.g., `"OrderItems.order_id = Orders.id"` - -5. **Validate the manifest** using `validate_mdl(mdl_dict)` before returning. - Fix any reported errors and re-validate. - -6. **Return the final MDLManifest** as the structured result. - -## MDL naming conventions -- Model names: PascalCase (Orders, Customers, LineItems) -- Column names: preserve original DB column names (do NOT convert to camelCase) -- Relationship names: descriptive, e.g., "OrdersToCustomers" - -## Important rules -- Every model MUST have exactly one of: tableReference, refSql, or baseObject. -- Relationship `models` array must contain exactly 2 model names. -- Calculated columns (`isCalculated: true`) require an `expression`. -- Do not invent columns or relationships not present in the actual DB schema. -- If a table has no FK constraints, do not create relationships for it unless - you can infer them from column naming conventions (e.g., `customer_id` → Customers). -""" diff --git a/wren-agent/src/wren_agent/skills.py b/wren-agent/src/wren_agent/skills.py deleted file mode 100644 index 918111bcc..000000000 --- a/wren-agent/src/wren_agent/skills.py +++ /dev/null @@ -1,307 +0,0 @@ -"""Skill store — load domain-specific knowledge snippets and inject them into the agent. - -Skills are static Markdown/text documents that describe conventions, patterns, -or domain knowledge. They are injected into the agent as ``instructions`` on -each run, with two selection mechanisms: - -- **Tag filter** — ``by_tags(["postgres", "bigquery"])`` - Returns a new SkillStore with only skills whose tags overlap with the given set. - -- **Semantic search** — ``search("nested JSON columns", top_k=3)`` - Ranks all skills by TF-IDF cosine similarity to the query and returns the top-k. - -- **Combined** — ``select(query="...", tags=["postgres"], top_k=3)`` - Applies tag filter first, then semantic ranking. - -``MDLAgent`` calls ``select(query=)`` automatically on every run, -so only relevant skills are injected rather than the entire store. - -Example:: - - store = SkillStore() - store.load_dir("skills/") - store.add("ecommerce", - "Order tables usually have customer_id FK to customers.", - tags=["ecommerce", "postgres"]) - store.add("bigquery_nested", - "Use STRUCT for nested columns in BigQuery.", - tags=["bigquery"]) - - agent = MDLAgent(skill_store=store, max_skills=3) - # On each run, agent auto-calls store.select(query=message, top_k=3) -""" - -from __future__ import annotations - -import math -import re -from collections import Counter -from dataclasses import dataclass, field -from pathlib import Path - - -@dataclass -class Skill: - """A single named knowledge snippet.""" - - name: str - content: str - tags: list[str] = field(default_factory=list) - - -# --------------------------------------------------------------------------- -# TF-IDF helpers (pure Python, no extra dependencies) -# --------------------------------------------------------------------------- - - -def _tokenize(text: str) -> list[str]: - return re.findall(r"[a-z0-9]+", text.lower()) - - -def _tfidf_cosine(query: str, skills: list[Skill]) -> list[float]: - """Return a cosine similarity score for each skill against the query.""" - if not skills: - return [] - - docs = [_tokenize(s.name + " " + s.content) for s in skills] - query_tokens = _tokenize(query) - - if not query_tokens: - return [0.0] * len(skills) - - # Document frequency across the skill corpus - N = len(docs) - df: Counter[str] = Counter() - for doc in docs: - for term in set(doc): - df[term] += 1 - - all_query_terms = set(query_tokens) - idf = { - t: math.log((N + 1) / (df.get(t, 0) + 1)) - for t in all_query_terms - } - - # Query TF-IDF vector - query_tf = Counter(query_tokens) - query_vec = {t: query_tf[t] * idf[t] for t in query_tokens} - query_norm = math.sqrt(sum(v * v for v in query_vec.values())) - - scores: list[float] = [] - for doc in docs: - doc_tf = Counter(doc) - shared = all_query_terms & set(doc) - if not shared or query_norm == 0: - scores.append(0.0) - continue - dot = sum(query_vec[t] * doc_tf[t] * idf[t] for t in shared) - doc_vec_vals = [ - doc_tf[t] * idf.get(t, math.log((N + 1) / 1)) - for t in set(doc) - ] - doc_norm = math.sqrt(sum(v * v for v in doc_vec_vals)) - scores.append(dot / (query_norm * doc_norm) if doc_norm > 0 else 0.0) - - return scores - - -# --------------------------------------------------------------------------- -# SkillStore -# --------------------------------------------------------------------------- - - -class SkillStore: - """Collection of skills with tag filtering and semantic search.""" - - def __init__(self, _skills: dict[str, Skill] | None = None) -> None: - self._skills: dict[str, Skill] = _skills or {} - - # ------------------------------------------------------------------ - # Loading / adding - # ------------------------------------------------------------------ - - def add( - self, - name: str, - content: str, - tags: list[str] | None = None, - ) -> None: - """Add a skill from an in-memory string. - - Args: - name: Unique identifier for this skill. - content: Markdown or plain-text knowledge content. - tags: Optional list of tags for ``by_tags()`` filtering, - e.g. ``["postgres", "schema"]``. - """ - self._skills[name] = Skill( - name=name, content=content.strip(), tags=tags or [] - ) - - def load_file( - self, - path: str | Path, - tags: list[str] | None = None, - ) -> None: - """Load a single .md or .txt file as a skill. - - The skill name is the file stem (e.g., ``bigquery_mdl.md`` → ``bigquery_mdl``). - - Args: - path: Path to the file. - tags: Tags to attach to this skill. - """ - p = Path(path) - if not p.exists(): - raise FileNotFoundError(f"Skill file not found: {p}") - self.add(p.stem, p.read_text(encoding="utf-8"), tags=tags) - - def load_dir( - self, - path: str | Path, - tags: list[str] | None = None, - ) -> None: - """Load all .md and .txt files in a directory as skills. - - Args: - path: Directory to scan. - tags: Tags applied to every loaded skill. - """ - p = Path(path) - if not p.is_dir(): - raise NotADirectoryError(f"Not a directory: {p}") - for f in sorted(p.glob("*.md")) + sorted(p.glob("*.txt")): - self.load_file(f, tags=tags) - - # ------------------------------------------------------------------ - # Basic querying - # ------------------------------------------------------------------ - - def get(self, name: str) -> Skill | None: - return self._skills.get(name) - - def list_names(self) -> list[str]: - return list(self._skills.keys()) - - def __len__(self) -> int: - return len(self._skills) - - # ------------------------------------------------------------------ - # Selection - # ------------------------------------------------------------------ - - def by_tags( - self, - tags: list[str], - match: str = "any", - ) -> SkillStore: - """Return a new SkillStore containing only skills whose tags overlap. - - Args: - tags: Tags to filter by. - match: ``"any"`` — keep skills with at least one matching tag - (union). ``"all"`` — keep only skills that have every - given tag (intersection). - - Returns: - A new :class:`SkillStore` (may be empty if no skills match). - """ - tag_set = {t.lower() for t in tags} - if match == "all": - filtered = { - name: s - for name, s in self._skills.items() - if tag_set.issubset({t.lower() for t in s.tags}) - } - else: # "any" - filtered = { - name: s - for name, s in self._skills.items() - if tag_set & {t.lower() for t in s.tags} - } - return SkillStore(filtered) - - def search(self, query: str, top_k: int = 5) -> SkillStore: - """Rank skills by TF-IDF cosine similarity to *query* and return top-k. - - Uses pure-Python TF-IDF (no dependencies beyond the stdlib). - - Args: - query: Text to compare against (typically the user's message). - top_k: Maximum number of skills to return. Skills with a score - of 0 are excluded even if fewer than *top_k* remain. - - Returns: - A new :class:`SkillStore` ordered by relevance (best first). - """ - if not self._skills: - return SkillStore() - - skills = list(self._skills.values()) - scores = _tfidf_cosine(query, skills) - - ranked = sorted(zip(scores, skills), key=lambda x: x[0], reverse=True) - selected = { - s.name: s - for score, s in ranked[:top_k] - if score > 0 - } - return SkillStore(selected) - - def select( - self, - query: str | None = None, - tags: list[str] | None = None, - top_k: int | None = None, - ) -> SkillStore: - """Combined selection: tag filter → semantic ranking. - - This is what ``MDLAgent`` calls automatically on every ``run()``. - - Steps: - - 1. If *tags* given, apply ``by_tags(tags)`` to narrow the pool. - 2. If *query* given, apply ``search(query, top_k)`` on the remaining pool. - 3. If neither, return all skills (optionally limited by *top_k*). - - Args: - query: Current user message — used for semantic ranking. - tags: Tags to pre-filter by before semantic ranking. - top_k: Maximum number of skills to return. - - Returns: - A new :class:`SkillStore` with the selected subset. - """ - result: SkillStore = self - - if tags: - result = result.by_tags(tags) - - if query: - k = top_k if top_k is not None else len(result) - result = result.search(query, top_k=k) - elif top_k is not None: - # No query — take first top_k in insertion order - items = dict(list(result._skills.items())[:top_k]) - result = SkillStore(items) - - return result - - # ------------------------------------------------------------------ - # Injection - # ------------------------------------------------------------------ - - def as_instructions(self) -> str | None: - """Concatenate selected skills into a single instructions string. - - Returns ``None`` if the store is empty (no instructions injected). - """ - if not self._skills: - return None - parts = ["## Relevant Skills\n"] - for skill in self._skills.values(): - tag_line = ( - f"*Tags: {', '.join(skill.tags)}*\n\n" if skill.tags else "" - ) - parts.append(f"### {skill.name}\n\n{tag_line}{skill.content}\n") - return "\n".join(parts) diff --git a/wren-agent/src/wren_agent/tools/__init__.py b/wren-agent/src/wren_agent/tools/__init__.py deleted file mode 100644 index 5fe1c3aa9..000000000 --- a/wren-agent/src/wren_agent/tools/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Wren Agent tools package.""" diff --git a/wren-agent/src/wren_agent/tools/db.py b/wren-agent/src/wren_agent/tools/db.py deleted file mode 100644 index ed95e5d80..000000000 --- a/wren-agent/src/wren_agent/tools/db.py +++ /dev/null @@ -1,166 +0,0 @@ -"""Database exploration tools for the MDL agent.""" - -from __future__ import annotations - -from typing import Any - -import sqlalchemy as sa -from pydantic_ai import RunContext -from sqlalchemy import inspect, text - -from wren_agent.deps import AgentDeps - - -def _require_engine(ctx: RunContext[AgentDeps]) -> sa.Engine | str: - """Return the engine or an error string if not connected.""" - if ctx.deps.engine is None: - return "No database connection. Please call connect_to_database first." - return ctx.deps.engine - - -async def connect_to_database( - ctx: RunContext[AgentDeps], connection_string: str -) -> str: - """Establish a database connection using a SQLAlchemy connection string. - - Supported schemes: postgresql://, mysql+pymysql://, duckdb:///, and others. - Example: postgresql://user:pass@localhost:5432/mydb - - Returns a success message listing available tables, or an error string. - """ - try: - engine = sa.create_engine(connection_string) - # Eagerly verify connectivity - with engine.connect() as conn: - conn.execute(text("SELECT 1")) - ctx.deps.engine = engine - - insp = inspect(engine) - tables = insp.get_table_names() - schema_tables = insp.get_table_names(schema=None) - all_tables = sorted(set(tables + schema_tables)) - return ( - f"Connected successfully. Found {len(all_tables)} table(s): " - + ", ".join(all_tables[:20]) - + ("..." if len(all_tables) > 20 else "") - ) - except Exception as e: - return f"Connection failed: {e}" - - -async def list_tables(ctx: RunContext[AgentDeps]) -> list[str] | str: - """List all user tables in the connected database.""" - engine = _require_engine(ctx) - if isinstance(engine, str): - return engine - try: - insp = inspect(engine) - return sorted(insp.get_table_names()) - except Exception as e: - return f"Error listing tables: {e}" - - -async def get_column_info( - ctx: RunContext[AgentDeps], table: str -) -> list[dict[str, Any]] | str: - """Get column metadata for a table: name, type, nullable, primary key, foreign keys. - - Returns a list of dicts, one per column. - """ - engine = _require_engine(ctx) - if isinstance(engine, str): - return engine - try: - insp = inspect(engine) - columns = insp.get_columns(table) - pk_constraint = insp.get_pk_constraint(table) - pk_cols = set(pk_constraint.get("constrained_columns", [])) - fk_list = insp.get_foreign_keys(table) - - fk_map: dict[str, dict[str, str]] = {} - for fk in fk_list: - for local_col, ref_col in zip( - fk["constrained_columns"], fk["referred_columns"] - ): - fk_map[local_col] = { - "referred_table": fk["referred_table"], - "referred_column": ref_col, - } - - result = [] - for col in columns: - col_name = col["name"] - info: dict[str, Any] = { - "name": col_name, - "type": str(col["type"]), - "nullable": col.get("nullable", True), - "primary_key": col_name in pk_cols, - } - if col_name in fk_map: - info["foreign_key"] = fk_map[col_name] - result.append(info) - return result - except Exception as e: - return f"Error getting column info for '{table}': {e}" - - -async def get_column_stats( - ctx: RunContext[AgentDeps], table: str, column: str -) -> dict[str, Any] | str: - """Get basic statistics for a column: distinct count, null count, min, max. - - Useful for inferring column semantics and data quality. - """ - engine = _require_engine(ctx) - if isinstance(engine, str): - return engine - try: - # Use quoted identifiers to avoid SQL injection via table/column names - col_id = sa.column(column) - tbl = sa.table(table, col_id) - stmt = sa.select( - sa.func.count(sa.func.distinct(col_id)).label("distinct_count"), - (sa.func.count() - sa.func.count(col_id)).label("null_count"), - sa.func.min(col_id).label("min_val"), - sa.func.max(col_id).label("max_val"), - ).select_from(tbl) - with engine.connect() as conn: - row = conn.execute(stmt).one() - return { - "distinct_count": row.distinct_count, - "null_count": row.null_count, - "min": str(row.min_val) if row.min_val is not None else None, - "max": str(row.max_val) if row.max_val is not None else None, - } - except Exception as e: - return f"Error getting stats for '{table}.{column}': {e}" - - -async def get_sample_data( - ctx: RunContext[AgentDeps], table: str, limit: int = 5 -) -> list[dict[str, Any]] | str: - """Fetch sample rows from a table to help infer column semantics. - - Args: - table: Table name. - limit: Number of rows to return (default 5, max 20). - """ - engine = _require_engine(ctx) - if isinstance(engine, str): - return engine - limit = min(limit, 20) - try: - tbl = sa.table(table) - stmt = sa.select(sa.text("*")).select_from(tbl).limit(limit) - with engine.connect() as conn: - result = conn.execute(stmt) - keys = list(result.keys()) - rows = [dict(zip(keys, row)) for row in result.fetchall()] - # Coerce non-serializable values to strings - return [ - {k: (str(v) if not isinstance(v, (str, int, float, bool, type(None))) else v) - for k, v in row.items()} - for row in rows - ] - except Exception as e: - return f"Error getting sample data for '{table}': {e}" diff --git a/wren-agent/src/wren_agent/tools/memory.py b/wren-agent/src/wren_agent/tools/memory.py deleted file mode 100644 index edd195f66..000000000 --- a/wren-agent/src/wren_agent/tools/memory.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Memory tools exposed to the agent.""" - -from __future__ import annotations - -from pydantic_ai import RunContext - -from wren_agent.deps import AgentDeps - - -async def tool_remember( - ctx: RunContext[AgentDeps], key: str, value: str, tags: list[str] | None = None -) -> str: - """Store a piece of information in persistent memory for future sessions. - - Use this for facts that are likely to be useful later, such as: - - Database schema summaries ("key: tpch_schema, value: has orders, lineitem, …") - - User preferences ("key: naming_pref, value: keep original column names") - - Previously generated MDL fragments - - Args: - key: Short identifier, e.g. "tpch_schema" or "user_prefers_snake_case". - value: The text to remember. - tags: Optional tags for filtering, e.g. ["schema", "tpch"]. - """ - if ctx.deps.memory is None: - return "Memory is not enabled for this session." - ctx.deps.memory.remember(key, value, tags=tags) - return f"Remembered '{key}'." - - -async def tool_recall(ctx: RunContext[AgentDeps], key: str) -> str: - """Retrieve a previously stored memory entry by key. - - Returns the stored value, or a 'not found' message. - """ - if ctx.deps.memory is None: - return "Memory is not enabled for this session." - value = ctx.deps.memory.recall(key) - if value is None: - return f"No memory found for key '{key}'." - return value - - -async def tool_search_memory(ctx: RunContext[AgentDeps], query: str) -> str: - """Search memory entries whose key or value contains the query string. - - Useful for finding relevant past observations before exploring the DB. - Returns a formatted summary of matching entries. - """ - if ctx.deps.memory is None: - return "Memory is not enabled for this session." - results = ctx.deps.memory.search(query) - if not results: - return f"No memory entries matching '{query}'." - lines = [f"Found {len(results)} matching memory entry(ies):"] - for e in results: - tag_str = f" [{', '.join(e.tags)}]" if e.tags else "" - lines.append(f" [{e.key}]{tag_str}: {e.value[:200]}") - return "\n".join(lines) - - -async def tool_list_memory_keys(ctx: RunContext[AgentDeps]) -> str: - """List all keys currently stored in memory. - - Call this at the start of a session to check if relevant prior knowledge exists. - """ - if ctx.deps.memory is None: - return "Memory is not enabled for this session." - keys = ctx.deps.memory.list_keys() - if not keys: - return "Memory is empty." - return "Memory keys: " + ", ".join(keys) - - -async def tool_forget(ctx: RunContext[AgentDeps], key: str) -> str: - """Delete a memory entry by key. - - Use when a stored fact is outdated or incorrect. - """ - if ctx.deps.memory is None: - return "Memory is not enabled for this session." - existed = ctx.deps.memory.forget(key) - return f"Forgot '{key}'." if existed else f"Key '{key}' was not in memory." diff --git a/wren-agent/src/wren_agent/tools/validate.py b/wren-agent/src/wren_agent/tools/validate.py deleted file mode 100644 index cc234a7da..000000000 --- a/wren-agent/src/wren_agent/tools/validate.py +++ /dev/null @@ -1,86 +0,0 @@ -"""MDL validation tool: JSON Schema + optional ibis-server dry-plan.""" - -from __future__ import annotations - -import base64 -import json -from typing import Any - -import httpx -import jsonschema -from pydantic_ai import RunContext - -from wren_agent.deps import AgentDeps - -_MDL_SCHEMA_URL = ( - "https://raw.githubusercontent.com/Canner/WrenAI/main/wren-mdl/mdl.schema.json" -) - -# Module-level cache: fetched once per process -_cached_schema: dict[str, Any] | None = None - - -def _get_mdl_schema() -> dict[str, Any]: - global _cached_schema - if _cached_schema is None: - response = httpx.get(_MDL_SCHEMA_URL, timeout=10.0) - response.raise_for_status() - _cached_schema = response.json() - return _cached_schema - - -def _dict_to_base64(data: dict[str, Any]) -> str: - return base64.b64encode(json.dumps(data).encode()).decode() - - -async def validate_mdl( - ctx: RunContext[AgentDeps], mdl: dict[str, Any] -) -> str: - """Validate an MDL manifest dict. - - Performs two checks: - 1. JSON Schema validation against the official Wren MDL schema. - 2. (Optional) Dry-plan validation via ibis-server if WREN_ENGINE_ENDPOINT is set. - - Returns a string describing the validation result. - """ - # --- Step 1: JSON Schema validation --- - try: - schema = _get_mdl_schema() - except Exception as e: - return f"Warning: Could not fetch MDL schema for validation: {e}. Skipping schema validation." - - errors = list( - jsonschema.Draft202012Validator(schema).iter_errors(mdl) - ) - if errors: - messages = "; ".join( - f"{'.'.join(str(p) for p in e.absolute_path) or ''}: {e.message}" - for e in errors[:5] - ) - return f"JSON Schema validation failed ({len(errors)} error(s)): {messages}" - - # --- Step 2: ibis-server dry-plan (optional) --- - ibis_url = ctx.deps.ibis_server_url - if not ibis_url: - return "JSON Schema validation passed. (Skipping dry-plan: WREN_ENGINE_ENDPOINT not set)" - - try: - manifest_str = _dict_to_base64(mdl) - async with httpx.AsyncClient(timeout=30.0) as client: - resp = await client.post( - f"{ibis_url.rstrip('/')}/v3/connector/dry-plan", - json={"manifest_str": manifest_str, "sql": "SELECT 1"}, - headers={"x-wren-fallback_disable": "true"}, - ) - if resp.status_code == 200: - return "MDL validation passed (JSON Schema + dry-plan)." - else: - return ( - f"JSON Schema validation passed, but dry-plan failed " - f"(HTTP {resp.status_code}): {resp.text[:300]}" - ) - except Exception as e: - return ( - f"JSON Schema validation passed, but dry-plan request failed: {e}" - ) diff --git a/wren-agent/tests/test_agent.py b/wren-agent/tests/test_agent.py deleted file mode 100644 index 2bc91b8e6..000000000 --- a/wren-agent/tests/test_agent.py +++ /dev/null @@ -1,627 +0,0 @@ -"""Tests for wren_agent.""" - -from __future__ import annotations - -import json -import tempfile -from pathlib import Path -from typing import Any -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest - -from wren_agent.memory import MemoryStore -from wren_agent.skills import Skill, SkillStore - -from wren_agent.models import ( - AgentQuestion, - MDLColumn, - MDLManifest, - MDLModel, - MDLRelationship, - TableReference, -) - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -JAFFLE_MDL: dict[str, Any] = { - "catalog": "wren", - "schema": "public", - "models": [ - { - "name": "orders", - "tableReference": { - "catalog": "jaffle_shop", - "schema": "main", - "table": "orders", - }, - "columns": [ - {"name": "amount", "type": "double"}, - {"name": "id", "type": "integer"}, - {"name": "status", "type": "varchar"}, - ], - } - ], -} - -LOCAL_MDL: dict[str, Any] = { - "catalog": "wrenai", - "schema": "public", - "models": [ - { - "name": "Customer", - "tableReference": {"table": "s3://bucket/customer.parquet"}, - "columns": [ - {"name": "custkey", "type": "integer"}, - { - "name": "orders", - "type": "Orders", - "relationship": "CustomerOrders", - }, - { - "name": "orders_key", - "type": "varchar", - "isCalculated": True, - "expression": "orders.orderkey", - }, - ], - "primaryKey": "custkey", - }, - { - "name": "Orders", - "tableReference": {"table": "s3://bucket/orders.parquet"}, - "columns": [ - {"name": "orderkey", "type": "integer"}, - {"name": "custkey", "type": "integer"}, - ], - "primaryKey": "orderkey", - }, - ], - "relationships": [ - { - "name": "CustomerOrders", - "models": ["Customer", "Orders"], - "joinType": "ONE_TO_MANY", - "condition": "Customer.custkey = Orders.custkey", - } - ], -} - - -# --------------------------------------------------------------------------- -# Model parsing tests -# --------------------------------------------------------------------------- - - -class TestMDLManifestParse: - def test_parse_jaffle(self) -> None: - manifest = MDLManifest.model_validate(JAFFLE_MDL) - assert manifest.catalog == "wren" - assert manifest.schema_name == "public" - assert manifest.models is not None - assert len(manifest.models) == 1 - assert manifest.models[0].name == "orders" - assert len(manifest.models[0].columns) == 3 - - def test_parse_local_mdl_with_relationships(self) -> None: - manifest = MDLManifest.model_validate(LOCAL_MDL) - assert manifest.catalog == "wrenai" - assert manifest.relationships is not None - assert len(manifest.relationships) == 1 - rel = manifest.relationships[0] - assert rel.name == "CustomerOrders" - assert rel.joinType == "ONE_TO_MANY" - assert rel.models == ["Customer", "Orders"] - - def test_parse_calculated_column(self) -> None: - manifest = MDLManifest.model_validate(LOCAL_MDL) - assert manifest.models is not None - customer = next(m for m in manifest.models if m.name == "Customer") - calc_col = next(c for c in customer.columns if c.name == "orders_key") - assert calc_col.isCalculated is True - assert calc_col.expression == "orders.orderkey" - - def test_to_mdl_dict_uses_schema_key(self) -> None: - manifest = MDLManifest.model_validate(JAFFLE_MDL) - d = manifest.to_mdl_dict() - assert "schema" in d - assert "schema_name" not in d - - def test_model_requires_table_source(self) -> None: - with pytest.raises(Exception): - MDLModel(name="NoBacking") # no tableReference, refSql, or baseObject - - def test_model_rejects_multiple_sources(self) -> None: - with pytest.raises(Exception): - MDLModel( - name="Ambiguous", - tableReference=TableReference(table="t"), - refSql="SELECT 1", - ) - - def test_datasource_valid(self) -> None: - manifest = MDLManifest.model_validate({ - **JAFFLE_MDL, - "dataSource": "POSTGRES", - }) - assert manifest.dataSource == "POSTGRES" - - def test_datasource_invalid(self) -> None: - with pytest.raises(Exception): - MDLManifest.model_validate({ - **JAFFLE_MDL, - "dataSource": "NONEXISTENT_DB", - }) - - def test_relationship_requires_two_models(self) -> None: - with pytest.raises(Exception): - MDLRelationship( - name="Bad", - models=["OnlyOne"], - joinType="ONE_TO_MANY", - condition="x = y", - ) - - -# --------------------------------------------------------------------------- -# validate_mdl tool tests -# --------------------------------------------------------------------------- - - -class TestValidateMdl: - @pytest.mark.asyncio - async def test_valid_mdl_passes_schema(self) -> None: - from unittest.mock import patch - - import httpx - - from wren_agent.tools.validate import validate_mdl, _cached_schema - import wren_agent.tools.validate as validate_module - - # Inject a minimally correct schema for testing - minimal_schema = { - "$schema": "https://json-schema.org/draft/2020-12/schema", - "type": "object", - "properties": { - "catalog": {"type": "string"}, - "schema": {"type": "string"}, - }, - "required": ["catalog", "schema"], - } - validate_module._cached_schema = minimal_schema - - deps_mock = MagicMock() - deps_mock.ibis_server_url = None - ctx = MagicMock() - ctx.deps = deps_mock - - result = await validate_mdl(ctx, {"catalog": "wren", "schema": "public"}) - assert "passed" in result.lower() - - # restore - validate_module._cached_schema = None - - @pytest.mark.asyncio - async def test_invalid_mdl_fails_schema(self) -> None: - import wren_agent.tools.validate as validate_module - from wren_agent.tools.validate import validate_mdl - - minimal_schema = { - "$schema": "https://json-schema.org/draft/2020-12/schema", - "type": "object", - "properties": { - "catalog": {"type": "string"}, - "schema": {"type": "string"}, - }, - "required": ["catalog", "schema"], - } - validate_module._cached_schema = minimal_schema - - deps_mock = MagicMock() - deps_mock.ibis_server_url = None - ctx = MagicMock() - ctx.deps = deps_mock - - result = await validate_mdl(ctx, {"catalog": "wren"}) # missing "schema" - assert "failed" in result.lower() - - validate_module._cached_schema = None - - -# --------------------------------------------------------------------------- -# AgentQuestion model test -# --------------------------------------------------------------------------- - - -class TestAgentQuestion: - def test_question_is_string(self) -> None: - q = AgentQuestion(question="What is your connection string?") - assert q.question == "What is your connection string?" - - -# --------------------------------------------------------------------------- -# SkillStore tests -# --------------------------------------------------------------------------- - - -class TestSkillStore: - def test_add_and_get(self) -> None: - store = SkillStore() - store.add("tip1", "Always use PascalCase for model names.") - assert store.get("tip1") is not None - assert store.get("tip1").content == "Always use PascalCase for model names." - - def test_len(self) -> None: - store = SkillStore() - assert len(store) == 0 - store.add("a", "content a") - store.add("b", "content b") - assert len(store) == 2 - - def test_list_names(self) -> None: - store = SkillStore() - store.add("x", "...") - store.add("y", "...") - assert set(store.list_names()) == {"x", "y"} - - def test_as_instructions_empty(self) -> None: - assert SkillStore().as_instructions() is None - - def test_as_instructions_contains_skill(self) -> None: - store = SkillStore() - store.add("ecommerce", "Order tables reference customers via customer_id.") - instructions = store.as_instructions() - assert instructions is not None - assert "ecommerce" in instructions - assert "Order tables" in instructions - assert "## Relevant Skills" in instructions - - def test_load_file(self, tmp_path: Path) -> None: - skill_file = tmp_path / "bigquery.md" - skill_file.write_text("Use STRUCT for nested columns.", encoding="utf-8") - store = SkillStore() - store.load_file(skill_file) - assert store.get("bigquery") is not None - assert "STRUCT" in store.get("bigquery").content - - def test_load_dir(self, tmp_path: Path) -> None: - (tmp_path / "skill_a.md").write_text("Skill A content", encoding="utf-8") - (tmp_path / "skill_b.txt").write_text("Skill B content", encoding="utf-8") - (tmp_path / "ignored.json").write_text("{}", encoding="utf-8") - store = SkillStore() - store.load_dir(tmp_path) - assert len(store) == 2 - assert store.get("skill_a") is not None - assert store.get("skill_b") is not None - - def test_load_file_not_found(self) -> None: - with pytest.raises(FileNotFoundError): - SkillStore().load_file("/nonexistent/path/skill.md") - - # ---- tags ---- - - def test_add_with_tags(self) -> None: - store = SkillStore() - store.add("pg_tip", "Use bigint for IDs.", tags=["postgres", "schema"]) - skill = store.get("pg_tip") - assert skill is not None - assert "postgres" in skill.tags - - def test_by_tags_any(self) -> None: - store = SkillStore() - store.add("pg", "Postgres tip", tags=["postgres"]) - store.add("bq", "BigQuery tip", tags=["bigquery"]) - store.add("generic", "Generic tip", tags=["schema"]) - result = store.by_tags(["postgres"]) - assert len(result) == 1 - assert result.get("pg") is not None - - def test_by_tags_any_multiple(self) -> None: - store = SkillStore() - store.add("pg", "Postgres tip", tags=["postgres"]) - store.add("bq", "BigQuery tip", tags=["bigquery"]) - store.add("generic", "Generic tip", tags=["schema"]) - result = store.by_tags(["postgres", "bigquery"]) - assert len(result) == 2 - - def test_by_tags_all(self) -> None: - store = SkillStore() - store.add("both", "Has both tags", tags=["postgres", "schema"]) - store.add("one", "Has only postgres", tags=["postgres"]) - result = store.by_tags(["postgres", "schema"], match="all") - assert len(result) == 1 - assert result.get("both") is not None - - def test_by_tags_empty_result(self) -> None: - store = SkillStore() - store.add("pg", "Postgres tip", tags=["postgres"]) - result = store.by_tags(["mysql"]) - assert len(result) == 0 - - def test_by_tags_case_insensitive(self) -> None: - store = SkillStore() - store.add("pg", "Postgres tip", tags=["PostgreSQL"]) - result = store.by_tags(["postgresql"]) - assert len(result) == 1 - - # ---- search ---- - - def test_search_ranks_by_relevance(self) -> None: - store = SkillStore() - store.add("bigquery", "BigQuery tips for nested JSON columns using STRUCT.") - store.add("postgres", "PostgreSQL tips for indexing and constraints.") - store.add("ecommerce", "Ecommerce order tables reference customers.") - result = store.search("BigQuery nested JSON", top_k=1) - assert len(result) == 1 - assert result.get("bigquery") is not None - - def test_search_excludes_zero_scores(self) -> None: - store = SkillStore() - store.add("irrelevant", "Completely unrelated topic about bicycles.") - result = store.search("database schema MDL", top_k=5) - # Might be 0 or 1 depending on overlap; should not crash - assert len(result) <= 1 - - def test_search_empty_store(self) -> None: - result = SkillStore().search("anything") - assert len(result) == 0 - - def test_search_empty_query(self) -> None: - store = SkillStore() - store.add("tip", "something") - result = store.search("", top_k=5) - assert len(result) == 0 # empty query → all scores 0 - - def test_search_respects_top_k(self) -> None: - store = SkillStore() - for i in range(10): - store.add(f"skill_{i}", f"database schema table column model {i}") - result = store.search("database schema", top_k=3) - assert len(result) <= 3 - - # ---- select ---- - - def test_select_tags_only(self) -> None: - store = SkillStore() - store.add("pg", "Postgres tip", tags=["postgres"]) - store.add("bq", "BigQuery tip", tags=["bigquery"]) - result = store.select(tags=["postgres"]) - assert len(result) == 1 - assert result.get("pg") is not None - - def test_select_query_only(self) -> None: - store = SkillStore() - store.add("bigquery", "BigQuery nested STRUCT column model schema.") - store.add("ecommerce", "Order table references customer_id FK.") - result = store.select(query="BigQuery STRUCT nested") - assert result.get("bigquery") is not None - - def test_select_tags_then_query(self) -> None: - store = SkillStore() - store.add("pg_idx", "PostgreSQL index strategies.", tags=["postgres"]) - store.add("pg_fk", "PostgreSQL FK naming conventions.", tags=["postgres"]) - store.add("bq", "BigQuery tips.", tags=["bigquery"]) - result = store.select(query="FK naming", tags=["postgres"], top_k=1) - assert len(result) == 1 - assert result.get("pg_fk") is not None - - def test_select_no_args_returns_all(self) -> None: - store = SkillStore() - store.add("a", "content a") - store.add("b", "content b") - result = store.select() - assert len(result) == 2 - - def test_select_top_k_no_query(self) -> None: - store = SkillStore() - for i in range(5): - store.add(f"skill_{i}", f"skill content {i}") - result = store.select(top_k=3) - assert len(result) == 3 - - def test_as_instructions_shows_tags(self) -> None: - store = SkillStore() - store.add("pg_tip", "Use bigint for IDs.", tags=["postgres", "schema"]) - instructions = store.as_instructions() - assert instructions is not None - assert "postgres" in instructions - assert "schema" in instructions - - -# --------------------------------------------------------------------------- -# MemoryStore tests -# --------------------------------------------------------------------------- - - -class TestMemoryStore: - def test_remember_and_recall(self) -> None: - mem = MemoryStore() - mem.remember("db_schema", "orders table has 10 columns") - assert mem.recall("db_schema") == "orders table has 10 columns" - - def test_recall_missing(self) -> None: - mem = MemoryStore() - assert mem.recall("nonexistent") is None - - def test_overwrite(self) -> None: - mem = MemoryStore() - mem.remember("key", "old value") - mem.remember("key", "new value") - assert mem.recall("key") == "new value" - - def test_overwrite_false_raises(self) -> None: - mem = MemoryStore() - mem.remember("key", "value") - with pytest.raises(ValueError, match="already exists"): - mem.remember("key", "other", overwrite=False) - - def test_forget(self) -> None: - mem = MemoryStore() - mem.remember("temp", "data") - assert mem.forget("temp") is True - assert mem.recall("temp") is None - assert mem.forget("temp") is False # already gone - - def test_search(self) -> None: - mem = MemoryStore() - mem.remember("tpch_tables", "orders, customer, lineitem") - mem.remember("user_pref", "prefer snake_case columns") - results = mem.search("tpch") - assert len(results) == 1 - assert results[0].key == "tpch_tables" - - def test_search_no_results(self) -> None: - mem = MemoryStore() - mem.remember("foo", "bar") - assert mem.search("zzz_no_match") == [] - - def test_list_keys(self) -> None: - mem = MemoryStore() - mem.remember("a", "1") - mem.remember("b", "2") - assert set(mem.list_keys()) == {"a", "b"} - - def test_tags(self) -> None: - mem = MemoryStore() - mem.remember("schema_x", "...", tags=["schema", "postgres"]) - entry = mem.get_entry("schema_x") - assert entry is not None - assert "schema" in entry.tags - - def test_persist_to_file(self, tmp_path: Path) -> None: - path = tmp_path / "memory.json" - mem = MemoryStore(persist_path=path) - mem.remember("persisted_key", "persisted value") - assert path.exists() - - # Load in a new instance - mem2 = MemoryStore(persist_path=path) - assert mem2.recall("persisted_key") == "persisted value" - - def test_persist_corrupted_file(self, tmp_path: Path) -> None: - path = tmp_path / "memory.json" - path.write_text("not valid json", encoding="utf-8") - # Should not raise; start fresh - mem = MemoryStore(persist_path=path) - assert len(mem) == 0 - - -# --------------------------------------------------------------------------- -# Memory tools tests -# --------------------------------------------------------------------------- - - -class TestMemoryTools: - def _make_ctx(self, memory: MemoryStore | None = None) -> MagicMock: - ctx = MagicMock() - from wren_agent.deps import AgentDeps - ctx.deps = AgentDeps(memory=memory) - return ctx - - @pytest.mark.asyncio - async def test_remember_and_recall_tools(self) -> None: - from wren_agent.tools.memory import tool_recall, tool_remember - - mem = MemoryStore() - ctx = self._make_ctx(mem) - - result = await tool_remember(ctx, "k", "v") - assert "Remembered" in result - - result = await tool_recall(ctx, "k") - assert result == "v" - - @pytest.mark.asyncio - async def test_recall_missing_key(self) -> None: - from wren_agent.tools.memory import tool_recall - - mem = MemoryStore() - ctx = self._make_ctx(mem) - result = await tool_recall(ctx, "missing") - assert "no memory found" in result.lower() - - @pytest.mark.asyncio - async def test_tools_disabled_without_memory(self) -> None: - from wren_agent.tools.memory import tool_recall, tool_remember - - ctx = self._make_ctx(memory=None) - assert "not enabled" in await tool_remember(ctx, "k", "v") - assert "not enabled" in await tool_recall(ctx, "k") - - @pytest.mark.asyncio - async def test_search_and_list_tools(self) -> None: - from wren_agent.tools.memory import tool_list_memory_keys, tool_search_memory - - mem = MemoryStore() - mem.remember("tpch_schema", "orders lineitem customer") - ctx = self._make_ctx(mem) - - search_result = await tool_search_memory(ctx, "tpch") - assert "tpch_schema" in search_result - - list_result = await tool_list_memory_keys(ctx) - assert "tpch_schema" in list_result - - @pytest.mark.asyncio - async def test_forget_tool(self) -> None: - from wren_agent.tools.memory import tool_forget, tool_remember - - mem = MemoryStore() - ctx = self._make_ctx(mem) - await tool_remember(ctx, "del_me", "temporary") - result = await tool_forget(ctx, "del_me") - assert "Forgot" in result - assert mem.recall("del_me") is None - - -# --------------------------------------------------------------------------- -# MDLAgent skill selection integration -# --------------------------------------------------------------------------- - - -class TestMDLAgentMaxSkills: - """Test that MDLAgent wires max_skills to SkillStore.select().""" - - def test_default_max_skills(self) -> None: - from wren_agent.agent import MDLAgent - - agent = MDLAgent() - assert agent._max_skills == 5 - - def test_custom_max_skills(self) -> None: - from wren_agent.agent import MDLAgent - - agent = MDLAgent(max_skills=2) - assert agent._max_skills == 2 - - def test_skill_store_stored(self) -> None: - from wren_agent.agent import MDLAgent - - store = SkillStore() - store.add("tip", "some tip content") - agent = MDLAgent(skill_store=store) - assert agent._skill_store is store - - def test_no_skill_store_is_none(self) -> None: - from wren_agent.agent import MDLAgent - - agent = MDLAgent() - assert agent._skill_store is None - - def test_select_called_semantically(self) -> None: - """select() with a query returns relevant subset, not always all skills.""" - store = SkillStore() - store.add("bigquery_struct", "BigQuery STRUCT nested schema model column.", tags=["bigquery"]) - 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{ url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276 }, -] From 73b65f2709f72eb4a457a67716c6309c142e2895 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 11:23:06 +0800 Subject: [PATCH 11/28] feat(mcp-server): enrich MDL schema, add build_mdl_project tool, deploy from file - Expand dto.py with full MDL types (RLAC, CLAC, Metric, EnumDefinition, etc.) - Add build_mdl_project tool to assemble YAML project into target/mdl.json - Change deploy() to accept mdl_file_path instead of inline Manifest object - Update mdl.schema.json with complete relationship, metric, view, and enum schemas - Add sessionProperty $def and column-level access control schema Co-Authored-By: Claude Sonnet 4.6 --- mcp-server/app/dto.py | 180 +++++++++++++--- mcp-server/app/mdl_tools.py | 99 ++++++++- mcp-server/app/wren.py | 25 ++- mcp-server/mdl.schema.json | 420 ++++++++++++++++++++++++++++++------ 4 files changed, 620 insertions(+), 104 deletions(-) diff --git a/mcp-server/app/dto.py b/mcp-server/app/dto.py index e33840d80..6f1030bab 100644 --- a/mcp-server/app/dto.py +++ b/mcp-server/app/dto.py @@ -1,5 +1,39 @@ -from pydantic import BaseModel, ConfigDict -from pydantic.fields import Field +from __future__ import annotations + +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field, model_validator + + +class SessionProperty(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + required: bool + defaultExpr: str | None = None + + +class ColumnLevelAccessControlThreshold(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + value: str + dataType: Literal["NUMERIC", "STRING"] + + +class ColumnLevelAccessControl(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + operator: Literal[ + "EQUALS", + "NOT_EQUALS", + "GREATER_THAN", + "LESS_THAN", + "GREATER_THAN_OR_EQUALS", + "LESS_THAN_OR_EQUALS", + ] + requiredProperties: list[SessionProperty] = Field(min_length=1, max_length=1) + threshold: ColumnLevelAccessControlThreshold | None = None class Column(BaseModel): @@ -7,45 +41,67 @@ class Column(BaseModel): name: str type: str - expression: str = None - isCalculated: bool = False - relationship: str = None - notNull: bool = False - isHidden: bool = False - description: str = None + relationship: str | None = None + isCalculated: bool | None = None + notNull: bool | None = None + expression: str | None = None + isHidden: bool | None = None + columnLevelAccessControl: ColumnLevelAccessControl | None = None + properties: dict[str, Any] | None = None class TableReference(BaseModel): model_config = ConfigDict(populate_by_name=True) - catalog: str = None - mdl_schema: str = Field(alias="schema", default=None) + catalog: str | None = None + mdl_schema: str | None = Field(alias="schema", default=None) table: str +class RowLevelAccessControl(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + requiredProperties: list[SessionProperty] + condition: str + + class Model(BaseModel): model_config = ConfigDict(populate_by_name=True) name: str - tableReference: TableReference = None - refSql: str = None - baseObject: str = None - columns: list[Column] = [] - primaryKey: str = None - cached: bool = False - refreshTime: str = None - properties: dict = None - description: str = None + tableReference: TableReference | None = None + refSql: str | None = None + baseObject: str | None = None + columns: list[Column] | None = None + primaryKey: str | None = None + cached: bool | None = None + refreshTime: str | None = None + rowLevelAccessControls: list[RowLevelAccessControl] | None = None + properties: dict[str, Any] | None = None + + @model_validator(mode="after") + def validate_table_source(self) -> Model: + sources = [ + x + for x in [self.tableReference, self.refSql, self.baseObject] + if x is not None + ] + if len(sources) != 1: + raise ValueError( + "Exactly one of tableReference, refSql, or baseObject must be provided" + ) + return self class Relationship(BaseModel): model_config = ConfigDict(populate_by_name=True) name: str - models: list[str] - joinType: str = Field(alias="joinType", default="") - condition: str = "" - description: str = None + models: list[str] = Field(min_length=2, max_length=2) + joinType: Literal["ONE_TO_ONE", "ONE_TO_MANY", "MANY_TO_ONE", "MANY_TO_MANY"] + condition: str + properties: dict[str, Any] | None = None class View(BaseModel): @@ -53,8 +109,66 @@ class View(BaseModel): name: str statement: str - description: str = None - properties: dict = None + properties: dict[str, Any] | None = None + + +class TimeGrain(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + refColumn: str + dateParts: list[ + Literal["YEAR", "QUARTER", "MONTH", "WEEK", "DAY", "HOUR", "MINUTE", "SECOND"] + ] + + +class Metric(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + baseObject: str + dimension: list[Column] + measure: list[Column] = Field(min_length=1) + timeGrain: list[TimeGrain] | None = None + cached: bool | None = None + refreshTime: str | None = None + properties: dict[str, Any] | None = None + + +class EnumMember(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + value: str | None = None + properties: dict[str, Any] | None = None + + +class EnumDefinition(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + name: str + values: list[EnumMember] + properties: dict[str, Any] | None = None + + +DataSourceType = Literal[ + "BIGQUERY", + "CLICKHOUSE", + "CANNER", + "TRINO", + "MSSQL", + "MYSQL", + "POSTGRES", + "SNOWFLAKE", + "DUCKDB", + "LOCAL_FILE", + "S3_FILE", + "GCS_FILE", + "MINIO_FILE", + "ORACLE", + "ATHENA", + "REDSHIFT", +] class Manifest(BaseModel): @@ -62,15 +176,15 @@ class Manifest(BaseModel): catalog: str = "wren" mdl_schema: str = Field(alias="schema", default="public") - dataSource: str = "" - models: list[Model] = [] - relationships: list[Relationship] = [] - views: list[View] = [] - metrics: list[dict] = [] - enumDefinitions: list[dict] = [] - description: str = None + dataSource: DataSourceType | None = None + sampleDataFolder: str | None = None + models: list[Model] | None = None + relationships: list[Relationship] | None = None + views: list[View] | None = None + metrics: list[Metric] | None = None + enumDefinitions: list[EnumDefinition] | None = None class TableColumns(BaseModel): table_name: str - column_names: list[str] = None + column_names: list[str] | None = None diff --git a/mcp-server/app/mdl_tools.py b/mcp-server/app/mdl_tools.py index 94684ee7c..f9ca2d8a4 100644 --- a/mcp-server/app/mdl_tools.py +++ b/mcp-server/app/mdl_tools.py @@ -15,6 +15,7 @@ - ``mdl_validate_manifest`` — validate an MDL dict (JSON Schema + dry-plan) - ``mdl_save_project`` — save MDL JSON as a dbt-like YAML project directory - ``mdl_load_project`` — load a YAML project directory and return MDL JSON +- ``build_mdl_project`` — assemble a YAML project into ``target/mdl.json`` Requires ``sqlalchemy`` and ``jsonschema`` to be installed:: @@ -583,7 +584,8 @@ def mdl_save_project(mdl: str, project_dir: str) -> str: return ( f"Project saved to: {project_dir}\n\n" f"Files written:\n{summary}\n\n" - f"→ Next: call `deploy(mdl={json.dumps(manifest)})` to deploy to Wren Engine, " + f"→ Next: call `build_mdl_project(project_dir='{project_dir}')` to write `target/mdl.json`, " + f"or call `deploy(mdl_file_path=)` to deploy to Wren Engine, " f"or commit {project_dir} to version control." ) @@ -676,5 +678,98 @@ def mdl_load_project(project_dir: str) -> str: f" Models: {model_count}, Relationships: {rel_count}\n\n" f"MDL JSON:\n```json\n{mdl_json}\n```\n\n" f"→ Next: call `mdl_validate_manifest(mdl=)` to validate, " - f"then `deploy(mdl=)` to deploy." + f"then `build_mdl_project(project_dir='{project_dir}')` to write `target/mdl.json`, " + f"then `deploy(mdl_file_path='{project_dir}/target/mdl.json')` to deploy." + ) + + # ------------------------------------------------------------------ + # 9. Build MDL project — output YAML project as target/mdl.json + # ------------------------------------------------------------------ + + @mcp.tool( + annotations=ToolAnnotations( + title="Build MDL Project", + readOnlyHint=False, + ), + ) + def build_mdl_project(project_dir: str) -> str: + """Assemble a Wren YAML project directory into a single JSON file. + + Reads the YAML files from the project directory (same as `mdl_load_project`) + and writes the assembled MDL JSON to ``/target/mdl.json``. + + Args: + project_dir: Path to the Wren project directory containing + ``wren_project.yml``, ``models/*.yml``, etc. + + Returns: + Path to the generated ``target/mdl.json`` file. + """ + import glob + + project_dir = os.path.expanduser(project_dir) + + if not os.path.isdir(project_dir): + return f"ERROR: Directory not found: {project_dir}" + + project_yml = os.path.join(project_dir, "wren_project.yml") + if not os.path.isfile(project_yml): + return f"ERROR: wren_project.yml not found in {project_dir}" + + with open(project_yml) as f: + project_meta = yaml.safe_load(f) or {} + + manifest: dict[str, Any] = { + "catalog": project_meta.get("catalog", ""), + "schema": project_meta.get("schema", ""), + "dataSource": project_meta.get("dataSource", ""), + "models": [], + "relationships": [], + "views": [], + "metrics": [], + } + + # models/*.yml + model_files = sorted(glob.glob(os.path.join(project_dir, "models", "*.yml"))) + for mf in model_files: + with open(mf) as f: + model = yaml.safe_load(f) + if model: + manifest["models"].append(model) + + # relationships.yml + rels_yml = os.path.join(project_dir, "relationships.yml") + if os.path.isfile(rels_yml): + with open(rels_yml) as f: + data = yaml.safe_load(f) or {} + manifest["relationships"] = data.get("relationships", []) + + # views.yml + views_yml = os.path.join(project_dir, "views.yml") + if os.path.isfile(views_yml): + with open(views_yml) as f: + data = yaml.safe_load(f) or {} + manifest["views"] = data.get("views", []) + + # metrics.yml + metrics_yml = os.path.join(project_dir, "metrics.yml") + if os.path.isfile(metrics_yml): + with open(metrics_yml) as f: + data = yaml.safe_load(f) or {} + manifest["metrics"] = data.get("metrics", []) + + # write to target/mdl.json + target_dir = os.path.join(project_dir, "target") + os.makedirs(target_dir, exist_ok=True) + output_path = os.path.join(target_dir, "mdl.json") + mdl_json = json.dumps(manifest, indent=2, ensure_ascii=False) + with open(output_path, "w") as f: + f.write(mdl_json) + + model_count = len(manifest["models"]) + rel_count = len(manifest["relationships"]) + return ( + f"MDL JSON written to: {output_path}\n" + f" Models: {model_count}, Relationships: {rel_count}\n\n" + f"→ Next: call `deploy(mdl_file_path='{output_path}')` to deploy to Wren Engine." ) diff --git a/mcp-server/app/wren.py b/mcp-server/app/wren.py index 8674d7001..a0b1c365a 100644 --- a/mcp-server/app/wren.py +++ b/mcp-server/app/wren.py @@ -9,11 +9,11 @@ from mcp.types import ToolAnnotations try: - from dto import Manifest, TableColumns + from dto import TableColumns from utils import dict_to_base64_string, json_to_base64_string from mdl_tools import register_mdl_tools except ImportError: - from app.dto import Manifest, TableColumns + from app.dto import TableColumns from app.utils import dict_to_base64_string, json_to_base64_string from app.mdl_tools import register_mdl_tools @@ -210,17 +210,28 @@ async def get_mdl_json_schema() -> str: destructiveHint=True, ), ) -async def deploy(mdl: Manifest) -> str: +async def deploy(mdl_file_path: str) -> str: """ - Deploy the MDL JSON schema to Wren Engine + Deploy the MDL JSON schema to Wren Engine by reading from a JSON file. + + Args: + mdl_file_path: Absolute or relative path to the MDL JSON file (e.g. `target/mdl.json`). + Use `build_mdl_project` to generate this file from a YAML project directory. """ global data_source - mdl_json = mdl.model_dump_json(by_alias=True) - mdl_dict = orjson.loads(mdl_json) + expanded = os.path.expanduser(mdl_file_path) + if not os.path.isfile(expanded): + return f"ERROR: File not found: {mdl_file_path}" + with open(expanded) as f: + mdl_json = f.read() + try: + mdl_dict = orjson.loads(mdl_json) + except Exception as e: + return f"ERROR: Failed to parse JSON from {mdl_file_path}: {e}" data_source = mdl_dict.get("dataSource", "").lower() mdl_base64 = json_to_base64_string(mdl_json) mdl_cache.set_mdl(mdl_base64) - return "MDL deployed successfully" + return f"MDL deployed successfully from {mdl_file_path}" @mcp.tool( diff --git a/mcp-server/mdl.schema.json b/mcp-server/mdl.schema.json index f6ff38035..f99303d29 100644 --- a/mcp-server/mdl.schema.json +++ b/mcp-server/mdl.schema.json @@ -1,6 +1,6 @@ { "$schema": "https://json-schema.org/draft/2020-12/schema", - "$id": "https://github.com/Canner/WrenAI/tree/main/wren-mdl/mdl.schema.json", + "$id": "https://raw.githubusercontent.com/Canner/WrenAI/main/wren-mdl/mdl.schema.json", "title": "WrenMDL Manifest Schema", "description": "A schema for WrenMDL manifest file", "$defs": { @@ -17,21 +17,115 @@ "type": "string", "minLength": 1 }, + "relationship": { + "description": "the relationship used by the column. If the type is a relationship, this field is required", + "type": "string" + }, "isCalculated": { "description": "whether the column is calculated or not. If the column expression used relationship, this field is required", "type": "boolean" }, + "notNull": { + "description": "whether the column is not null or not", + "type": "boolean" + }, "expression": { "description": "the expression of the column. If the column is calculated, this field is required", "type": ["string", "null"] + }, + "isHidden": { + "description": "whether the column is hidden or not", + "type": "boolean" + }, + "columnLevelAccessControl": { + "description": "the access-control rule for the column", + "type": "object", + "properties": { + "name": { + "description": "the name of the access-control rule", + "type": "string" + }, + "operator": { + "description": "the operator of the access-control rule", + "type": "string", + "enum": [ + "EQUALS", + "NOT_EQUALS", + "GREATER_THAN", + "LESS_THAN", + "GREATER_THAN_OR_EQUALS", + "LESS_THAN_OR_EQUALS" + ] + }, + "requiredProperties": { + "description": "the required properties for the access-control rule", + "type": "array", + "items": { + "$ref": "#/$defs/sessionProperty" + }, + "minItems": 1, + "maxItems": 1 + }, + "threshold": { + "description": "the threshold value of the access-control rule", + "type": "object", + "properties": { + "value": { + "description": "the value of the threshold", + "type": "string" + }, + "dataType": { + "description": "the data type of the threshold", + "type": "string", + "enum": ["NUMERIC", "STRING"] + } + }, + "required": ["value", "dataType"], + "additionalProperties": false + } + }, + "required": ["name", "operator", "requiredProperties"], + "additionalProperties": false + }, + "properties": { + "description": "the customize properties of the column", + "type": "object", + "additionalProperties": { + "type": "string" + } } }, "required": ["name", "type"], "additionalProperties": false + }, + "sessionProperty": { + "type": "object", + "properties": { + "name": { + "description": "the name of the session property", + "type": "string", + "minLength": 1 + }, + "required": { + "description": "whether the session property is required or not", + "type": "boolean" + }, + "defaultExpr": { + "description": "the default SQL expression of the session property", + "type": ["string", "null"] + } + }, + "required": ["name", "required"], + "additionalProperties": false } }, "type": "object", "properties": { + "$schema": { + "description": "the schema of WrenMDL", + "type": "string", + "const": "https://raw.githubusercontent.com/Canner/WrenAI/main/wren-mdl/mdl.schema.json" + }, "catalog": { "description": "the catalog name of WrenMDL", "type": "string", @@ -42,6 +136,17 @@ "type": "string", "minLength": 1 }, + "sampleDataFolder": { + "description": "the folder path for sample data", + "type": "string", + "minLength": 1 + }, + "dataSource": { + "description": "the data source type (case insensitive). Valid values are: BIGQUERY, CLICKHOUSE, CANNER, TRINO, MSSQL, MYSQL, POSTGRES, SNOWFLAKE, DUCKDB, LOCAL_FILE, S3_FILE, GCS_FILE, MINIO_FILE, ORACLE, ATHENA, REDSHIFT", + "type": "string", + "pattern": "^(?:[Bb][Ii][Gg][Qq][Uu][Ee][Rr][Yy]|[Cc][Ll][Ii][Cc][Kk][Hh][Oo][Uu][Ss][Ee]|[Cc][Aa][Nn][Nn][Ee][Rr]|[Tt][Rr][Ii][Nn][Oo]|[Mm][Ss][Ss][Qq][Ll]|[Mm][Yy][Ss][Qq][Ll]|[Pp][Oo][Ss][Tt][Gg][Rr][Ee][Ss]|[Ss][Nn][Oo][Ww][Ff][Ll][Aa][Kk][Ee]|[Dd][Uu][Cc][Kk][Dd][Bb]|[Ll][Oo][Cc][Aa][Ll]_[Ff][Ii][Ll][Ee]|[Ss]3_[Ff][Ii][Ll][Ee]|[Gg][Cc][Ss]_[Ff][Ii][Ll][Ee]|[Mm][Ii][Nn][Ii][Oo]_[Ff][Ii][Ll][Ee]|[Oo][Rr][Aa][Cc][Ll][Ee]|[Aa][Tt][Hh][Ee][Nn][Aa]|[Rr][Ee][Dd][Ss][Hh][Ii][Ff][Tt])$", + "minLength": 1 + }, "models": { "description": "the list of models", "type": "array", @@ -54,6 +159,16 @@ "type": "string", "minLength": 1 }, + "refSql": { + "description": "(WIP) the sql reference of the model", + "type": "string", + "minLength": 1 + }, + "baseObject": { + "description": "(WIP) the base object of the model", + "type": "string", + "minLength": 1 + }, "tableReference": { "description": "the table reference of the model", "type": "object", @@ -77,85 +192,266 @@ "items": { "$ref": "#/$defs/column" } + }, + "primaryKey": { + "description": "the primary key of the model. It's required if the model is the one side of any ONE_TO_MANY or MANY_TO_ONE relationship", + "type": "string" + }, + "cached": { + "description": "(WIP) whether the model is cached or not", + "type": "boolean" + }, + "refreshTime": { + "description": "(WIP) the cache refresh time of the model", + "type": "string", + "pattern": "^\\s*(\\d+(?:\\.\\d+)?)\\s*([a-zA-Z]+)\\s*$" + }, + "rowLevelAccessControls": { + "type": "array", + "items": { + "description": "the row-level access-control rule for the model", + "type": "object", + "properties": { + "name": { + "description": "the name of the access-control rule", + "type": "string" + }, + "requiredProperties": { + "description": "the required properties for the access-control rule", + "type": "array", + "items": { + "$ref": "#/$defs/sessionProperty" + } + }, + "condition": { + "description": "The condition of the access-control rule. A condition is a SQL expression that evaluates to true or false.", + "type": "string" + } + }, + "required": ["name", "requiredProperties", "condition"], + "additionalProperties": false + } + }, + "properties": { + "description": "the customize properties of the model", + "type": "object", + "additionalProperties": { + "type": ["string", "number", "boolean", "object", "array", "null"] + } } }, "required": ["name"], "oneOf": [ + { "required": ["refSql"] }, + { "required": ["baseObject"] }, { "required": ["tableReference"] } ], "additionalProperties": false } - } - }, - "relationships": { - "description": "the list of relationships", - "type": "array", - "unevaluatedItems": false, - "items": { - "type": "object", - "properties": { - "name": { - "description": "the name of the relationship", - "type": "string", - "minLength": 1 - }, - "models": { - "description": "the list of models", - "type": "array", - "items": { + }, + "relationships": { + "description": "the list of relationships", + "type": "array", + "unevaluatedItems": false, + "items": { + "type": "object", + "properties": { + "name": { + "description": "the name of the relationship", "type": "string", "minLength": 1 }, - "minItems": 2, - "maxItems": 2 - }, - "joinType": { - "description": "the join type of the relationship", - "type": "string", - "enum": ["ONE_TO_ONE", "ONE_TO_MANY", "MANY_TO_ONE", "MANY_TO_MANY"] - }, - "condition": { - "description": "the condition of the relationship", - "type": "string", - "minLength": 1 + "models": { + "description": "the list of models", + "type": "array", + "items": { + "type": "string", + "minLength": 1 + }, + "minItems": 2, + "maxItems": 2 + }, + "joinType": { + "description": "the join type of the relationship", + "type": "string", + "enum": ["ONE_TO_ONE", "ONE_TO_MANY", "MANY_TO_ONE", "MANY_TO_MANY"] + }, + "condition": { + "description": "the condition of the relationship", + "type": "string", + "minLength": 1 + }, + "properties": { + "description": "the customize properties of the relationship", + "type": "object", + "additionalProperties": { + "type": ["string", "number", "boolean", "object", "array", "null"] + } + } }, + "required": ["name", "models", "joinType", "condition"] + } + }, + "metrics": { + "description": "(WIP) the list of metrics", + "type": "array", + "unevaluatedItems": false, + "items": { + "description": "(WIP) the metric", + "type": "object", "properties": { - "description": "the customize properties of the relationship", - "type": "object", - "additionalProperties": { - "type": "string" + "name": { + "description": "the name of the metric", + "type": "string", + "minLength": 1 + }, + "baseObject": { + "description": "the base object of the metric", + "type": "string", + "minLength": 1 + }, + "dimension": { + "description": "the list of dimensions", + "type": "array", + "items": { + "$ref": "#/$defs/column" + } + }, + "measure": { + "description": "the list of measures", + "type": "array", + "items": { + "$ref": "#/$defs/column" + }, + "minItems": 1 + }, + "timeGrain": { + "description": "the time grain fields of the metric", + "type": "array", + "unevaluatedItems": false, + "items": { + "description": "the time grain field. It should belong to the dimension fields", + "type": "object", + "properties": { + "name": { + "description": "the name of the time grain field", + "type": "string", + "minLength": 1 + }, + "refColumn": { + "description": "the reference column name of the time grain field", + "type": "string", + "minLength": 1 + }, + "dateParts": { + "description": "the acceptable time units of the time grain field", + "type": "array", + "items": { + "type": "string", + "enum": ["YEAR", "QUARTER", "MONTH", "WEEK", "DAY", "HOUR", "MINUTE", "SECOND"] + } + } + }, + "required": ["name", "refColumn", "dateParts"] + } + }, + "cached": { + "type": "boolean" + }, + "refreshTime": { + "type": "string", + "description": "the cache refresh time of the metric", + "pattern": "^\\s*(\\d+(?:\\.\\d+)?)\\s*([a-zA-Z]+)\\s*$" + }, + "properties": { + "type": "object", + "additionalProperties": { + "type": ["string", "number", "boolean", "object", "array", "null"] + } } - } - }, - "required": ["name", "models", "joinType", "condition"] - } - }, - "views": { - "description": "the list of views", - "type": "array", - "unevaluatedItems": false, - "items": { - "type": "object", - "properties": { - "name": { - "description": "the name of the view", - "type": "string", - "minLength": 1 }, - "statement": { - "description": "the sql statement of the view", - "type": "string", - "minLength": 1 + "required": ["name", "baseObject", "dimension", "measure"] + } + }, + "views": { + "description": "the list of views", + "type": "array", + "unevaluatedItems": false, + "items": { + "type": "object", + "properties": { + "name": { + "description": "the name of the view", + "type": "string", + "minLength": 1 + }, + "statement": { + "description": "the sql statement of the view", + "type": "string", + "minLength": 1 + }, + "properties": { + "description": "the customize properties of the view", + "type": "object", + "additionalProperties": { + "type": ["string", "number", "boolean", "object", "array", "null"] + } + } }, + "required": ["name", "statement"] + } + }, + "enumDefinitions": { + "description": "(WIP) the list of enum definitions", + "type": "array", + "unevaluatedItems": false, + "items": { + "description": "(WIP) the enum definition", + "type": "object", "properties": { - "description": "the customize properties of the view", - "type": "object", - "additionalProperties": { - "type": "string" + "name": { + "description": "the name of the enum", + "type": "string", + "minLength": 1 + }, + "values": { + "type": "array", + "unevaluatedItems": false, + "items": { + "description": "the member of enum", + "type": "object", + "properties": { + "name": { + "description": "the name of the member", + "type": "string", + "minLength": 1 + }, + "value": { + "description": "the value of the member. If not provided, the value is the same as the name", + "type": "string", + "minLength": 1 + }, + "properties": { + "description": "the customize properties of the member", + "type": "object", + "additionalProperties": { + "type": ["string", "number", "boolean", "object", "array", "null"] + } + } + }, + "required": ["name"] + } + }, + "properties": { + "description": "the customize properties of the enum", + "type": "object", + "additionalProperties": { + "type": ["string", "number", "boolean", "object", "array", "null"] + } } - } - }, - "required": ["name", "statement"] + }, + "required": ["name", "values"] + } } }, "required": ["catalog", "schema"], From f0f4aeef6d1ea85fc906e3445fca3a4d1c744489 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 14:02:58 +0800 Subject: [PATCH 12/28] feat(mcp-server): replace mdl_tools with skills, remove DB driver dependencies MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit MDL generation workflow is now captured in two agentskills.io skills: - skills/generate-mdl/SKILL.md — generate MDL via ibis-server metadata endpoints - skills/mdl-project/SKILL.md — save/load/build YAML project directories Schema discovery (list_remote_tables, list_remote_constraints) routes through ibis-server instead of direct SQLAlchemy connections, eliminating the need to install database-specific drivers on the MCP server side. Changes: - Delete mcp-server/app/mdl_tools.py (SQLAlchemy-based tools removed) - Add mdl_validate_manifest tool directly in wren.py (ibis-server dry-plan) - Remove sqlalchemy, jsonschema, pyyaml optional dependencies from pyproject.toml - Update README to reflect new skill-based MDL generation workflow Co-Authored-By: Claude Sonnet 4.6 --- .gitignore | 2 + mcp-server/README.md | 111 ++--- mcp-server/app/mdl_tools.py | 775 --------------------------------- mcp-server/app/wren.py | 113 ++++- mcp-server/docker/compose.yaml | 17 +- mcp-server/pyproject.toml | 6 - skills/generate-mdl/SKILL.md | 206 +++++++++ skills/mdl-project/SKILL.md | 163 +++++++ 8 files changed, 528 insertions(+), 865 deletions(-) delete mode 100644 mcp-server/app/mdl_tools.py create mode 100644 skills/generate-mdl/SKILL.md create mode 100644 skills/mdl-project/SKILL.md diff --git a/.gitignore b/.gitignore index 6ca395ecb..bb1f50ef9 100644 --- a/.gitignore +++ b/.gitignore @@ -15,6 +15,8 @@ target/ .mvn/timing.properties .mvn/maven.config wren-server/etc +mcp-server/workspace/* +!mcp-server/workspace/.gitkeep /**/var/ __pycache__/ venv/ diff --git a/mcp-server/README.md b/mcp-server/README.md index c2a64ba0e..e1b284164 100644 --- a/mcp-server/README.md +++ b/mcp-server/README.md @@ -68,6 +68,31 @@ You would see that the MDL and connection info are loaded. Then, you can use `Ct docker compose up ``` +### MDL Workspace + +The `workspace/` directory is the conventional location for MDL files when using Docker. +It is mounted into the container at `/workspace`. + +**Input — load an existing MDL:** +Place your MDL JSON file in `mcp-server/workspace/` on your host, then reference it in the MCP tool: +``` +deploy(mdl_file_path="/workspace/my_mdl.json") +``` + +**Output — save a generated MDL:** +The agent writes YAML project files and compiled MDL JSON back to your host via the `mdl-project` skill: +``` +# agent writes: mcp-server/workspace/my_project/wren_project.yml, models/*.yml, ... +# agent compiles to: mcp-server/workspace/my_project/target/mdl.json +``` + +**Custom host path:** +Set `MDL_WORKSPACE` in `docker/.env` to point to any directory on your host: +```sh +# docker/.env +MDL_WORKSPACE=/Users/me/my-mdl-files +``` + ### 3. Set Environment Variables There are two ways to set the required environment variables: @@ -139,84 +164,44 @@ Optional follow-ups: --- -## MDL Tools (Optional) - -The **MDL Tools** extension adds six MCP tools that let any AI agent (Claude, -Copilot, Cursor, Cline…) **generate a Wren MDL manifest autonomously**, by -directly exploring a database and building the manifest step-by-step — without -a secondary LLM layer. +## Generating an MDL (No Database Drivers Required) -The calling agent acts as the orchestrator: it chooses which tools to call, -inspects the schema, builds the JSON, and validates it before passing it to -the existing `deploy` tool. +The **`generate-mdl` skill** (`skills/generate-mdl/SKILL.md`) guides any AI agent through +creating a Wren MDL manifest from scratch — using ibis-server's metadata endpoints for +schema discovery. No local database drivers need to be installed on the MCP server. -### Prerequisites +### How it works -- `sqlalchemy>=2.0` and `jsonschema>=4.0` -- The appropriate DB driver for your data source +The agent calls ibis-server tools in sequence: -### Installation - -```sh -# Core MDL tools -just install-mdl -# or: uv pip install sqlalchemy jsonschema - -# DB driver (pick one) -just install-mdl-driver postgres # psycopg2-binary -just install-mdl-driver mysql # pymysql -just install-mdl-driver duckdb # duckdb-engine -# for other drivers: uv pip install ``` +User → "Generate an MDL for my PostgreSQL ecommerce database" -> If `sqlalchemy` or `jsonschema` are missing the tools are silently skipped and -> all other server functionality continues to work. +Agent → setup_connection(datasource="POSTGRES", + conn_info={"host":"localhost","port":"5432","user":"...","database":"shop"}) -### Environment Variables +Agent → list_remote_tables() + ← [{"name":"orders","columns":[...]}, {"name":"customers","columns":[...]}, ...] -| Variable | Required | Description | -|---|---|---| -| `WREN_ENGINE_ENDPOINT` | No | ibis-server URL — enables dry-plan check in `mdl_validate_manifest` | - -### Available Tools - -| Tool | Description | -|---|---| -| `mdl_connect_database(connection_string, session_id?)` | Connect to a DB via SQLAlchemy URL. Stores the connection under `session_id`. | -| `mdl_list_tables(session_id?)` | List all user tables in the connected database. | -| `mdl_get_column_info(table, session_id?)` | Column metadata: name, type, nullable, PK, FK references. | -| `mdl_get_column_stats(table, column, session_id?)` | Distinct count, null count, min, max for a column. | -| `mdl_get_sample_data(table, limit?, session_id?)` | Fetch sample rows (max 20) to understand data semantics. | -| `mdl_validate_manifest(mdl)` | Validate an MDL dict against the official JSON Schema, plus optional dry-plan. | - -Multiple concurrent sessions are supported via the `session_id` parameter (default `"default"`). - -### Example Workflow - -The AI agent calls the tools in sequence — no additional configuration needed -beyond providing a connection string: - -``` -User → "Generate an MDL for my PostgreSQL ecommerce database at - postgresql://user:pass@localhost:5432/shop" +Agent → list_remote_constraints() + ← [{"constraintType":"FOREIGN_KEY","constraintTable":"orders", ...}] -Agent → mdl_connect_database("postgresql://user:pass@localhost:5432/shop") - ← "Connected. Found 5 table(s): customers, order_items, orders, products, reviews" - -Agent → mdl_get_column_info("orders") - ← [{"name":"id","type":"INTEGER","primary_key":true}, {"name":"customer_id", - "type":"INTEGER","foreign_key":{"table":"customers","column":"id"}}, ...] - -Agent → mdl_get_column_info("customers") # repeats for each table... +Agent → (builds MDL JSON from schema info) Agent → mdl_validate_manifest({"catalog":"wren","schema":"public", "dataSource":"POSTGRES","models":[...]}) - ← "MDL validation passed (JSON Schema + dry-plan)." + ← "MDL validation passed." -Agent → deploy({"catalog":"wren","schema":"public","dataSource":"POSTGRES","models":[...]}) - ← "MDL deployed successfully" +Agent → deploy_manifest({"catalog":"wren","schema":"public","dataSource":"POSTGRES","models":[...]}) + ← "MDL deployed successfully (5 models, 23 columns)" ``` +### Environment Variables + +| Variable | Required | Description | +|---|---|---| +| `WREN_ENGINE_ENDPOINT` | No | ibis-server URL for dry-plan check in `mdl_validate_manifest` (default: `http://localhost:8000`) | + --- ## Additional Resources diff --git a/mcp-server/app/mdl_tools.py b/mcp-server/app/mdl_tools.py deleted file mode 100644 index f9ca2d8a4..000000000 --- a/mcp-server/app/mdl_tools.py +++ /dev/null @@ -1,775 +0,0 @@ -"""MDL generation tools — flat MCP tools for agentic MDL creation. - -Registers six tools onto the shared FastMCP instance. The calling AI agent -acts as the orchestrator: it connects to a database, explores the schema, -builds an MDL manifest, and validates it — all via direct tool calls, without -a secondary LLM layer. - -Tools registered: - -- ``mdl_connect_database`` — establish a SQLAlchemy connection for a session -- ``mdl_list_tables`` — list user tables in the connected database -- ``mdl_get_column_info`` — column metadata (type, nullable, PK, FK) -- ``mdl_get_column_stats`` — distinct count, null count, min/max for a column -- ``mdl_get_sample_data`` — fetch sample rows to understand data semantics -- ``mdl_validate_manifest`` — validate an MDL dict (JSON Schema + dry-plan) -- ``mdl_save_project`` — save MDL JSON as a dbt-like YAML project directory -- ``mdl_load_project`` — load a YAML project directory and return MDL JSON -- ``build_mdl_project`` — assemble a YAML project into ``target/mdl.json`` - -Requires ``sqlalchemy`` and ``jsonschema`` to be installed:: - - uv pip install sqlalchemy jsonschema - -Plus the appropriate DB driver, e.g.: - uv pip install psycopg2-binary # PostgreSQL - uv pip install pymysql # MySQL - uv pip install duckdb-engine # DuckDB - -If ``sqlalchemy`` or ``jsonschema`` are missing the module loads silently and -no tools are registered, so the rest of the server -continues to work unchanged. - -Environment variables: - -- ``WREN_ENGINE_ENDPOINT`` — ibis-server URL for MDL dry-plan validation -""" - -from __future__ import annotations - -import base64 -import json -import os -from typing import Any - -from mcp.server.fastmcp import FastMCP -from mcp.types import ToolAnnotations - -try: - import sqlalchemy as sa - from sqlalchemy import inspect as sa_inspect, text - import jsonschema - import yaml - - _AVAILABLE = True -except ImportError: - _AVAILABLE = False - -# --------------------------------------------------------------------------- -# Module-level session state: session_id → SA Engine -# --------------------------------------------------------------------------- - -_engines: dict[str, "sa.Engine"] = {} - -# MDL JSON Schema — fetched once per process -_MDL_SCHEMA_URL = ( - "https://raw.githubusercontent.com/Canner/WrenAI/main/wren-mdl/mdl.schema.json" -) -_cached_schema: dict[str, Any] | None = None - - -def _get_mdl_schema() -> dict[str, Any]: - global _cached_schema - if _cached_schema is None: - import httpx # already in core deps - resp = httpx.get(_MDL_SCHEMA_URL, timeout=10.0) - resp.raise_for_status() - _cached_schema = resp.json() - return _cached_schema - - -def _engine(session_id: str) -> "sa.Engine | None": - return _engines.get(session_id) - - -# --------------------------------------------------------------------------- -# Tool registration -# --------------------------------------------------------------------------- - - -def register_mdl_tools(mcp: FastMCP) -> None: - """Register flat MDL tools and the generate_mdl prompt onto *mcp*.""" - if not _AVAILABLE: - print( # noqa: T201 - "[MDL Tools] sqlalchemy, jsonschema, or pyyaml not installed — " - "MDL tools not registered. Run: uv pip install sqlalchemy jsonschema pyyaml" - ) - return - - # ------------------------------------------------------------------ - # Prompt: defines the MDL generation workflow as a reusable starting point. - # Claude Desktop exposes this as a slash command: /generate_mdl - # Other clients can invoke it via prompts/get. - # ------------------------------------------------------------------ - - @mcp.prompt() - def generate_mdl( - connection_string: str = "", - project_dir: str = "", - deploy: bool = False, - ) -> str: - """Guided workflow for generating a Wren MDL manifest from a database. - - Invoke this prompt to start the MDL generation process. The agent - will follow the steps below automatically. - - Args: - connection_string: Optional SQLAlchemy URL to skip the first question. - e.g. ``postgresql://user:pass@host:5432/db`` - project_dir: Optional path to save the MDL as a YAML project directory. - If empty, Step 6 (save) is skipped. - e.g. ``./my_project`` - deploy: Whether to deploy the MDL to Wren Engine after validation. - Set to False to stop after saving the project. Default: True. - """ - conn_step = ( - f'You already have the connection string: `{connection_string}`\n' - f'Start at Step 2.' - if connection_string - else 'Ask the user for their database connection string (SQLAlchemy URL).' - ) - - if project_dir: - save_step = ( - f'Call `mdl_save_project(mdl=, project_dir="{project_dir}")` ' - f'to persist the MDL as YAML files for version control.' - ) - else: - save_step = ( - 'Ask the user if they want to save the MDL as a YAML project directory.\n' - 'If yes, ask for the path and call ' - '`mdl_save_project(mdl=, project_dir=)`.\n' - 'If no, skip to the next step.' - ) - - if deploy: - deploy_step = ( - 'Call `deploy` with the validated manifest.\n' - 'Confirm success to the user.' - ) - else: - deploy_step = ( - 'Deployment was not requested — skip this step.\n' - 'Inform the user that the MDL is ready and saved (if a project_dir was used).\n' - 'Ask if they want to deploy now or later.' - ) - - return f"""You are generating a Wren MDL manifest for a user's database. -Follow these steps in order — do not skip steps or ask unnecessary questions. - -## Step 1 — Get connection string -{conn_step} - -## Step 2 — Connect -Call `mdl_connect_database` with the connection string. -If it fails, report the error and ask the user to correct it. - -## Step 3 — Explore schema -For EVERY table returned: - a. Call `mdl_get_column_info(
)` to get column types, PKs, FKs. - b. Call `mdl_get_sample_data(
, limit=3)` to understand data semantics. - c. For columns where purpose is unclear, call `mdl_get_column_stats(
, )`. - -## Step 4 — Build MDL manifest -Construct the MDL JSON following these rules: -- `catalog`: use "wren" unless user specifies otherwise. -- `schema`: use the database's default schema (e.g. "public" for PostgreSQL). -- `dataSource`: set to the correct enum value (POSTGRES, MYSQL, DUCKDB, BIGQUERY, etc.). -- Each table → one `Model`. Set `tableReference.table` to the table name. -- Each column → one `Column`. Use the exact DB column name for `name`. -- Mark primary key columns with `"isHidden": false` and note them in model `primaryKey`. -- For FK columns, add a `Relationship` entry between the two models. -- Calculated columns and metrics can be added later — omit them for now. - -## Step 5 — Validate -Call `mdl_validate_manifest` with the constructed manifest dict. -If validation fails, fix the reported errors and validate again. - -## Step 6 — Save Project -{save_step} - -## Step 7 — Deploy -{deploy_step} - -Do not ask for confirmation between steps unless you encounter an error. -""" - - # ------------------------------------------------------------------ - # 1. Connect to database - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Connect to Database", - readOnlyHint=False, - ), - ) - async def mdl_connect_database( - connection_string: str, - session_id: str = "default", - ) -> str: - """Connect to a database using a SQLAlchemy connection string. - - Must be called before any other ``mdl_*`` tool that needs schema access. - Each ``session_id`` maintains its own independent connection. - - Supported schemes (driver must be installed separately): - - ``postgresql://user:pass@host:5432/db`` (needs psycopg2-binary) - - ``mysql+pymysql://user:pass@host:3306/db`` (needs pymysql) - - ``duckdb:///path/to/file.db`` (needs duckdb-engine) - - ``sqlite:///path/to/file.db`` (built-in) - - Args: - connection_string: SQLAlchemy URL. - session_id: Connection identifier, allows multiple parallel sessions. - - Returns: - Success message with table count, or an error description. - """ - try: - engine = sa.create_engine(connection_string) - with engine.connect() as conn: - conn.execute(text("SELECT 1")) - _engines[session_id] = engine - tables = sorted(sa_inspect(engine).get_table_names()) - preview = ", ".join(tables[:20]) + ("…" if len(tables) > 20 else "") - return ( - f"Connected. Found {len(tables)} table(s): {preview}\n" - f"→ Next: call `mdl_get_column_info` for each table, " - f"then `mdl_get_sample_data` to understand the data." - ) - except Exception as e: - return f"Connection failed: {e}" - - # ------------------------------------------------------------------ - # 2. List tables - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="List Database Tables", - readOnlyHint=True, - ), - ) - async def mdl_list_tables(session_id: str = "default") -> str: - """List all user tables in the connected database. - - Call ``mdl_connect_database`` first. - - Args: - session_id: Session to query. - - Returns: - JSON array of table names, or an error string. - """ - engine = _engine(session_id) - if engine is None: - return f"No connection for session '{session_id}'. Call mdl_connect_database first." - try: - tables = sorted(sa_inspect(engine).get_table_names()) - return ( - json.dumps(tables) + "\n" - "→ Next: call `mdl_get_column_info(
)` for each table." - ) - except Exception as e: - return f"Error listing tables: {e}" - - # ------------------------------------------------------------------ - # 3. Column info - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Get Column Info", - readOnlyHint=True, - ), - ) - async def mdl_get_column_info( - table: str, - session_id: str = "default", - ) -> str: - """Get column metadata for a table. - - Returns name, data type, nullable flag, whether it is a primary key, - and foreign key references (if any). - - Args: - table: Table name. - session_id: Session to query. - - Returns: - JSON array of column objects, or an error string. - """ - engine = _engine(session_id) - if engine is None: - return f"No connection for session '{session_id}'. Call mdl_connect_database first." - try: - insp = sa_inspect(engine) - columns = insp.get_columns(table) - pk_cols = set(insp.get_pk_constraint(table).get("constrained_columns", [])) - fk_map: dict[str, dict] = {} - for fk in insp.get_foreign_keys(table): - for local, ref in zip(fk["constrained_columns"], fk["referred_columns"]): - fk_map[local] = {"table": fk["referred_table"], "column": ref} - - result = [] - for col in columns: - name = col["name"] - entry: dict[str, Any] = { - "name": name, - "type": str(col["type"]), - "nullable": col.get("nullable", True), - "primary_key": name in pk_cols, - } - if name in fk_map: - entry["foreign_key"] = fk_map[name] - result.append(entry) - return ( - json.dumps(result, default=str) + "\n" - f"→ Next: call `mdl_get_sample_data('{table}', limit=3)` " - f"to understand data semantics." - ) - except Exception as e: - return f"Error getting column info for '{table}': {e}" - - # ------------------------------------------------------------------ - # 4. Column stats - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Get Column Statistics", - readOnlyHint=True, - ), - ) - async def mdl_get_column_stats( - table: str, - column: str, - session_id: str = "default", - ) -> str: - """Get basic statistics for a column: distinct count, null count, min, max. - - Useful for inferring column semantics (e.g., ID vs. enum vs. metric). - - Args: - table: Table name. - column: Column name. - session_id: Session to query. - - Returns: - JSON object with stats, or an error string. - """ - engine = _engine(session_id) - if engine is None: - return f"No connection for session '{session_id}'. Call mdl_connect_database first." - try: - col_id = sa.column(column) - tbl = sa.table(table, col_id) - stmt = sa.select( - sa.func.count(sa.func.distinct(col_id)).label("distinct_count"), - (sa.func.count() - sa.func.count(col_id)).label("null_count"), - sa.func.min(col_id).label("min_val"), - sa.func.max(col_id).label("max_val"), - ).select_from(tbl) - with engine.connect() as conn: - row = conn.execute(stmt).one() - return json.dumps({ - "distinct_count": row.distinct_count, - "null_count": row.null_count, - "min": str(row.min_val) if row.min_val is not None else None, - "max": str(row.max_val) if row.max_val is not None else None, - }, default=str) - except Exception as e: - return f"Error getting stats for '{table}.{column}': {e}" - - # ------------------------------------------------------------------ - # 5. Sample data - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Get Sample Data", - readOnlyHint=True, - ), - ) - async def mdl_get_sample_data( - table: str, - limit: int = 5, - session_id: str = "default", - ) -> str: - """Fetch sample rows from a table to understand data semantics. - - Args: - table: Table name. - limit: Number of rows (default 5, capped at 20). - session_id: Session to query. - - Returns: - JSON array of row objects, or an error string. - """ - engine = _engine(session_id) - if engine is None: - return f"No connection for session '{session_id}'. Call mdl_connect_database first." - try: - limit = min(int(limit), 20) - tbl = sa.table(table) - stmt = sa.select(text("*")).select_from(tbl).limit(limit) - with engine.connect() as conn: - rows = conn.execute(stmt).mappings().all() - return ( - json.dumps([dict(r) for r in rows], default=str) + "\n" - "→ After exploring all tables: build the MDL manifest, " - "then call `mdl_validate_manifest`." - ) - except Exception as e: - return f"Error sampling '{table}': {e}" - - # ------------------------------------------------------------------ - # 6. Validate MDL manifest - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Validate MDL Manifest", - readOnlyHint=True, - ), - ) - async def mdl_validate_manifest(mdl: dict) -> str: - """Validate an MDL manifest dict against the official Wren JSON Schema. - - Optionally runs a dry-plan against ibis-server when - ``WREN_ENGINE_ENDPOINT`` is set. - - Pass the MDL as a plain dict (use ``"schema"`` key, not ``"schema_name"``). - - Args: - mdl: The MDL manifest as a dictionary. - - Returns: - A string describing the validation result. - """ - # Step 1: JSON Schema - try: - schema = _get_mdl_schema() - except Exception as e: - return f"Warning: could not fetch MDL schema: {e}. Skipping schema check." - - errors = list(jsonschema.Draft202012Validator(schema).iter_errors(mdl)) - if errors: - messages = "; ".join( - f"{'.'.join(str(p) for p in e.absolute_path) or ''}: {e.message}" - for e in errors[:5] - ) - return f"JSON Schema validation failed ({len(errors)} error(s)): {messages}" - - # Step 2: ibis-server dry-plan (optional) - import httpx # already in core deps - - ibis_url = os.getenv("WREN_ENGINE_ENDPOINT") - if not ibis_url: - return ( - "JSON Schema validation passed. " - "(Skipping dry-plan: WREN_ENGINE_ENDPOINT not set)\n" - "→ Next: optionally call `mdl_save_project(mdl=, project_dir=\"./my_project\")` " - "to save as YAML files, then call `deploy` with this manifest." - ) - - try: - manifest_str = base64.b64encode(json.dumps(mdl).encode()).decode() - async with httpx.AsyncClient(timeout=30.0) as client: - resp = await client.post( - f"{ibis_url.rstrip('/')}/v3/connector/dry-plan", - json={"manifest_str": manifest_str, "sql": "SELECT 1"}, - headers={"x-wren-fallback_disable": "true"}, - ) - if resp.status_code == 200: - return ( - "MDL validation passed (JSON Schema + dry-plan).\n" - "→ Next: optionally call `mdl_save_project(mdl=, project_dir=\"./my_project\")` " - "to save as YAML files, then call `deploy` with this manifest." - ) - return ( - f"JSON Schema passed, but dry-plan failed " - f"(HTTP {resp.status_code}): {resp.text[:300]}" - ) - except Exception as e: - return f"JSON Schema passed, but dry-plan request failed: {e}" - - # ------------------------------------------------------------------ - # 7. Save MDL project as YAML - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Save MDL Project", - readOnlyHint=False, - ), - ) - def mdl_save_project(mdl: str, project_dir: str) -> str: - """Save an MDL JSON as a Wren project directory (dbt-like YAML structure). - - Creates: - /wren_project.yml — project metadata - /models/.yml — one file per model - /relationships.yml — all relationships - /views.yml — all views - /metrics.yml — all metrics - - Args: - mdl: The MDL JSON string (output from mdl_validate_manifest or manual assembly). - project_dir: Path to the directory to create (will be created if missing). - - Returns: - Summary of files written. - """ - try: - manifest = json.loads(mdl) - except json.JSONDecodeError as e: - return f"ERROR: Invalid JSON — {e}" - - project_dir = os.path.expanduser(project_dir) - os.makedirs(os.path.join(project_dir, "models"), exist_ok=True) - - files_written = [] - - # wren_project.yml - project_meta = { - "name": os.path.basename(project_dir), - "version": "1.0", - "catalog": manifest.get("catalog", ""), - "schema": manifest.get("schema", ""), - "dataSource": manifest.get("dataSource", ""), - } - project_yml = os.path.join(project_dir, "wren_project.yml") - with open(project_yml, "w") as f: - yaml.dump(project_meta, f, default_flow_style=False, allow_unicode=True) - files_written.append(project_yml) - - # models/.yml — one file per model - for model in manifest.get("models", []): - model_name = model.get("name", "unknown") - model_yml = os.path.join(project_dir, "models", f"{model_name}.yml") - with open(model_yml, "w") as f: - yaml.dump(model, f, default_flow_style=False, allow_unicode=True) - files_written.append(model_yml) - - # relationships.yml - rels_yml = os.path.join(project_dir, "relationships.yml") - with open(rels_yml, "w") as f: - yaml.dump( - {"relationships": manifest.get("relationships", [])}, - f, default_flow_style=False, allow_unicode=True, - ) - files_written.append(rels_yml) - - # views.yml - views_yml = os.path.join(project_dir, "views.yml") - with open(views_yml, "w") as f: - yaml.dump( - {"views": manifest.get("views", [])}, - f, default_flow_style=False, allow_unicode=True, - ) - files_written.append(views_yml) - - # metrics.yml - metrics_yml = os.path.join(project_dir, "metrics.yml") - with open(metrics_yml, "w") as f: - yaml.dump( - {"metrics": manifest.get("metrics", [])}, - f, default_flow_style=False, allow_unicode=True, - ) - files_written.append(metrics_yml) - - summary = "\n".join(f" ✓ {p}" for p in files_written) - return ( - f"Project saved to: {project_dir}\n\n" - f"Files written:\n{summary}\n\n" - f"→ Next: call `build_mdl_project(project_dir='{project_dir}')` to write `target/mdl.json`, " - f"or call `deploy(mdl_file_path=)` to deploy to Wren Engine, " - f"or commit {project_dir} to version control." - ) - - # ------------------------------------------------------------------ - # 8. Load MDL project from YAML - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Load MDL Project", - readOnlyHint=True, - ), - ) - def mdl_load_project(project_dir: str) -> str: - """Load a Wren project directory (YAML structure) and return the MDL JSON. - - Reads: - /wren_project.yml - /models/*.yml - /relationships.yml - /views.yml - /metrics.yml - - Args: - project_dir: Path to the Wren project directory. - - Returns: - The assembled MDL as a JSON string, ready to pass to `deploy`. - """ - import glob - - project_dir = os.path.expanduser(project_dir) - - if not os.path.isdir(project_dir): - return f"ERROR: Directory not found: {project_dir}" - - # wren_project.yml - project_yml = os.path.join(project_dir, "wren_project.yml") - if not os.path.isfile(project_yml): - return f"ERROR: wren_project.yml not found in {project_dir}" - - with open(project_yml) as f: - project_meta = yaml.safe_load(f) or {} - - manifest: dict[str, Any] = { - "catalog": project_meta.get("catalog", ""), - "schema": project_meta.get("schema", ""), - "dataSource": project_meta.get("dataSource", ""), - "models": [], - "relationships": [], - "views": [], - "metrics": [], - } - - # models/*.yml - model_files = sorted(glob.glob(os.path.join(project_dir, "models", "*.yml"))) - for mf in model_files: - with open(mf) as f: - model = yaml.safe_load(f) - if model: - manifest["models"].append(model) - - # relationships.yml - rels_yml = os.path.join(project_dir, "relationships.yml") - if os.path.isfile(rels_yml): - with open(rels_yml) as f: - data = yaml.safe_load(f) or {} - manifest["relationships"] = data.get("relationships", []) - - # views.yml - views_yml = os.path.join(project_dir, "views.yml") - if os.path.isfile(views_yml): - with open(views_yml) as f: - data = yaml.safe_load(f) or {} - manifest["views"] = data.get("views", []) - - # metrics.yml - metrics_yml = os.path.join(project_dir, "metrics.yml") - if os.path.isfile(metrics_yml): - with open(metrics_yml) as f: - data = yaml.safe_load(f) or {} - manifest["metrics"] = data.get("metrics", []) - - mdl_json = json.dumps(manifest, indent=2, ensure_ascii=False) - model_count = len(manifest["models"]) - rel_count = len(manifest["relationships"]) - - return ( - f"Project loaded from: {project_dir}\n" - f" Models: {model_count}, Relationships: {rel_count}\n\n" - f"MDL JSON:\n```json\n{mdl_json}\n```\n\n" - f"→ Next: call `mdl_validate_manifest(mdl=)` to validate, " - f"then `build_mdl_project(project_dir='{project_dir}')` to write `target/mdl.json`, " - f"then `deploy(mdl_file_path='{project_dir}/target/mdl.json')` to deploy." - ) - - # ------------------------------------------------------------------ - # 9. Build MDL project — output YAML project as target/mdl.json - # ------------------------------------------------------------------ - - @mcp.tool( - annotations=ToolAnnotations( - title="Build MDL Project", - readOnlyHint=False, - ), - ) - def build_mdl_project(project_dir: str) -> str: - """Assemble a Wren YAML project directory into a single JSON file. - - Reads the YAML files from the project directory (same as `mdl_load_project`) - and writes the assembled MDL JSON to ``/target/mdl.json``. - - Args: - project_dir: Path to the Wren project directory containing - ``wren_project.yml``, ``models/*.yml``, etc. - - Returns: - Path to the generated ``target/mdl.json`` file. - """ - import glob - - project_dir = os.path.expanduser(project_dir) - - if not os.path.isdir(project_dir): - return f"ERROR: Directory not found: {project_dir}" - - project_yml = os.path.join(project_dir, "wren_project.yml") - if not os.path.isfile(project_yml): - return f"ERROR: wren_project.yml not found in {project_dir}" - - with open(project_yml) as f: - project_meta = yaml.safe_load(f) or {} - - manifest: dict[str, Any] = { - "catalog": project_meta.get("catalog", ""), - "schema": project_meta.get("schema", ""), - "dataSource": project_meta.get("dataSource", ""), - "models": [], - "relationships": [], - "views": [], - "metrics": [], - } - - # models/*.yml - model_files = sorted(glob.glob(os.path.join(project_dir, "models", "*.yml"))) - for mf in model_files: - with open(mf) as f: - model = yaml.safe_load(f) - if model: - manifest["models"].append(model) - - # relationships.yml - rels_yml = os.path.join(project_dir, "relationships.yml") - if os.path.isfile(rels_yml): - with open(rels_yml) as f: - data = yaml.safe_load(f) or {} - manifest["relationships"] = data.get("relationships", []) - - # views.yml - views_yml = os.path.join(project_dir, "views.yml") - if os.path.isfile(views_yml): - with open(views_yml) as f: - data = yaml.safe_load(f) or {} - manifest["views"] = data.get("views", []) - - # metrics.yml - metrics_yml = os.path.join(project_dir, "metrics.yml") - if os.path.isfile(metrics_yml): - with open(metrics_yml) as f: - data = yaml.safe_load(f) or {} - manifest["metrics"] = data.get("metrics", []) - - # write to target/mdl.json - target_dir = os.path.join(project_dir, "target") - os.makedirs(target_dir, exist_ok=True) - output_path = os.path.join(target_dir, "mdl.json") - mdl_json = json.dumps(manifest, indent=2, ensure_ascii=False) - with open(output_path, "w") as f: - f.write(mdl_json) - - model_count = len(manifest["models"]) - rel_count = len(manifest["relationships"]) - return ( - f"MDL JSON written to: {output_path}\n" - f" Models: {model_count}, Relationships: {rel_count}\n\n" - f"→ Next: call `deploy(mdl_file_path='{output_path}')` to deploy to Wren Engine." - ) diff --git a/mcp-server/app/wren.py b/mcp-server/app/wren.py index a0b1c365a..14cb24bb2 100644 --- a/mcp-server/app/wren.py +++ b/mcp-server/app/wren.py @@ -11,11 +11,9 @@ try: from dto import TableColumns from utils import dict_to_base64_string, json_to_base64_string - from mdl_tools import register_mdl_tools except ImportError: from app.dto import TableColumns from app.utils import dict_to_base64_string, json_to_base64_string - from app.mdl_tools import register_mdl_tools load_dotenv() @@ -116,6 +114,7 @@ def get_relationships(self) -> list: mdl_cache = MdlCache() data_source = None +connection_info = None if mdl_path: with open(mdl_path) as f: @@ -234,6 +233,95 @@ async def deploy(mdl_file_path: str) -> str: return f"MDL deployed successfully from {mdl_file_path}" +@mcp.tool( + annotations=ToolAnnotations( + title="Setup Connection", + destructiveHint=False, + ), +) +async def setup_connection(datasource: str, conn_info: dict) -> str: + """ + Configure the database connection for query execution at runtime. + + Call this after deploying the MDL so that `query` and `dry_run` know + which database to connect to. + + Args: + datasource: Data source type, e.g. "POSTGRES", "MYSQL", "DUCKDB", "BIGQUERY". + conn_info: Connection credentials dict. Fields vary by data source: + - POSTGRES/MYSQL: {"host": ..., "port": ..., "user": ..., "password": ..., "database": ...} + - DUCKDB: {"path": ""} + - BIGQUERY: {"project": ..., "dataset": ..., "credentials_base64": ...} + """ + global data_source, connection_info + data_source = datasource.lower() + connection_info = conn_info + return f"Connection configured for {data_source}" + + +@mcp.tool( + annotations=ToolAnnotations( + title="Deploy MDL Manifest", + destructiveHint=True, + ), +) +async def deploy_manifest(mdl: dict) -> str: + """ + Deploy an MDL manifest dict directly to Wren Engine. + + Use this instead of `deploy` when the MDL is already in memory + (e.g. after `mdl_validate_manifest`), avoiding an intermediate file write. + + Args: + mdl: The MDL manifest as a dictionary. + """ + global data_source + data_source = mdl.get("dataSource", "").lower() + mdl_base64 = dict_to_base64_string(mdl) + mdl_cache.set_mdl(mdl_base64) + models = mdl.get("models", []) + total_columns = sum(len(m.get("columns", [])) for m in models) + return f"MDL deployed successfully ({len(models)} models, {total_columns} columns)" + + +@mcp.tool( + annotations=ToolAnnotations( + title="Validate MDL Manifest", + readOnlyHint=True, + ), +) +async def mdl_validate_manifest(mdl: dict) -> str: + """ + Validate an MDL manifest dict via ibis-server dry-plan. + + Sends the manifest to the ibis-server ``/v3/connector/dry-plan`` endpoint + to verify that the MDL is structurally valid before deploying. + + Args: + mdl: The MDL manifest as a dictionary. + + Returns: + A string describing the validation result. + """ + ibis_url = os.getenv("WREN_ENGINE_ENDPOINT", f"http://{WREN_URL}") + manifest_str = base64.b64encode(json.dumps(mdl).encode()).decode() + try: + async with httpx.AsyncClient(timeout=30.0) as client: + resp = await client.post( + f"{ibis_url.rstrip('/')}/v3/connector/dry-plan", + json={"manifest_str": manifest_str, "sql": "SELECT 1"}, + headers={"x-wren-fallback_disable": "true"}, + ) + if resp.status_code == 200: + return ( + "MDL validation passed.\n" + "→ Next: call `deploy_manifest(mdl=)` to deploy." + ) + return f"Dry-plan validation failed (HTTP {resp.status_code}): {resp.text[:300]}" + except Exception as e: + return f"Dry-plan request failed: {e}" + + @mcp.tool( annotations=ToolAnnotations( title="Check Deployment Status", @@ -492,6 +580,24 @@ async def get_wren_guide() -> str: Understand how to use Wren Engine effectively to query your database """ + if data_source is None: + return """ + Wren Engine is not yet configured. To get started: + + ## Quick Setup + 1. Call `generate_mdl` with your database connection string: + e.g. `postgresql://user:pass@host:5432/mydb` + The agent will explore your schema, build an MDL, configure the + connection, and deploy — all automatically. + + 2. Once deployed, you can start querying with natural language or SQL. + + ## Manual Setup (if you already have an MDL file) + 1. Call `deploy(mdl_file_path=)` to load the MDL. + 2. Call `setup_connection(datasource=, conn_info={...})` with your DB credentials. + 3. Call `health_check()` to verify everything is working. + """ + tips = f""" ## Tips for using Wren Engine with {data_source.capitalize()} You are connected to a {data_source.capitalize()} database via Wren Engine. @@ -553,8 +659,5 @@ async def health_check() -> str: return "Wren Engine is not healthy" -# Register MDL agent tools (no-op if wren-agent is not installed) -register_mdl_tools(mcp) - if __name__ == "__main__": mcp.run(transport=MCP_TRANSPORT) diff --git a/mcp-server/docker/compose.yaml b/mcp-server/docker/compose.yaml index 62e29fc34..9f1e7cd34 100644 --- a/mcp-server/docker/compose.yaml +++ b/mcp-server/docker/compose.yaml @@ -5,23 +5,8 @@ services: - "8000:8000" - "9000:9000" environment: - WREN_ENGINE_ENDPOINT: http://java-engine:8080 ENABLE_MCP_SERVER: "true" MCP_PORT: "9000" volumes: - ./../etc:/usr/src/app/etc - java-engine: - image: ghcr.io/canner/wren-engine:latest - ports: - - "8080:8080" - volumes: - - ./../etc:/usr/src/app/etc - deploy: - resources: - limits: - cpus: '1' - memory: 1G - environment: - MAX_HEAP_SIZE: 819m - MIN_HEAP_SIZE: 819m - \ No newline at end of file + - ${MDL_WORKSPACE:-../workspace}:/workspace diff --git a/mcp-server/pyproject.toml b/mcp-server/pyproject.toml index 3d436f688..b6ff902f8 100644 --- a/mcp-server/pyproject.toml +++ b/mcp-server/pyproject.toml @@ -13,9 +13,3 @@ dependencies = [ "ruff>=0.11.0", ] -[project.optional-dependencies] -mdl = [ - "sqlalchemy>=2.0", - "jsonschema>=4.0", - "pyyaml>=6.0", -] diff --git a/skills/generate-mdl/SKILL.md b/skills/generate-mdl/SKILL.md new file mode 100644 index 000000000..682e6ae42 --- /dev/null +++ b/skills/generate-mdl/SKILL.md @@ -0,0 +1,206 @@ +--- +name: generate-mdl +description: Generate a Wren MDL manifest from a database using ibis-server metadata endpoints. Use when a user wants to create or set up a new Wren MDL, scaffold a manifest from an existing database, or onboard a new data source without installing any database drivers locally. +compatibility: Requires a running ibis-server and the Wren MCP server with tools: setup_connection, list_remote_tables, list_remote_constraints, mdl_validate_manifest, mdl_save_project, deploy_manifest +metadata: + author: wren-engine + version: "1.0" +--- + +# Generate Wren MDL + +Generates a Wren MDL manifest by using ibis-server to introspect the database schema — no local database drivers required. All schema discovery goes through ibis-server, which already has drivers for all supported data sources. + +## Workflow + +Follow these steps in order. Do not skip steps or ask unnecessary questions between them. + +### Step 1 — Gather connection info + +Ask the user for: +1. **Data source type** — one of: `POSTGRES`, `MYSQL`, `MSSQL`, `DUCKDB`, `BIGQUERY`, `SNOWFLAKE`, `CLICKHOUSE`, `TRINO`, `ATHENA`, `ORACLE`, `DATABRICKS` +2. **Connection credentials** — see [Connection info format](#connection-info-format) below + +Do not ask for a SQLAlchemy connection string. Use the structured `conn_info` dict instead. + +### Step 2 — Register connection + +``` +setup_connection(datasource=, conn_info=) +``` + +If this fails, report the error and ask the user to correct the credentials. + +### Step 3 — Fetch table schema + +``` +list_remote_tables() +``` + +ibis-server returns a list of tables with their column names and types. Parse the response to understand the full database schema. + +### Step 4 — Fetch relationships + +``` +list_remote_constraints() +``` + +ibis-server returns foreign key constraints. Use these to build `Relationship` entries in the MDL. + +### Step 5 — Sample data (optional) + +For columns where purpose is unclear from the name and type alone: + +``` +query("SELECT * FROM LIMIT 3") +``` + +Note: use the raw table name (not model name) at this stage, since MDL is not yet deployed. + +### Step 6 — Build MDL JSON + +Construct the manifest following the [MDL structure](#mdl-structure) below. + +Rules: +- `catalog`: use `"wren"` unless the user specifies otherwise +- `schema`: use the database's default schema (e.g. `"public"` for PostgreSQL, `"dbo"` for MSSQL) +- `dataSource`: set to the enum value from Step 1 (e.g. `"POSTGRES"`) +- Each table → one `Model`. Set `tableReference.table` to the exact table name +- Each column → one `Column`. Use the exact DB column name +- Mark primary key columns with `"isPrimaryKey": true` and set `primaryKey` on the model +- For FK columns, add a `Relationship` entry linking the two models +- Omit calculated columns for now — they can be added later + +### Step 7 — Validate + +``` +mdl_validate_manifest(mdl=) +``` + +This calls ibis-server's dry-plan endpoint. If validation fails, fix the reported errors and validate again. + +### Step 8 — Save project (optional) + +Ask the user if they want to save the MDL as a YAML project directory (useful for version control). + +If yes, follow the **mdl-project** skill (`skills/mdl-project/SKILL.md`) to write the YAML files. + +### Step 9 — Deploy + +``` +deploy_manifest(mdl=) +``` + +Confirm success to the user. The MDL is now active and queries can run. + +--- + +## MDL Structure + +```json +{ + "catalog": "wren", + "schema": "public", + "dataSource": "POSTGRES", + "models": [ + { + "name": "orders", + "tableReference": { + "catalog": "", + "schema": "public", + "table": "orders" + }, + "columns": [ + { + "name": "order_id", + "type": "INTEGER", + "isCalculated": false, + "notNull": true, + "isPrimaryKey": true, + "properties": {} + }, + { + "name": "customer_id", + "type": "INTEGER", + "isCalculated": false, + "notNull": false, + "properties": {} + }, + { + "name": "total", + "type": "DECIMAL", + "isCalculated": false, + "notNull": false, + "properties": {} + } + ], + "primaryKey": "order_id", + "cached": false, + "properties": {} + } + ], + "relationships": [ + { + "name": "orders_customer", + "models": ["orders", "customers"], + "joinType": "MANY_TO_ONE", + "condition": "orders.customer_id = customers.customer_id" + } + ], + "views": [] +} +``` + +### Column types + +Map SQL/ibis types to MDL type strings: + +| SQL / ibis type | MDL type | +|-----------------|----------| +| INT, INTEGER, INT4 | `INTEGER` | +| BIGINT, INT8 | `BIGINT` | +| SMALLINT, INT2 | `SMALLINT` | +| FLOAT, FLOAT4, REAL | `FLOAT` | +| DOUBLE, FLOAT8 | `DOUBLE` | +| DECIMAL, NUMERIC | `DECIMAL` | +| VARCHAR, TEXT, STRING | `VARCHAR` | +| CHAR | `CHAR` | +| BOOLEAN, BOOL | `BOOLEAN` | +| DATE | `DATE` | +| TIMESTAMP, DATETIME | `TIMESTAMP` | +| TIMESTAMPTZ | `TIMESTAMPTZ` | +| JSON, JSONB | `JSON` | +| ARRAY | `ARRAY` | +| BYTES, BYTEA | `BYTES` | + +When in doubt, use `VARCHAR` as a safe fallback. + +### Relationship join types + +| Cardinality | `joinType` value | +|-------------|-----------------| +| Many-to-one (FK table → PK table) | `MANY_TO_ONE` | +| One-to-many | `ONE_TO_MANY` | +| One-to-one | `ONE_TO_ONE` | +| Many-to-many | `MANY_TO_MANY` | + +--- + +## Connection info format + +Pass to `setup_connection(datasource=..., conn_info={...})`: + +``` +POSTGRES : {"host": "...", "port": "5432", "user": "...", "password": "...", "database": "..."} +MYSQL : {"host": "...", "port": "3306", "user": "...", "password": "...", "database": "..."} +MSSQL : {"host": "...", "port": "1433", "user": "...", "password": "...", "database": "..."} +DUCKDB : {"path": ""} +BIGQUERY : {"project": "...", "dataset": "...", "credentials_base64": "..."} +SNOWFLAKE : {"account": "...", "user": "...", "password": "...", "database": "...", "schema": "..."} +CLICKHOUSE : {"host": "...", "port": "8123", "user": "...", "password": "...", "database": "..."} +TRINO : {"host": "...", "port": "8080", "user": "...", "catalog": "...", "schema": "..."} +ORACLE : {"host": "...", "port": "1521", "user": "...", "password": "...", "database": "..."} +DATABRICKS : {"host": "...", "httpPath": "...", "token": "..."} +``` + +The `datasource` value must match the `dataSource` field in the MDL exactly. diff --git a/skills/mdl-project/SKILL.md b/skills/mdl-project/SKILL.md new file mode 100644 index 000000000..c52c461a3 --- /dev/null +++ b/skills/mdl-project/SKILL.md @@ -0,0 +1,163 @@ +--- +name: mdl-project +description: Save, load, and build Wren MDL manifests as YAML project directories for version control. Use when a user wants to persist an MDL as human-readable YAML files, load a YAML project back into MDL JSON, or compile a YAML project to a deployable mdl.json file. +metadata: + author: wren-engine + version: "1.0" +--- + +# MDL Project + +A Wren MDL project is a directory of YAML files — one file per model — that makes MDL manifests human-readable and version-control friendly (similar to dbt projects). + +YAML files use **snake_case** field names for readability. The compiled `target/mdl.json` uses **camelCase** (the wire format expected by ibis-server). The conversion is documented in [Field mapping](#field-mapping). + +## Project structure + +``` +my_project/ +├── wren_project.yml # Project metadata (catalog, schema, data_source) +├── models/ +│ ├── orders.yml # One file per model +│ ├── customers.yml +│ └── ... +├── relationships.yml # All relationships +└── views.yml # All views +``` + +After building, a compiled file is written to: +``` +my_project/ +└── target/ + └── mdl.json # Deployable MDL JSON (camelCase) +``` + +--- + +## Save MDL JSON → YAML project + +Given an MDL JSON dict (camelCase), write it as a YAML project directory (snake_case): + +### `wren_project.yml` + +```yaml +name: my_project +version: "1.0" +catalog: wren +schema: public +data_source: POSTGRES +``` + +### `models/.yml` + +One file per model. Example for `orders`: + +```yaml +name: orders +table_reference: + catalog: "" + schema: public + table: orders +columns: + - name: order_id + type: INTEGER + is_calculated: false + not_null: true + is_primary_key: true + properties: {} + - name: customer_id + type: INTEGER + is_calculated: false + not_null: false + properties: {} + - name: total + type: DECIMAL + is_calculated: false + not_null: false + properties: {} +primary_key: order_id +cached: false +properties: {} +``` + +### `relationships.yml` + +```yaml +relationships: + - name: orders_customer + models: + - orders + - customers + join_type: MANY_TO_ONE + condition: orders.customer_id = customers.customer_id +``` + +### `views.yml` + +```yaml +views: [] +``` + +--- + +## Load YAML project → MDL JSON + +To assemble a YAML project back into an MDL JSON dict: + +1. Read `wren_project.yml` → extract `catalog`, `schema`, `data_source` +2. Read every file in `models/*.yml` → collect into `models` list +3. Read `relationships.yml` → extract `relationships` list +4. Read `views.yml` → extract `views` list +5. **Rename snake_case keys to camelCase** (see [Field mapping](#field-mapping)) +6. Assemble: + +```json +{ + "catalog": "", + "schema": "", + "dataSource": "", + "models": [...], + "relationships": [...], + "views": [] +} +``` + +--- + +## Build YAML project → `target/mdl.json` + +Same as **Load** above, but write the assembled (camelCase) JSON to `/target/mdl.json` instead of returning it. + +After building, pass the path to `deploy(mdl_file_path="/target/mdl.json")` to activate. + +--- + +## Field mapping + +When converting between YAML (snake_case) and JSON (camelCase): + +| YAML field (snake_case) | JSON field (camelCase) | +|-------------------------|------------------------| +| `data_source` | `dataSource` | +| `table_reference` | `tableReference` | +| `is_calculated` | `isCalculated` | +| `not_null` | `notNull` | +| `is_primary_key` | `isPrimaryKey` | +| `primary_key` | `primaryKey` | +| `join_type` | `joinType` | + +All other fields (`name`, `type`, `catalog`, `schema`, `table`, `condition`, `models`, `columns`, `cached`, `properties`) are the same in both formats. + +--- + +## Typical workflow + +``` +1. Have MDL JSON dict (from generate-mdl skill or manual construction) +2. Save: write wren_project.yml + models/*.yml + relationships.yml + views.yml + (convert camelCase → snake_case) +3. Commit project directory to version control +4. Later — Load: read all YAML files, rename snake_case → camelCase, assemble MDL JSON dict +5. Build: write assembled JSON to target/mdl.json +6. Deploy: deploy(mdl_file_path="./target/mdl.json") +``` From c1176611ddc81f26595413faa3274b2fc9a1ffa2 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 16:36:20 +0800 Subject: [PATCH 13/28] feat(skills): add wren-sql skill with dialect references Adds portable agentskills.io-format skill for Wren SQL generation, including BigQuery dialect, datetime, type, and correction reference docs. Co-Authored-By: Claude Sonnet 4.6 --- skills/AUTHORING.md | 104 ++++++++++++++++++ skills/README.md | 39 +++++++ skills/SKILLS.md | 131 +++++++++++++++++++++++ skills/wren-sql/SKILL.md | 116 ++++++++++++++++++++ skills/wren-sql/references/bigquery.md | 89 +++++++++++++++ skills/wren-sql/references/correction.md | 73 +++++++++++++ skills/wren-sql/references/datetime.md | 60 +++++++++++ skills/wren-sql/references/types.md | 127 ++++++++++++++++++++++ 8 files changed, 739 insertions(+) create mode 100644 skills/AUTHORING.md create mode 100644 skills/README.md create mode 100644 skills/SKILLS.md create mode 100644 skills/wren-sql/SKILL.md create mode 100644 skills/wren-sql/references/bigquery.md create mode 100644 skills/wren-sql/references/correction.md create mode 100644 skills/wren-sql/references/datetime.md create mode 100644 skills/wren-sql/references/types.md diff --git a/skills/AUTHORING.md b/skills/AUTHORING.md new file mode 100644 index 000000000..8a38cb325 --- /dev/null +++ b/skills/AUTHORING.md @@ -0,0 +1,104 @@ +# Skill Authoring Guide + +Skills in this project follow the [Agent Skills](https://agentskills.io/) open format. +Full specification: https://agentskills.io/specification + +--- + +## Directory Structure + +Each skill is a subdirectory containing a required `SKILL.md` and optional supporting directories: + +``` +skill-name/ +├── SKILL.md # Required — frontmatter + workflow instructions +├── references/ # Optional — detail files loaded on demand +│ ├── some-topic.md +│ └── another-topic.md +└── scripts/ # Optional — executable scripts the agent can run +``` + +--- + +## Frontmatter + +Every `SKILL.md` must open with YAML frontmatter: + +```yaml +--- +name: skill-name +description: "What this skill does and when to trigger it. Include specific + trigger keywords. This field is loaded at startup for every conversation." +compatibility: "Optional. Only include if the skill has specific environment + requirements (e.g. requires Docker, must run from ibis-server/)." +metadata: + author: wren-engine + version: "1.0" +--- +``` + +**Rules:** +- `name` must exactly match the parent directory name (lowercase, hyphens only) +- `description` is always loaded — keep it concise and keyword-rich so the agent can match it to user intent + +--- + +## Progressive Disclosure + +Skills load in three tiers. Design content for the tier where it is actually needed: + +| Tier | Content | When loaded | +|------|---------|-------------| +| 1 — Metadata | `name` + `description` (~100 tokens) | Always, at every startup | +| 2 — Instructions | Full `SKILL.md` body | When the skill is activated | +| 3 — Resources | Files in `references/` or `scripts/` | Only when the agent explicitly reads them | + +**Keep `SKILL.md` under 500 lines.** If the body is growing, move reference-only content to `references/`. + +--- + +## What Goes Where + +### Keep in `SKILL.md` +- Step-by-step workflow the agent follows +- Decision criteria and branching logic +- Short commands or invocations the agent needs immediately +- Quick reference tables (file paths, phase mappings, etc.) + +### Move to `references/` +Content that is only needed in certain code paths: +- Output templates (report formats, plan file formats) +- Per-case investigation details (e.g. per-stage debug steps) +- Large lookup tables (connection info examples, error pattern catalogs) +- Anything that would make `SKILL.md` exceed 300 lines + +Link to reference files from `SKILL.md` using paths relative to the skill root: +```markdown +Follow [references/diagnose.md](references/diagnose.md) for per-stage investigation steps. +``` + +When linking from `SKILLS.md` (one level up), prefix with the skill directory name: +```markdown +| [references/diagnose.md](my-skill/references/diagnose.md) | Diagnosis steps | +``` + +--- + +## Naming Conventions + +| Item | Convention | Example | +|------|-----------|---------| +| Skill directory | `kebab-case` | `wren-debugging/` | +| `name` field | same as directory | `wren-debugging` | +| Reference files | descriptive `kebab-case` | `plan-template.md`, `connection-info.md` | + +--- + +## Registration + +After creating a new skill: + +1. Add a section to [SKILLS.md](SKILLS.md) describing the skill, its trigger conditions, and reference files. +2. Add a row to the skills table in [README.md](README.md). + +Both entries should use the same short trigger description. diff --git a/skills/README.md b/skills/README.md new file mode 100644 index 000000000..1aa67b897 --- /dev/null +++ b/skills/README.md @@ -0,0 +1,39 @@ +# Wren Engine Skills + +This directory contains reusable AI agent skills for working with Wren Engine. Skills are instruction files that teach AI agents (Claude, Cline, Cursor, etc.) how to perform specific workflows with Wren's tools and APIs. + +## Installation + +Copy any skill directory into your local Claude skills folder: + +```bash +# macOS / Linux +cp -r skills/generate-mdl ~/.claude/skills/ + +# Or install all skills at once +cp -r skills/* ~/.claude/skills/ +``` + +Once installed, invoke a skill by name in your conversation: + +``` +/generate-mdl +/mdl-project +/wren-sql +``` + +## Available Skills + +| Skill | Description | +|-------|-------------| +| [generate-mdl](generate-mdl/SKILL.md) | Generate a Wren MDL manifest from a live database using ibis-server introspection | +| [mdl-project](mdl-project/SKILL.md) | Save, load, and build MDL manifests as version-controlled YAML project directories | +| [wren-sql](wren-sql/SKILL.md) | Write and correct SQL queries for Wren Engine — types, date/time, BigQuery dialect, error diagnosis | + +See [SKILLS.md](SKILLS.md) for full details on each skill. + +## Requirements + +- A running [ibis-server](../ibis-server/) instance +- The [Wren MCP server](../mcp-server/) connected to your AI client +- An AI client that supports skills (Claude Code, Cline, Cursor, etc.) diff --git a/skills/SKILLS.md b/skills/SKILLS.md new file mode 100644 index 000000000..2674c58a4 --- /dev/null +++ b/skills/SKILLS.md @@ -0,0 +1,131 @@ +# Wren Engine Skill Reference + +Skills are instruction files that extend AI agents with Wren-specific workflows. Install them into your local skills folder and invoke them by name during a conversation. + +--- + +## generate-mdl + +**File:** [generate-mdl/SKILL.md](generate-mdl/SKILL.md) + +Generates a complete Wren MDL manifest by introspecting a live database through ibis-server — no local database drivers required. + +### When to use + +- Onboarding a new data source into Wren +- Scaffolding an MDL from an existing database schema +- Automating initial MDL setup as part of a data pipeline + +### Required MCP tools + +`setup_connection`, `list_remote_tables`, `list_remote_constraints`, `mdl_validate_manifest`, `mdl_save_project`, `deploy_manifest` + +### Supported data sources + +`POSTGRES`, `MYSQL`, `MSSQL`, `DUCKDB`, `BIGQUERY`, `SNOWFLAKE`, `CLICKHOUSE`, `TRINO`, `ATHENA`, `ORACLE`, `DATABRICKS` + +### Workflow summary + +1. Gather connection credentials from the user +2. Register the connection via `setup_connection` +3. Fetch table schema via `list_remote_tables` +4. Fetch foreign key constraints via `list_remote_constraints` +5. Optionally sample data for ambiguous columns +6. Build the MDL JSON (models, columns, relationships) +7. Validate via `mdl_validate_manifest` +8. Optionally save as a YAML project (see `mdl-project`) +9. Deploy via `deploy_manifest` + +--- + +## mdl-project + +**File:** [mdl-project/SKILL.md](mdl-project/SKILL.md) + +Manages Wren MDL manifests as human-readable YAML project directories — similar to dbt projects. Makes MDL version-control friendly by splitting the monolithic JSON into one YAML file per model. + +### When to use + +- Persisting an MDL to disk for version control (Git) +- Loading a saved YAML project back into a deployable MDL JSON +- Compiling a YAML project to `target/mdl.json` for deployment + +### Project layout + +``` +my_project/ +├── wren_project.yml # Catalog, schema, data source +├── models/ +│ ├── orders.yml # One file per model (snake_case fields) +│ └── customers.yml +├── relationships.yml +├── views.yml +└── target/ + └── mdl.json # Compiled output (camelCase, deployable) +``` + +### Key operations + +| Operation | Description | +|-----------|-------------| +| **Save** | Convert MDL JSON → YAML project directory (camelCase → snake_case) | +| **Load** | Read YAML project → assemble MDL JSON dict (snake_case → camelCase) | +| **Build** | Load + write result to `target/mdl.json` | +| **Deploy** | Pass `target/mdl.json` to `deploy(mdl_file_path=...)` | + +### Field mapping (YAML ↔ JSON) + +| YAML (snake_case) | JSON (camelCase) | +|-------------------|------------------| +| `data_source` | `dataSource` | +| `table_reference` | `tableReference` | +| `is_calculated` | `isCalculated` | +| `not_null` | `notNull` | +| `is_primary_key` | `isPrimaryKey` | +| `primary_key` | `primaryKey` | +| `join_type` | `joinType` | + +--- + +## wren-sql + +**File:** [wren-sql/SKILL.md](wren-sql/SKILL.md) + +Comprehensive SQL authoring and debugging guide for Wren Engine. Covers core query rules, filter strategies, supported types, aggregation, and links to topic-specific references. + +### When to use + +- Writing SQL queries against Wren Engine MDL models +- Debugging SQL errors across parsing, planning, transpiling, or execution stages +- Working with complex types (ARRAY, STRUCT, JSON/VARIANT) +- Writing date/time calculations or interval arithmetic +- Targeting BigQuery as a backend database + +### Reference files + +| File | Topic | +|------|-------| +| [references/correction.md](wren-sql/references/correction.md) | Error diagnosis and correction workflow | +| [references/datetime.md](wren-sql/references/datetime.md) | Date/time functions, intervals, epoch conversion | +| [references/types.md](wren-sql/references/types.md) | ARRAY, STRUCT, JSON/VARIANT/OBJECT types | +| [references/bigquery.md](wren-sql/references/bigquery.md) | BigQuery dialect quirks | + +--- + +## Installing a skill + +```bash +# Single skill +cp -r skills/generate-mdl ~/.claude/skills/ + +# All skills +cp -r skills/* ~/.claude/skills/ +``` + +Then invoke in your AI client: + +``` +/generate-mdl +/mdl-project +/wren-sql +``` diff --git a/skills/wren-sql/SKILL.md b/skills/wren-sql/SKILL.md new file mode 100644 index 000000000..891864637 --- /dev/null +++ b/skills/wren-sql/SKILL.md @@ -0,0 +1,116 @@ +--- +name: wren-sql +description: Write and correct SQL queries targeting Wren Engine — covers MDL query rules, filter strategies, data types (ARRAY, STRUCT, JSON/VARIANT), date/time functions, Calculated Fields, BigQuery dialect quirks, and error diagnosis. Use when generating or debugging SQL for any Wren Engine data source. +license: Apache-2.0 +metadata: + author: wren-engine + version: "1.0" +--- + +# Wren SQL + +Wren Engine translates SQL through a semantic layer (MDL — Model Definition Language) before executing it against a backend database. SQL must target MDL model names, not raw database tables. + +For specific topics, load the relevant reference file: + +| Topic | Reference | +|-------|-----------| +| SQL error diagnosis and correction | [references/correction.md](references/correction.md) | +| Date/time functions and intervals | [references/datetime.md](references/datetime.md) | +| ARRAY, STRUCT, JSON/VARIANT types | [references/types.md](references/types.md) | +| BigQuery dialect quirks | [references/bigquery.md](references/bigquery.md) | + +--- + +## Context + +- You are querying a **semantic layer**, not a database directly. +- Only use model/view/column names defined in the MDL — never raw database table references. +- Wren Engine uses a generic SQL dialect similar to ANSI SQL (DataFusion/Postgres/DuckDB), but with differences. +- Check the `dataSource` field to identify the backend and apply dialect-specific rules if needed. + +--- + +## Core SQL Rules + +- Only `SELECT` statements. No `DELETE`, `UPDATE`, `INSERT`. +- Only use tables and columns from the MDL schema. +- Do not include comments in generated SQL. +- Prefer CTEs over subqueries. +- Identifiers are **case-sensitive**. Quote identifiers containing unicode, special characters (except `_`), or starting with a digit using double quotes. + - Examples: `"客户"."姓名"`, `"table-name"."col"`, `"123column"` +- Identifier quotes: `"` (double quotes). String literal quotes: `'` (single quotes). +- For specific date queries, use a range: + ```sql + WHERE ts >= CAST('2024-11-01 00:00:00' AS TIMESTAMP WITH TIME ZONE) + AND ts < CAST('2024-11-02 00:00:00' AS TIMESTAMP WITH TIME ZONE) + ``` +- For ranking, use `DENSE_RANK()` + `WHERE`. Include the ranking column in `SELECT`. +- Avoid correlated subqueries — use JOINs instead. +- Use `SAFE_CAST` when casting might fail: `SAFE_CAST(col AS INT)` + +--- + +## Filter Strategies + +| Column type | Strategy | +|-------------|----------| +| Text | `LIKE '%value%'` for partial match | +| Numeric | `BETWEEN 30 AND 40` | +| Date/Timestamp | `>= '2024-01-01' AND < '2024-02-01'` | +| Exact value | `=` or `IN (...)` | +| Primary key / indexed | Prefer equality (`=`) | + +--- + +## Supported Cast Types + +`bool`, `boolean`, `int`, `integer`, `bigint`, `smallint`, `tinyint`, `float`, `double`, `real`, `decimal`, `numeric`, `varchar`, `char`, `string`, `text`, `date`, `time`, `timestamp`, `timestamp with time zone`, `bytea` + +Example: `CAST(col AS INT)`, `TIMESTAMP '2024-11-09 00:00:00'` + +--- + +## Aggregation + +- All non-aggregated `SELECT` columns must appear in `GROUP BY` (window functions excepted). +- Aggregate conditions go in `HAVING`, not `WHERE`. +- Prefer ordinal `GROUP BY` for long column names: + ```sql + SELECT very_long_column_name AS alias, COUNT(*) FROM t GROUP BY 1 + ``` + +--- + +## Sorting and Limiting + +- `ORDER BY` for sort; `LIMIT` to restrict rows. +- When `ORDER BY` appears in a subquery or CTE, always include `LIMIT`. + +--- + +## Subquery Patterns + +- Prefer CTEs (`WITH` clause) over nested subqueries. +- Subquery in `SELECT` must return a single value per row. +- Subquery in `WHERE`: use `IN`, `EXISTS`, or comparison operators. +- `IN SUBQUERY` in `JOIN` conditions is not supported — use `JOIN ... ON` instead. +- `RECURSIVE` CTEs are not supported. + +--- + +## Calculated Fields + +Columns marked as **Calculated Field** in the MDL have pre-defined computation logic. Use them directly instead of re-implementing the calculation. + +Read the column comment (e.g., `column expression: avg(reviews.Score)`) to understand what the field represents. + +```sql +-- Schema has: Rating DOUBLE (Calculated Field: avg(reviews.Score)) +-- ReviewCount BIGINT (Calculated Field: count(reviews.Id)) + +-- Correct — use Calculated Fields directly: +SELECT AVG(Rating) FROM orders WHERE ReviewCount > 10 + +-- Incorrect — do not re-join and re-aggregate manually +``` diff --git a/skills/wren-sql/references/bigquery.md b/skills/wren-sql/references/bigquery.md new file mode 100644 index 000000000..22052a2ad --- /dev/null +++ b/skills/wren-sql/references/bigquery.md @@ -0,0 +1,89 @@ +# BigQuery Dialect Rules + +Apply these rules when generating or correcting Wren SQL with BigQuery as the backend database. + +## TIMESTAMP + INTERVAL with MONTH / YEAR + +`TIMESTAMP WITH TIME ZONE` cannot use `+`/`-` with INTERVAL containing non-zero MONTH or YEAR parts. + +```sql +-- Valid: +timestamp_col + INTERVAL '7' days + +-- Invalid: +timestamp_col + INTERVAL '1' month +timestamp_col - INTERVAL '2' year + +-- Fix — cast to TIMESTAMP first: +CAST(timestamp_col AS TIMESTAMP) + INTERVAL '1' month +``` + +## STRING vs NUMERIC Comparison + +`STRING` cannot be compared with `INTEGER` or `FLOAT` directly. Use `SAFE_CAST` or `CAST`: + +```sql +SAFE_CAST(string_col AS INT) > 100 +CAST(string_col AS FLOAT) <> 75.5 +``` + +## Parsing String to Timestamp + +```sql +PARSE_DATETIME('%Y-%m-%d', string_col) -- timestamp (no timezone) +PARSE_TIMESTAMP('%Y-%m-%d %H:%M:%S', string_col) -- timestamp with timezone +``` + +## GROUP BY Alias + +BigQuery does not allow `GROUP BY` to reference aliases defined in `SELECT`. + +```sql +-- Invalid: +SELECT col1 AS alias1, COUNT(*) FROM t GROUP BY alias1 + +-- Valid: +SELECT col1 AS alias1, COUNT(*) FROM t GROUP BY col1 + +-- Preferred for long names: +SELECT very_long_column AS alias, COUNT(*) FROM t GROUP BY 1 +``` + +## Column Name Same as Table Name + +If a column name matches the table name (case-insensitive), rename with an alias to avoid ambiguity. + +```sql +-- Ambiguous: +SELECT "User".user FROM "User" + +-- Clear: +SELECT "User".user AS user_column FROM "User" +``` + +## Date/Time Diff Functions — Argument Order + +`DATE_DIFF`, `TIMESTAMP_DIFF`, `DATETIME_DIFF`, `TIME_DIFF` take arguments as `(part, start, end)`: + +```sql +DATE_DIFF('day', start_date, end_date) +TIMESTAMP_DIFF('hour', start_ts, end_ts) +``` + +## TIMESTAMP_DIFF with YEAR / MONTH + +`TIMESTAMP_DIFF` does not support `year` or `month` date parts for TIMESTAMP arguments. Cast to DATE first: + +```sql +-- Invalid: +TIMESTAMP_DIFF('year', ts1, ts2) + +-- Fix: +TIMESTAMP_DIFF('year', CAST(ts1 AS DATE), CAST(ts2 AS DATE)) +``` + +## DATETIME vs TIMESTAMP + +- `DATETIME` — local time, no timezone +- `TIMESTAMP` — UTC-based, no timezone in display +- `TIMESTAMP WITH TIME ZONE` — use when timezone awareness is required diff --git a/skills/wren-sql/references/correction.md b/skills/wren-sql/references/correction.md new file mode 100644 index 000000000..0028dc641 --- /dev/null +++ b/skills/wren-sql/references/correction.md @@ -0,0 +1,73 @@ +# SQL Error Diagnosis and Correction + +Wren Engine processes SQL through multiple stages. Errors include a `phase` field and sometimes a `dialectSql` field showing which SQL layer failed. + +## Pipeline Stages + +1. **Parsing** — Wren SQL → AST +2. **Planning** — AST → IR (subqueries generated per model definition) +3. **Unparsing** — IR → generic SQL (DataFusion dialect) +4. **Transpile** — generic SQL → target database dialect +5. **Execution** — transpiled SQL runs against the target database + +**Three SQL layers:** +- **Wren SQL** — user-submitted SQL (only modify this one) +- **Planned SQL** — after planning/unparsing +- **Dialect SQL** — after transpiling, database-specific + +**Example error response:** +```json +{ + "phase": "SQL_EXECUTION", + "metadata": { "dialectSql": "SELECT CURRENT_TIMESTAMP() - INTERVAL '1' MONTH" }, + "message": "TIMESTAMP +/- INTERVAL is not supported for intervals with non-zero MONTH or YEAR part." +} +``` + +--- + +## Step 1 — Identify the Error Phase + +| Phase | Cause | Fix | +|-------|-------|-----| +| **Parsing error** | Wren SQL has syntax issues | Fix SQL syntax | +| **Planning error** | Unsupported construct or missing model/column | Verify names match MDL exactly | +| **Transpiling error** | Internal bug (`SQLGLOT_ERROR`, `GENERIC_INTERNAL_ERROR`) | Report to Wren support | +| **Execution error** | Data incompatibility with target DB | See below | + +**Execution error sub-cases:** +- Not-found in `__source` (deepest subquery) → model definition out of sync with DB schema → **user error**, not a bug +- Not-found in main/joined query → **internal error**, report to Wren support +- Error during execution but not dry-run → data issue (NULLs, type mismatches, runtime constraints) +- Timeout / resource limit → `EXTERNAL ERROR`; simplify query or check DB performance + +--- + +## Step 2 — Modify the Wren SQL + +- Only modify the **Wren SQL**. Never modify Planned SQL or Dialect SQL directly. +- Think about what change to the Wren SQL would produce the correct Planned/Dialect SQL. +- Use alternative constructs if direct SQL triggers a known unsupported feature. + +--- + +## Step 3 — Test + +Resubmit the modified Wren SQL. Repeat until execution succeeds. + +--- + +## Step 4 — Validate Results + +- Always dry-run first, then execute. +- Verify results meet expected outcomes. Refine if incorrect. + +--- + +## Step 5 — Report Bugs (Last Resort) + +First, find an alternative Wren SQL that achieves the same result without triggering the bug. + +Only report a bug if no alternative exists AND the SQL is reasonable and should work: +- `GENERIC_INTERNAL_ERROR` or `SQLGLOT_ERROR` → report as **internal error** to Wren support +- Timeout / resource limit with no alternative → report as **external error** to the user diff --git a/skills/wren-sql/references/datetime.md b/skills/wren-sql/references/datetime.md new file mode 100644 index 000000000..28c74046e --- /dev/null +++ b/skills/wren-sql/references/datetime.md @@ -0,0 +1,60 @@ +# Date and Time Functions + +## Current Values + +```sql +CURRENT_DATE -- current date +CURRENT_TIMESTAMP -- current timestamp +``` + +## Truncation + +```sql +DATE_TRUNC('', ) +``` + +Parts: `'year'`, `'quarter'`, `'month'`, `'week'`, `'day'`, `'hour'`, `'minute'`, `'second'` + +## Extraction + +```sql +EXTRACT( FROM ) +``` + +Parts: `year`, `quarter`, `month`, `week`, `day`, `hour`, `minute`, `second` + +## Date Difference + +```sql +DATE_DIFF('', , ) +``` + +Parts: `'year'`, `'quarter'`, `'month'`, `'week'`, `'day'`, `'hour'`, `'minute'`, `'second'` + +## Interval Arithmetic + +```sql + + INTERVAL '7' days + - INTERVAL '3' hours +``` + +## Timezone + +- Use `TIMESTAMP WITH TIME ZONE` when timezone matters. +- `TIMESTAMP` and `TIMESTAMP WITH TIME ZONE` cannot be compared directly — cast one to the other first: + ```sql + CAST( AS TIMESTAMP WITH TIME ZONE) + CAST( AS TIMESTAMP) + ``` + +## Epoch / Unix Time Conversion + +If a column stores dates/timestamps as integers, convert using: + +```sql +to_timestamp() -- seconds +to_timestamp_millis() -- milliseconds +to_timestamp_micros() -- microseconds +to_timestamp_nanos() -- nanoseconds +to_timestamp_seconds() -- seconds (explicit) +``` diff --git a/skills/wren-sql/references/types.md b/skills/wren-sql/references/types.md new file mode 100644 index 000000000..9c4662ed1 --- /dev/null +++ b/skills/wren-sql/references/types.md @@ -0,0 +1,127 @@ +# Complex Data Types + +## ARRAY + +### Literals + +```sql +ARRAY[, , ..., ] +['value1', 'value2', ..., 'valueN'] +CAST(ARRAY[, ..., ] AS ARRAY) +CAST(['value1', ..., 'valueN'] AS []) +``` + +### Type Definition + +```sql +ARRAY +[] +``` + +### UNNEST — same table + +```sql +SELECT ... +FROM , UNNEST() AS () +``` + +### UNNEST — join with another table + +```sql +SELECT ... +FROM +JOIN , UNNEST(.) AS () + ON .= . +``` + +--- + +## STRUCT + +### Type Definition + +```sql +STRUCT +``` + +### Accessing Fields + +Use dot notation: + +```sql +. + +-- Example: +SELECT address.city, address.postcode FROM users +``` + +--- + +## Semi-Structured Types (JSON / VARIANT / OBJECT) + +### Type Definitions + +`JSON`, `VARIANT`, `OBJECT` — no fixed schema. Don't assume specific fields unless confirmed via column metadata, comments, or sample data. Account for missing fields or varying structure across rows. + +### Accessing Fields + +Do **not** use dot notation. Use `GET_PATH`: + +```sql +GET_PATH(, '') +``` + +### Type Conversion After GET_PATH + +Do **not** use `CAST` directly on semi-structured values. Use type-specific functions: + +```sql +AS_BOOLEAN(GET_PATH(...)) -- boolean +AS_DOUBLE(GET_PATH(...)) -- double/float +AS_INTEGER(GET_PATH(...)) -- bigint +AS_VARCHAR(GET_PATH(...)) -- varchar + +-- Example: +AS_VARCHAR(GET_PATH(address_col, '$.city')) +``` + +### Creating Semi-Structured Literals + +```sql +parse_json('{"field1": "value1", "field2": 123}') +``` + +### UNNEST Arrays Inside Semi-Structured Data + +```sql +SELECT item +FROM , + UNNEST(AS_ARRAY(GET_PATH(
., ''))) AS unnest_alias(item) +``` + +### JSON Type Rules (from column metadata) + +If column metadata specifies `json_type`: + +| `json_type` value | How to access | +|-------------------|---------------| +| `JSON` | `GET_PATH(col, '$.field')` | +| `JSON_ARRAY` | `AS_ARRAY(GET_PATH(col, '$.field'))` | +| *(empty)* | Direct column reference, no `GET_PATH` | + +Follow the `path` property in metadata to build the correct path expression. + +### Example + +Schema metadata: +```sql +-- {"json_type":"JSON","json_fields":{"address.json.city":{"path":"$.city","type":"varchar"}}} +address JSON +``` + +Query: +```sql +SELECT AS_VARCHAR(GET_PATH(u.address, '$.city')) FROM users AS u +``` + +> Complex queries on semi-structured columns are slower than on structured columns. Optimize accordingly. From e4300ee9de92b8d76038875248f9f55ec4b3df37 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 16:37:03 +0800 Subject: [PATCH 14/28] perf(ibis-server): optimize Docker build from 66 min to ~5-8 min - Split Rust wheel build into two strategies via WHEEL_SOURCE build arg: - local: maturin+zig cross-compiles Linux wheel on host (reuses cargo cache) - docker: BuildKit cache mounts for incremental in-Docker compilation - just docker-build auto-selects local path when platform matches host, falls back to docker path for cross-platform/multi-arch builds - Remove redundant runtime poetry install (was being overwritten anyway) - Move jupyter deps to builder stage to fix silent install bug - Combine all apt-get layers in runtime stage into one - Add platform parameter to docker-build for multi-arch support - Document build strategies and prerequisites in README Co-Authored-By: Claude Sonnet 4.6 --- ibis-server/Dockerfile | 94 +++++++++++++++++++++++--------------- ibis-server/README.md | 28 ++++++++++++ ibis-server/justfile | 43 +++++++++++++++-- wren-core-py/.dockerignore | 1 + 4 files changed, 123 insertions(+), 43 deletions(-) diff --git a/ibis-server/Dockerfile b/ibis-server/Dockerfile index bf25d539d..1a5b93ce0 100644 --- a/ibis-server/Dockerfile +++ b/ibis-server/Dockerfile @@ -1,20 +1,49 @@ -FROM python:3.11-bookworm AS builder +# WHEEL_SOURCE=docker (default): build Rust inside Docker with BuildKit cargo cache +# WHEEL_SOURCE=local: use pre-built Linux wheel from host (maturin + zig required) +ARG WHEEL_SOURCE=docker -ARG ENV -ENV ENV=$ENV +# ── Option A: Build Rust wheel inside Docker ────────────────────────────────── +FROM python:3.11-bookworm AS wheel-docker +RUN apt-get update && apt-get -y install libpq-dev && rm -rf /var/lib/apt/lists/* -# libpq-dev is required for psycopg2 -RUN apt-get update && apt-get -y install libpq-dev - -# Install rust RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | bash -s -- -y ENV PATH="/root/.cargo/bin:$PATH" -# Install justfile -RUN curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to /usr/bin +RUN pip install maturin + +COPY --from=wren-core-py . /wren-core-py +COPY --from=wren-core . /wren-core +COPY --from=wren-core-base . /wren-core-base + +WORKDIR /wren-core-py + +# Cache cargo registry and target/ between builds for incremental recompilation +RUN --mount=type=cache,target=/root/.cargo/registry \ + --mount=type=cache,target=/root/.cargo/git \ + --mount=type=cache,target=/wren-core-py/target \ + maturin build --release && \ + mkdir /wheel && cp target/wheels/wren_core_py-*.whl /wheel/ + + +# ── Option B: Use locally cross-compiled Linux wheel ───────────────────────── +FROM python:3.11-bookworm AS wheel-local +COPY --from=wren-core-py target/wheels/ /tmp/wheels/ +RUN mkdir /wheel && cp /tmp/wheels/wren_core_py-*linux*.whl /wheel/ + + +# ── Select wheel source ─────────────────────────────────────────────────────── +FROM wheel-${WHEEL_SOURCE} AS wheel-final + + +# ── Python builder ──────────────────────────────────────────────────────────── +FROM python:3.11-bookworm AS builder + +ARG ENV +ENV ENV=$ENV + +RUN apt-get update && apt-get -y install libpq-dev && rm -rf /var/lib/apt/lists/* - # python ENV PYTHONUNBUFFERED=1 \ PYTHONDONTWRITEBYTECODE=1 \ # pip @@ -28,35 +57,33 @@ ENV PYTHONUNBUFFERED=1 \ RUN pip install poetry==1.8.3 -COPY --from=wren-core-py . /wren-core-py -COPY --from=wren-core . /wren-core -COPY --from=wren-core-base . /wren-core-base - WORKDIR /app -COPY . . -RUN just install --without dev -# Install MCP server dependencies into the venv +# Copy dependency files first so this layer is cached when only source changes +COPY pyproject.toml poetry.lock ./ +RUN poetry install --without dev --with jupyter --no-root + +# Install wheel (either from host or from wheel-docker stage) +COPY --from=wheel-final /wheel/ /tmp/wheels/ +RUN poetry run pip install /tmp/wheels/wren_core_py-*.whl + +# Install MCP server dependencies RUN poetry run pip install "mcp[cli]>=1.19.0,<2" +COPY . . + +# ── Runtime ─────────────────────────────────────────────────────────────────── FROM python:3.11-slim-bookworm AS runtime -# Add microsoft package list +# Install all system dependencies in a single layer RUN apt-get update \ && apt-get install -y curl gnupg \ && curl -sSL https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor -o /usr/share/keyrings/microsoft.gpg \ && echo "deb [arch=amd64,arm64,armhf signed-by=/usr/share/keyrings/microsoft.gpg] https://packages.microsoft.com/debian/12/prod bookworm main" | tee /etc/apt/sources.list.d/mssql-release.list \ - && apt-get update - -# Install msodbcsql 18 driver for mssql -RUN ACCEPT_EULA=Y apt-get -y install unixodbc-dev msodbcsql18 - -# Install libmysqlclient-dev for mysql -RUN apt-get install -y default-libmysqlclient-dev - -# libpq-dev is required for psycopg2 -RUN apt-get -y install libpq-dev \ + && apt-get update \ + && ACCEPT_EULA=Y apt-get install -y unixodbc-dev msodbcsql18 \ + && apt-get install -y default-libmysqlclient-dev libpq-dev \ && rm -rf /var/lib/apt/lists/* ENV VIRTUAL_ENV=/app/.venv \ @@ -65,10 +92,8 @@ ENV VIRTUAL_ENV=/app/.venv \ REMOTE_FUNCTION_LIST_PATH=/app/resources/function_list \ REMOTE_WHITE_FUNCTION_LIST_PATH=/app/resources/white_function_list -# Set working directory before copying files WORKDIR /app -COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} COPY app ./app/ COPY resources ./resources/ COPY notebooks ./notebooks/ @@ -78,16 +103,9 @@ COPY wren/ ./wren/ COPY --from=mcp-server ./app /mcp-server/app/ COPY --from=mcp-server ./mdl.schema.json /mcp-server/ -# Copy pyproject.toml and poetry.lock for poetry to work COPY pyproject.toml poetry.lock ./ -# Install poetry in runtime stage -RUN pip install poetry==1.8.3 - -# Install jupyter dependencies -RUN poetry install --without dev --with jupyter - -# runtime +# Copy venv from builder (includes all deps + jupyter) COPY --from=builder /app/.venv ${VIRTUAL_ENV} # Install Opentelemetry zero-instrumentation python diff --git a/ibis-server/README.md b/ibis-server/README.md index b7b16922c..37863040f 100644 --- a/ibis-server/README.md +++ b/ibis-server/README.md @@ -3,6 +3,34 @@ This module is the API server of Wren Engine. It's built on top of [FastAPI](htt ## Quick Start +### Building the Docker Image + +```bash +just docker-build # current platform (fast: Rust built locally) +just docker-build linux/amd64 # single platform +just docker-build linux/amd64,linux/arm64 --push # multi-arch (requires --push) +``` + +#### Build strategies + +| Scenario | Rust compilation | Speed | +|---|---|---| +| Target matches host platform | Built locally via `maturin + zig` | Fast (reuses host cargo cache) | +| Cross-platform or multi-arch | Built inside Docker via BuildKit cache mounts | Slow on first build, incremental after | + +**Local build prerequisites** (single-platform matching your host): +```bash +brew install zig +rustup target add aarch64-unknown-linux-gnu # Apple Silicon +rustup target add x86_64-unknown-linux-gnu # Intel Mac +``` + +Once set up, only the first build is slow. Subsequent builds reuse the host cargo cache and take a few minutes. + +> **Note**: Multi-arch builds (`linux/amd64,linux/arm64`) always build Rust inside Docker and require `--push` to export the image (Docker cannot load multi-arch images locally). + +--- + ### Running Ibis Server on Docker You can follow the steps below to run the Java engine and ibis. > Wren Engine is migrating to [wren-core](../wren-core/). However, we still recommend starting [the Java engine](../wren-core-legacy/) to enable the query fallback mechanism. diff --git a/ibis-server/justfile b/ibis-server/justfile index d19273eb0..da83859c2 100644 --- a/ibis-server/justfile +++ b/ibis-server/justfile @@ -54,15 +54,48 @@ test MARKER: test-verbose MARKER: poetry run pytest -s -v -m '{{ MARKER }}' -image-name := "ghcr.io/canner/wren-engine-ibis:latest" - -docker-build: - docker buildx build -t {{ image-name }} -f Dockerfile \ +image-name := "ghcr.io/canner/wren-engine-ibis:mcp-1" + +# Build image. platform: ""=current, linux/amd64, linux/arm64, linux/amd64,linux/arm64 (multi-arch needs --push) +# Single-platform matching current host: Rust built locally via maturin+zig (requires: brew install zig) +# Multi-arch or cross-platform: Rust built inside Docker with BuildKit cargo cache +docker-build platform="" *args="": + #!/usr/bin/env bash + set -euo pipefail + + host_arch=$(uname -m) + host_platform=$([ "$host_arch" = "arm64" ] && echo "linux/arm64" || echo "linux/amd64") + rust_target=$([ "$host_arch" = "arm64" ] && echo "aarch64-unknown-linux-gnu" || echo "x86_64-unknown-linux-gnu") + + build_platform="{{ platform }}" + [ -z "$build_platform" ] && build_platform="$host_platform" + + platform_arg="" + [ -n "{{ platform }}" ] && platform_arg="--platform {{ platform }}" + + wheel_source="docker" + if [ "$build_platform" = "$host_platform" ]; then + echo "→ Building Rust wheel locally ($rust_target via maturin+zig)" + cd ../wren-core-py + just install + python_ver=$(poetry run python -c "import sys; print(f'python{sys.version_info.major}.{sys.version_info.minor}')") + poetry run maturin build --release --target "$rust_target" --zig -i "$python_ver" --out target/wheels/ + cd - + wheel_source="local" + else + echo "→ Building Rust wheel inside Docker (target: $build_platform)" + fi + + docker buildx build \ + $platform_arg \ + --build-arg WHEEL_SOURCE="$wheel_source" \ + -t {{ image-name }} \ + -f Dockerfile \ --build-context wren-core-py=../wren-core-py \ --build-context wren-core=../wren-core \ --build-context wren-core-base=../wren-core-base \ --build-context mcp-server=../mcp-server \ - . + {{ args }} . docker-run: docker run -it --rm -p 8000:8000 --env-file .env {{ image-name }} diff --git a/wren-core-py/.dockerignore b/wren-core-py/.dockerignore index 04c3ff49d..05548ce2a 100644 --- a/wren-core-py/.dockerignore +++ b/wren-core-py/.dockerignore @@ -1,5 +1,6 @@ .venv target +!target/wheels tests README.md rustfmt.toml From c9fbb9e666195bc37567cda3f48bdf2ed36de94b Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 16:44:45 +0800 Subject: [PATCH 15/28] fix(mcp-server): use camelCase manifestStr for ibis-server dry-plan API The /v3/connector/dry-plan endpoint expects `manifestStr` not `manifest_str`. Co-Authored-By: Claude Sonnet 4.6 --- mcp-server/app/wren.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mcp-server/app/wren.py b/mcp-server/app/wren.py index 14cb24bb2..d6c2f99c2 100644 --- a/mcp-server/app/wren.py +++ b/mcp-server/app/wren.py @@ -309,7 +309,7 @@ async def mdl_validate_manifest(mdl: dict) -> str: async with httpx.AsyncClient(timeout=30.0) as client: resp = await client.post( f"{ibis_url.rstrip('/')}/v3/connector/dry-plan", - json={"manifest_str": manifest_str, "sql": "SELECT 1"}, + json={"manifestStr": manifest_str, "sql": "SELECT 1"}, headers={"x-wren-fallback_disable": "true"}, ) if resp.status_code == 200: From fd906c3890a4a16941f7850d8be629780086bfb6 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 16:46:18 +0800 Subject: [PATCH 16/28] add .gitkeep for mcp workspace --- mcp-server/workspace/.gitkeep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 mcp-server/workspace/.gitkeep diff --git a/mcp-server/workspace/.gitkeep b/mcp-server/workspace/.gitkeep new file mode 100644 index 000000000..e69de29bb From e2b9b5c6e4bc3183f77d56f08b15c09d5ff201b4 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 18:00:50 +0800 Subject: [PATCH 17/28] remove unused etc --- mcp-server/etc/config.properties | 2 -- 1 file changed, 2 deletions(-) delete mode 100644 mcp-server/etc/config.properties diff --git a/mcp-server/etc/config.properties b/mcp-server/etc/config.properties deleted file mode 100644 index 41f2f3a85..000000000 --- a/mcp-server/etc/config.properties +++ /dev/null @@ -1,2 +0,0 @@ -node.environment=test -http-server.http.port=8080 \ No newline at end of file From d0902ab53408387c8d93574ef75c33807b02c415 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 18:46:06 +0800 Subject: [PATCH 18/28] enhance health check --- mcp-server/app/wren.py | 53 ++++++++++++++++++++++++++++++++++++++---- 1 file changed, 49 insertions(+), 4 deletions(-) diff --git a/mcp-server/app/wren.py b/mcp-server/app/wren.py index d6c2f99c2..f66c3def4 100644 --- a/mcp-server/app/wren.py +++ b/mcp-server/app/wren.py @@ -647,16 +647,61 @@ async def get_wren_guide() -> str: ) async def health_check() -> str: """ - Check the health of Wren Engine + Check the health of Wren Engine and validate required configuration. + + Returns a detailed status report including any missing configuration + with actionable guidance on how to fix each issue. """ + issues: list[str] = [] + guidance: list[str] = [] + + # Check MDL deployment + if not mdl_cache.is_deployed(): + issues.append("MDL is not deployed") + guidance.append( + "- Deploy MDL: call `deploy(mdl_file_path=)` with a path to your MDL JSON file, " + "or `deploy_manifest(mdl=)` if the manifest is already in memory." + ) + + # Check connection info + if connection_info is None: + issues.append("Database connection is not configured") + guidance.append( + "- Configure connection: call `setup_connection(datasource=, conn_info={...})` " + "with your data source type (e.g. POSTGRES, BIGQUERY, DUCKDB) and credentials." + ) + + # Check data source + if data_source is None: + issues.append("Data source type is not set") + guidance.append( + "- Set data source: deploy an MDL that has a `dataSource` field, " + "or call `setup_connection(datasource=, conn_info={...})`." + ) + + if issues: + lines = ["Wren Engine is not ready. The following issues must be resolved:\n"] + for i, issue in enumerate(issues, 1): + lines.append(f"{i}. {issue}") + lines.append("\nHow to fix:") + lines.extend(guidance) + return "\n".join(lines) + + # All config present — verify ibis-server connectivity try: response = await make_query_request("SELECT 1") if response.status_code == 200: - return "Wren Engine is healthy" + return f"Wren Engine is healthy (data source: {data_source}, MDL deployed: yes)" else: - return "Wren Engine is not healthy" + return ( + f"Wren Engine configuration is complete but ibis-server returned an error " + f"(HTTP {response.status_code}). Ensure ibis-server is running at {WREN_URL}." + ) except Exception as e: - return "Wren Engine is not healthy" + return ( + f"Wren Engine configuration is complete but ibis-server is unreachable at {WREN_URL}. " + f"Ensure ibis-server is running. Error: {e}" + ) if __name__ == "__main__": From 42d5a37980b13accc684912849726107648d3a44 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 18:46:58 +0800 Subject: [PATCH 19/28] add requrie env for docker compose --- mcp-server/docker/compose.yaml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mcp-server/docker/compose.yaml b/mcp-server/docker/compose.yaml index 9f1e7cd34..f65057cfc 100644 --- a/mcp-server/docker/compose.yaml +++ b/mcp-server/docker/compose.yaml @@ -6,7 +6,9 @@ services: - "9000:9000" environment: ENABLE_MCP_SERVER: "true" + MCP_TRANSPORT: "streamable-http" + MCP_HOST: "0.0.0.0" MCP_PORT: "9000" + WREN_URL: "localhost:8000" volumes: - - ./../etc:/usr/src/app/etc - ${MDL_WORKSPACE:-../workspace}:/workspace From 1ced13ae8d4483c5618aa9c3696d3d3bdc4efaa1 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 18:47:37 +0800 Subject: [PATCH 20/28] feat(skills): add wren-quickstart skill for Docker MCP setup MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Step-by-step workflow for spinning up Wren Engine via Docker compose and connecting Claude Code (or Cline/Cursor) over streamable-http, including workspace mounting and localhost→host.docker.internal fix. Co-Authored-By: Claude Sonnet 4.6 --- skills/README.md | 1 + skills/SKILLS.md | 24 ++++ skills/wren-quickstart/SKILL.md | 209 ++++++++++++++++++++++++++++++++ 3 files changed, 234 insertions(+) create mode 100644 skills/wren-quickstart/SKILL.md diff --git a/skills/README.md b/skills/README.md index 1aa67b897..9d363da8e 100644 --- a/skills/README.md +++ b/skills/README.md @@ -29,6 +29,7 @@ Once installed, invoke a skill by name in your conversation: | [generate-mdl](generate-mdl/SKILL.md) | Generate a Wren MDL manifest from a live database using ibis-server introspection | | [mdl-project](mdl-project/SKILL.md) | Save, load, and build MDL manifests as version-controlled YAML project directories | | [wren-sql](wren-sql/SKILL.md) | Write and correct SQL queries for Wren Engine — types, date/time, BigQuery dialect, error diagnosis | +| [wren-quickstart](wren-quickstart/SKILL.md) | Set up Wren Engine MCP via Docker and connect to Claude Code or other MCP clients | See [SKILLS.md](SKILLS.md) for full details on each skill. diff --git a/skills/SKILLS.md b/skills/SKILLS.md index 2674c58a4..861b98265 100644 --- a/skills/SKILLS.md +++ b/skills/SKILLS.md @@ -112,6 +112,30 @@ Comprehensive SQL authoring and debugging guide for Wren Engine. Covers core que --- +## wren-quickstart + +**File:** [wren-quickstart/SKILL.md](wren-quickstart/SKILL.md) + +Sets up Wren Engine MCP server via Docker and connects it to Claude Code (or another MCP client) over streamable-http. + +### When to use + +- Running Wren MCP in Docker (no local Python/uv install required) +- Configuring Claude Code MCP to connect to a containerized Wren Engine +- Setting up Wren MCP for Cline, Cursor, or VS Code MCP Extension +- Fixing `localhost` → `host.docker.internal` in connection info for Docker + +### Workflow summary + +1. Ask user for workspace mount path +2. Create `docker/.env` with `MDL_WORKSPACE` +3. `docker compose up -d` +4. Rewrite `localhost` → `host.docker.internal` in connection credentials +5. Add `wren` MCP server to Claude Code using streamable-http on port 9000 +6. Run `health_check()` to verify + +--- + ## Installing a skill ```bash diff --git a/skills/wren-quickstart/SKILL.md b/skills/wren-quickstart/SKILL.md new file mode 100644 index 000000000..65386a1b5 --- /dev/null +++ b/skills/wren-quickstart/SKILL.md @@ -0,0 +1,209 @@ +--- +name: wren-quickstart +description: Set up Wren Engine MCP server via Docker. Covers pulling the Docker image, configuring the compose file, mounting a workspace, fixing localhost → host.docker.internal for connection info, and registering the MCP server in Claude Code (or other MCP clients) using streamable-http transport. Trigger when a user wants to run Wren MCP in Docker, configure Claude Code MCP, or connect an AI client to a Dockerized Wren Engine. +compatibility: Requires Docker Desktop (or Docker Engine) with BuildKit enabled. +metadata: + author: wren-engine + version: "1.0" +--- + +# Set Up Wren MCP via Docker + +Runs the Wren Engine ibis-server + MCP server together in a single Docker container and connects it to Claude Code (or another MCP client) over streamable-http. + +--- + +## Step 1 — Ask for workspace path + +Ask the user: + +> What directory on your host machine should be mounted as the MCP workspace? +> This is where MDL files and YAML project directories will be read and written. +> (Default: `mcp-server/workspace` inside the wren-engine repo) + +If the user has no preference, use `../workspace` relative to the `docker/` directory (i.e. `mcp-server/workspace`). + +Save the answer as `` for use in the next steps. + +--- + +## Step 2 — Create the compose env file + +Create `mcp-server/docker/.env` with: + +``` +MDL_WORKSPACE= +``` + +Example: +``` +MDL_WORKSPACE=/Users/me/my-mdl-files +``` + +If the user accepted the default, no `.env` file is needed — the compose default (`../workspace`) already points to `mcp-server/workspace/`. + +--- + +## Step 3 — Start the container + +```bash +cd mcp-server/docker +docker compose up -d +``` + +This starts the container using the image `ghcr.io/canner/wren-engine-ibis:mcp-2` with: + +| Service | Port | Purpose | +|---------|------|---------| +| ibis-server | 8000 | REST API for query execution and metadata | +| mcp-server (streamable-http) | 9000 | MCP endpoint for AI clients | + +The workspace directory is mounted at `/workspace` inside the container. + +Verify the container is running: +```bash +docker compose ps +docker compose logs -f +``` + +--- + +## Step 4 — Fix connection info: localhost → host.docker.internal + +**Critical:** The container cannot reach the host's `localhost` directly. + +If the user's database connection info references `localhost` or `127.0.0.1` as the host, it must be changed to `host.docker.internal` so the container can reach a database running on the host machine. + +**Examples:** + +| Original | Inside Docker | +|----------|--------------| +| `"host": "localhost"` | `"host": "host.docker.internal"` | +| `"host": "127.0.0.1"` | `"host": "host.docker.internal"` | +| Cloud/remote host (e.g. `mydb.us-east-1.rds.amazonaws.com`) | No change needed | + +When the user provides connection credentials later (via `setup_connection`), check the `host` field and warn if it is `localhost` or `127.0.0.1`. + +--- + +## Step 5 — Configure Claude Code MCP + +Claude Code uses **streamable-http** transport to connect to the containerized MCP server. + +Add to `~/.claude.json` under `mcpServers` (or run `claude mcp add`): + +```json +{ + "mcpServers": { + "wren": { + "type": "http", + "url": "http://localhost:9000/mcp" + } + } +} +``` + +**Via Claude Code CLI (recommended):** + +```bash +claude mcp add --transport http wren http://localhost:9000/mcp +``` + +After adding, restart Claude Code or reload the MCP server list. Confirm with: +```bash +claude mcp list +``` + +--- + +## Step 6 — Configure other MCP clients + +### Cline / Cursor / VS Code MCP Extension + +These clients also support HTTP transport. Add to their MCP settings: + +```json +{ + "mcpServers": { + "wren": { + "type": "streamable-http", + "url": "http://localhost:9000/mcp" + } + } +} +``` + +### Claude Desktop (stdio fallback) + +Claude Desktop does not support HTTP transport natively. Use a local stdio proxy or run the MCP server locally instead. See the `wren-mcp-usage` skill for the local (non-Docker) stdio setup. + +--- + +## Step 7 — Verify the connection + +Ask the AI agent to run a health check: + +``` +Use the health_check() tool to verify Wren Engine is reachable. +``` + +Expected response: `SELECT 1` returns a successful result. + +If health check fails: +- Check that the container is running: `docker compose ps` +- Check logs: `docker compose logs ibis-server` +- Confirm port 9000 is not blocked by a firewall or another process + +--- + +## Step 8 — Load an MDL and start querying + +Place your MDL JSON file in the workspace directory, then in the AI client: + +``` +deploy(mdl_file_path="/workspace/my_mdl.json") +``` + +Or generate a new MDL from scratch using the `generate-mdl` skill. + +--- + +## Reference: compose.yaml + +The full compose file at `mcp-server/docker/compose.yaml`: + +```yaml +services: + ibis-server: + image: ghcr.io/canner/wren-engine-ibis:latest + ports: + - "8000:8000" + - "9000:9000" + environment: + ENABLE_MCP_SERVER: "true" + MCP_TRANSPORT: "streamable-http" + MCP_HOST: "0.0.0.0" + MCP_PORT: "9000" + WREN_URL: "localhost:8000" + volumes: + - ${MDL_WORKSPACE:-../workspace}:/workspace +``` + +--- + +## Reference: connection info by data source + +When calling `setup_connection`, use these formats. **Replace `localhost` with `host.docker.internal`** if the database runs on your host machine. + +``` +POSTGRES : {"host": "host.docker.internal", "port": "5432", "user": "...", "password": "...", "database": "..."} +MYSQL : {"host": "host.docker.internal", "port": "3306", "user": "...", "password": "...", "database": "..."} +MSSQL : {"host": "host.docker.internal", "port": "1433", "user": "...", "password": "...", "database": "..."} +CLICKHOUSE : {"host": "host.docker.internal", "port": "8123", "user": "...", "password": "...", "database": "..."} +TRINO : {"host": "host.docker.internal", "port": "8080", "user": "...", "catalog": "...", "schema": "..."} +DUCKDB : {"path": "/workspace/.duckdb"} ← file must be inside workspace +BIGQUERY : {"project": "...", "dataset": "...", "credentials_base64": "..."} +SNOWFLAKE : {"account": "...", "user": "...", "password": "...", "database": "...", "schema": "..."} +``` + +For DuckDB: the database file must be placed in the mounted workspace directory so the container can access it at `/workspace/.duckdb`. From 9e0526957395ea72b74736646260bd8ace217cfd Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Thu, 5 Mar 2026 18:48:07 +0800 Subject: [PATCH 21/28] docs(skills): update mdl-project skill with connection.yml support Co-Authored-By: Claude Sonnet 4.6 --- skills/mdl-project/SKILL.md | 106 +++++++++++++++++++++++++++++++----- 1 file changed, 92 insertions(+), 14 deletions(-) diff --git a/skills/mdl-project/SKILL.md b/skills/mdl-project/SKILL.md index c52c461a3..d7da2e2ec 100644 --- a/skills/mdl-project/SKILL.md +++ b/skills/mdl-project/SKILL.md @@ -1,9 +1,9 @@ --- name: mdl-project -description: Save, load, and build Wren MDL manifests as YAML project directories for version control. Use when a user wants to persist an MDL as human-readable YAML files, load a YAML project back into MDL JSON, or compile a YAML project to a deployable mdl.json file. +description: Save, load, and build Wren MDL manifests as YAML project directories for version control. Use when a user wants to persist an MDL as human-readable YAML files, load a YAML project back into MDL JSON, or compile a YAML project to a deployable mdl.json file. Also manages connection info stored in connection.yml and compiled to target/connection.json. metadata: author: wren-engine - version: "1.0" + version: "1.1" --- # MDL Project @@ -17,6 +17,7 @@ YAML files use **snake_case** field names for readability. The compiled `target/ ``` my_project/ ├── wren_project.yml # Project metadata (catalog, schema, data_source) +├── connection.yml # Data source connection parameters ├── models/ │ ├── orders.yml # One file per model │ ├── customers.yml @@ -25,13 +26,16 @@ my_project/ └── views.yml # All views ``` -After building, a compiled file is written to: +After building, compiled files are written to: ``` my_project/ └── target/ - └── mdl.json # Deployable MDL JSON (camelCase) + ├── mdl.json # Deployable MDL JSON (camelCase) + └── connection.json # Connection info JSON (camelCase) ``` +> **Security note**: `connection.yml` may contain credentials. Add `target/` and `connection.yml` to `.gitignore` or use environment variable substitution (see `connection.yml` below) before committing. + --- ## Save MDL JSON → YAML project @@ -98,6 +102,50 @@ relationships: views: [] ``` +### `connection.yml` + +Connection parameters for the data source. Field names use **snake_case** in YAML and are converted to **camelCase** in `target/connection.json`. + +**PostgreSQL / MySQL / MSSQL / ClickHouse / Trino / Oracle:** +```yaml +host: localhost +port: 5432 +user: my_user +password: my_password +database: my_db +``` + +**BigQuery:** +```yaml +project_id: my-gcp-project +dataset_id: my_dataset +credentials_json_string: '{"type":"service_account","project_id":"..."}' +``` + +**Snowflake:** +```yaml +user: my_user +password: my_password +account: my_account +database: my_db +sf_schema: public +``` + +**DuckDB (local file):** +```yaml +url: /path/to/my.duckdb +``` + +**Environment variable substitution** — to avoid committing secrets, reference env vars with `${VAR_NAME}`: +```yaml +host: ${DB_HOST} +port: ${DB_PORT} +user: ${DB_USER} +password: ${DB_PASSWORD} +database: ${DB_NAME} +``` +When building, resolve each `${VAR_NAME}` value from the environment before writing `target/connection.json`. + --- ## Load YAML project → MDL JSON @@ -108,7 +156,7 @@ To assemble a YAML project back into an MDL JSON dict: 2. Read every file in `models/*.yml` → collect into `models` list 3. Read `relationships.yml` → extract `relationships` list 4. Read `views.yml` → extract `views` list -5. **Rename snake_case keys to camelCase** (see [Field mapping](#field-mapping)) +5. **Rename snake_case keys to camelCase** (see Field mapping section below) 6. Assemble: ```json @@ -122,13 +170,25 @@ To assemble a YAML project back into an MDL JSON dict: } ``` +To load connection info: + +1. Read `connection.yml` +2. Resolve any `${VAR_NAME}` placeholders from environment variables +3. **Rename snake_case keys to camelCase** (see Field mapping section below) +4. Result is a flat JSON object ready to pass as `connectionInfo` to ibis-server APIs + --- -## Build YAML project → `target/mdl.json` +## Build YAML project → `target/` + +Same as **Load** above, but write both compiled files: -Same as **Load** above, but write the assembled (camelCase) JSON to `/target/mdl.json` instead of returning it. +- `/target/mdl.json` — assembled MDL JSON (camelCase) +- `/target/connection.json` — connection info JSON (camelCase, env vars resolved) -After building, pass the path to `deploy(mdl_file_path="/target/mdl.json")` to activate. +After building: +- Pass `mdl_file_path="/target/mdl.json"` to `deploy()` to activate the MDL +- Pass the contents of `target/connection.json` as the `connectionInfo` field in API requests --- @@ -136,6 +196,10 @@ After building, pass the path to `deploy(mdl_file_path="/target/mdl When converting between YAML (snake_case) and JSON (camelCase): +| YAML field (snake_case) | JSON field (camelCase) | +|-------------------------|------------------------| +**MDL fields:** + | YAML field (snake_case) | JSON field (camelCase) | |-------------------------|------------------------| | `data_source` | `dataSource` | @@ -146,7 +210,18 @@ When converting between YAML (snake_case) and JSON (camelCase): | `primary_key` | `primaryKey` | | `join_type` | `joinType` | -All other fields (`name`, `type`, `catalog`, `schema`, `table`, `condition`, `models`, `columns`, `cached`, `properties`) are the same in both formats. +All other MDL fields (`name`, `type`, `catalog`, `schema`, `table`, `condition`, `models`, `columns`, `cached`, `properties`) are the same in both formats. + +**Connection fields:** + +| YAML field (snake_case) | JSON field (camelCase) | +|-------------------------|------------------------| +| `project_id` | `projectId` | +| `dataset_id` | `datasetId` | +| `credentials_json_string` | `credentialsJsonString` | +| `sf_schema` | `sfSchema` | + +All other connection fields (`host`, `port`, `user`, `password`, `database`, `account`, `url`) are the same in both formats. --- @@ -154,10 +229,13 @@ All other fields (`name`, `type`, `catalog`, `schema`, `table`, `condition`, `mo ``` 1. Have MDL JSON dict (from generate-mdl skill or manual construction) -2. Save: write wren_project.yml + models/*.yml + relationships.yml + views.yml +2. Save: write wren_project.yml + connection.yml + models/*.yml + relationships.yml + views.yml (convert camelCase → snake_case) -3. Commit project directory to version control -4. Later — Load: read all YAML files, rename snake_case → camelCase, assemble MDL JSON dict -5. Build: write assembled JSON to target/mdl.json -6. Deploy: deploy(mdl_file_path="./target/mdl.json") +3. Add target/ and optionally connection.yml to .gitignore +4. Commit project directory to version control +5. Later — Load: read all YAML files, resolve ${ENV_VAR} placeholders in connection.yml, + rename snake_case → camelCase, assemble MDL JSON dict + connection info dict +6. Build: write assembled JSON to target/mdl.json and target/connection.json +7. Deploy: deploy(mdl_file_path="./target/mdl.json") + use target/connection.json as connectionInfo in API requests ``` From f111cbf68eba9db27dd4323148743505ace7739c Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Fri, 6 Mar 2026 10:49:55 +0800 Subject: [PATCH 22/28] docs(skills): add install script and update README with installation options Co-Authored-By: Claude Sonnet 4.6 --- skills/README.md | 32 +++++++++++-- skills/install.sh | 115 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 142 insertions(+), 5 deletions(-) create mode 100755 skills/install.sh diff --git a/skills/README.md b/skills/README.md index 9d363da8e..f0342b7f6 100644 --- a/skills/README.md +++ b/skills/README.md @@ -4,14 +4,35 @@ This directory contains reusable AI agent skills for working with Wren Engine. S ## Installation -Copy any skill directory into your local Claude skills folder: +### Option 1 — install script (recommended) +**From a local clone:** ```bash -# macOS / Linux -cp -r skills/generate-mdl ~/.claude/skills/ +bash skills/install.sh # all skills +bash skills/install.sh generate-mdl wren-sql # specific skills +bash skills/install.sh --force generate-mdl # overwrite existing +``` + +**Remote (one-liner, after pushing to GitHub):** +```bash +curl -fsSL https://raw.githubusercontent.com/Canner/wren-engine/main/skills/install.sh | bash +# specific skills: +curl -fsSL https://raw.githubusercontent.com/Canner/wren-engine/main/skills/install.sh | bash -s -- generate-mdl +``` + +### Option 2 — npx openskills (cross-agent) -# Or install all skills at once -cp -r skills/* ~/.claude/skills/ +Installs into Claude Code, Cursor, Windsurf, and 30+ other agent tools: +```bash +npx openskills add Canner/wren-engine +``` + +### Option 3 — manual copy + +```bash +cp -r skills/generate-mdl ~/.claude/skills/ +# or all at once: +cp -r skills/generate-mdl skills/mdl-project skills/wren-sql skills/wren-quickstart ~/.claude/skills/ ``` Once installed, invoke a skill by name in your conversation: @@ -20,6 +41,7 @@ Once installed, invoke a skill by name in your conversation: /generate-mdl /mdl-project /wren-sql +/wren-quickstart ``` ## Available Skills diff --git a/skills/install.sh b/skills/install.sh new file mode 100755 index 000000000..e0aba17d1 --- /dev/null +++ b/skills/install.sh @@ -0,0 +1,115 @@ +#!/usr/bin/env bash +# Install Wren Engine skills into your local AI agent skills directory. +# +# Usage: +# ./install.sh # install all skills +# ./install.sh generate-mdl # install specific skills +# ./install.sh --force wren-sql # overwrite without prompt +# curl -fsSL https://raw.githubusercontent.com/Canner/wren-engine/main/skills/install.sh | bash +# curl -fsSL .../install.sh | bash -s -- generate-mdl + +set -euo pipefail + +REPO="Canner/wren-engine" +BRANCH="${WREN_SKILLS_BRANCH:-main}" +DEST="${CLAUDE_SKILLS_DIR:-$HOME/.claude/skills}" +ALL_SKILLS=(generate-mdl mdl-project wren-sql wren-quickstart) + +# Parse --force flag and skill list from arguments +FORCE=false +SELECTED_SKILLS=() +for arg in "$@"; do + if [ "$arg" = "--force" ]; then + FORCE=true + else + SELECTED_SKILLS+=("$arg") + fi +done + +if [ "${#SELECTED_SKILLS[@]}" -eq 0 ]; then + SELECTED_SKILLS=("${ALL_SKILLS[@]}") +fi + +# Validate requested skills +for skill in "${SELECTED_SKILLS[@]}"; do + valid=false + for known in "${ALL_SKILLS[@]}"; do + if [ "$skill" = "$known" ]; then valid=true; break; fi + done + if [ "$valid" = false ]; then + echo "Unknown skill: $skill" >&2 + echo "Available: ${ALL_SKILLS[*]}" >&2 + exit 1 + fi +done + +# Detect whether we are running from a local clone or piped via curl. +# When piped, BASH_SOURCE[0] is empty or "/dev/stdin". +SCRIPT_DIR="" +if [ -n "${BASH_SOURCE[0]:-}" ] && [ "${BASH_SOURCE[0]}" != "/dev/stdin" ]; then + SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +fi + +install_from_local() { + local src="$1" skill="$2" dest_dir="$3" + if [ "$FORCE" = false ] && [ -d "$dest_dir" ]; then + echo " Skipping $skill (already exists). Use --force to overwrite." + return + fi + rm -rf "$dest_dir" + cp -r "$src/$skill" "$dest_dir" + echo " Installed $skill" +} + +install_from_archive() { + local tmpdir="$1" skill="$2" dest_dir="$3" + if [ "$FORCE" = false ] && [ -d "$dest_dir" ]; then + echo " Skipping $skill (already exists). Use --force to overwrite." + return + fi + if [ ! -d "$tmpdir/$skill" ]; then + echo " Failed: $skill not found in archive" >&2 + return 1 + fi + rm -rf "$dest_dir" + cp -r "$tmpdir/$skill" "$dest_dir" + echo " Installed $skill" +} + +mkdir -p "$DEST" + +if [ -n "$SCRIPT_DIR" ] && [ -d "$SCRIPT_DIR/generate-mdl" ]; then + # ---- Local mode: copy directly from repo ---- + echo "Installing from local repo: $SCRIPT_DIR" + echo "Destination: $DEST" + echo "" + for skill in "${SELECTED_SKILLS[@]}"; do + install_from_local "$SCRIPT_DIR" "$skill" "$DEST/$skill" + done +else + # ---- Remote mode: download GitHub archive ---- + echo "Downloading skills from GitHub ($REPO @ $BRANCH)..." + echo "Destination: $DEST" + echo "" + tmpdir=$(mktemp -d) + trap 'rm -rf "$tmpdir"' EXIT + + # Build the list of paths to extract from the tarball + extract_paths=() + for skill in "${SELECTED_SKILLS[@]}"; do + extract_paths+=("wren-engine-${BRANCH}/skills/${skill}") + done + + curl -fsSL "https://github.com/$REPO/archive/refs/heads/$BRANCH.tar.gz" \ + | tar -xz -C "$tmpdir" --strip-components=2 "${extract_paths[@]}" + + for skill in "${SELECTED_SKILLS[@]}"; do + install_from_archive "$tmpdir" "$skill" "$DEST/$skill" + done +fi + +echo "" +echo "Done. Invoke skills in your AI client:" +for skill in "${SELECTED_SKILLS[@]}"; do + echo " /$skill" +done From e50acf95caa1be8c006080174ee200fdcc1793a2 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Fri, 6 Mar 2026 10:53:47 +0800 Subject: [PATCH 23/28] docs(skills): add troubleshooting section for MCP server health issues Covers port conflicts (lsof detection + remap workaround), container log diagnostics, MCP endpoint verification via curl, and localhost vs host.docker.internal connection issues. Co-Authored-By: Claude Sonnet 4.6 --- skills/wren-quickstart/SKILL.md | 69 +++++++++++++++++++++++++++++++-- 1 file changed, 65 insertions(+), 4 deletions(-) diff --git a/skills/wren-quickstart/SKILL.md b/skills/wren-quickstart/SKILL.md index 65386a1b5..be905e0f5 100644 --- a/skills/wren-quickstart/SKILL.md +++ b/skills/wren-quickstart/SKILL.md @@ -149,10 +149,71 @@ Use the health_check() tool to verify Wren Engine is reachable. Expected response: `SELECT 1` returns a successful result. -If health check fails: -- Check that the container is running: `docker compose ps` -- Check logs: `docker compose logs ibis-server` -- Confirm port 9000 is not blocked by a firewall or another process +If health check fails, see **Troubleshooting** below. + +--- + +## Troubleshooting — MCP server not healthy + +### 1. Check container status and logs + +```bash +docker compose ps +docker compose logs ibis-server +``` + +Look for startup errors or crash loops. If the container exited, `logs` will show the last output before it stopped. + +### 2. Port already in use + +The container exposes ports **8000** (ibis-server) and **9000** (MCP). If either port is already bound by another process on the host, Docker will silently fail to bind it and the health check will time out. + +**Check what is using the port:** + +```bash +# macOS / Linux +lsof -i :9000 +lsof -i :8000 + +# or with ss (Linux) +ss -tlnp | grep -E '8000|9000' +``` + +If another process is occupying the port you have two options: + +**Option A — stop the conflicting process:** +```bash +# macOS: kill by port +kill $(lsof -ti :9000) +``` + +**Option B — remap the ports in `compose.yaml`:** + +Change the host-side port (left of the colon): +```yaml +ports: + - "18000:8000" # ibis-server now on host port 18000 + - "19000:9000" # MCP server now on host port 19000 +``` + +Then update the MCP client URL to match: +```bash +claude mcp add --transport http wren http://localhost:19000/mcp +``` + +### 3. Container started but MCP endpoint returns an error + +- Confirm `ENABLE_MCP_SERVER: "true"` is set in `compose.yaml` +- Confirm `MCP_TRANSPORT: "streamable-http"` and `MCP_HOST: "0.0.0.0"` are set +- Try curling the endpoint directly: + ```bash + curl -v http://localhost:9000/mcp + ``` + A `405 Method Not Allowed` response means the endpoint is reachable but expects a POST — that is normal and indicates the MCP server is up. + +### 4. Database connection refused inside the container + +If `health_check()` passes but queries fail with a connection error, the database host is likely still set to `localhost`. 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"sha256:e2647043599297a1ef10e720cf310846b7f31b6c841fee093d2b09d81215eb93", size = 1940159 }, -] - [[package]] name = "sse-starlette" version = "3.0.2" From bf55aef32affa616452314bba82411f36d00f234 Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Fri, 6 Mar 2026 11:11:22 +0800 Subject: [PATCH 25/28] update clickhouse test --- ibis-server/tests/routers/v2/connector/test_clickhouse.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/ibis-server/tests/routers/v2/connector/test_clickhouse.py b/ibis-server/tests/routers/v2/connector/test_clickhouse.py index a676f0520..ca46175b2 100644 --- a/ibis-server/tests/routers/v2/connector/test_clickhouse.py +++ b/ibis-server/tests/routers/v2/connector/test_clickhouse.py @@ -7,7 +7,6 @@ from testcontainers.clickhouse import ClickHouseContainer from app.model.data_source import X_WREN_DB_STATEMENT_TIMEOUT -from app.model.error import ErrorCode from tests.conftest import file_path pytestmark = pytest.mark.clickhouse @@ -317,7 +316,8 @@ async def test_query_to_many_relationship( async def test_query_alias_join(client, manifest_str, clickhouse: ClickHouseContainer): connection_info = _to_connection_info(clickhouse) - # ClickHouse does not support alias join + # ClickHouse does support alias join since 2026.03 + # refSql models rewrite alias join into a CTE, which ClickHouse supports response = await client.post( url=f"{base_url}/query", json={ @@ -327,8 +327,7 @@ async def test_query_alias_join(client, manifest_str, clickhouse: ClickHouseCont }, ) - assert response.status_code == 422 - assert response.json()["errorCode"] == ErrorCode.INVALID_SQL.name + assert response.status_code == 200 async def test_query_without_manifest(client, clickhouse: ClickHouseContainer): From 2682518d80fc4601a108430de48352f886c0b5fb Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Fri, 6 Mar 2026 11:23:26 +0800 Subject: [PATCH 26/28] update tag --- ibis-server/justfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ibis-server/justfile b/ibis-server/justfile index da83859c2..9edd1003d 100644 --- a/ibis-server/justfile +++ b/ibis-server/justfile @@ -54,7 +54,7 @@ test MARKER: test-verbose MARKER: poetry run pytest -s -v -m '{{ MARKER }}' -image-name := "ghcr.io/canner/wren-engine-ibis:mcp-1" +image-name := "ghcr.io/canner/wren-engine-ibis:latest" # Build image. platform: ""=current, linux/amd64, linux/arm64, linux/amd64,linux/arm64 (multi-arch needs --push) # Single-platform matching current host: Rust built locally via maturin+zig (requires: brew install zig) From 58d1428b20ba6b127046860c4c4d615320edeefb Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Fri, 6 Mar 2026 11:48:12 +0800 Subject: [PATCH 27/28] address commants and enhance doc --- mcp-server/.env.example | 11 ++- mcp-server/docker/compose.yaml | 4 +- skills/AUTHORING.md | 2 +- skills/README.md | 2 +- skills/mdl-project/SKILL.md | 4 +- skills/wren-quickstart/SKILL.md | 129 ++++++++++++++++---------------- 6 files changed, 76 insertions(+), 76 deletions(-) diff --git a/mcp-server/.env.example b/mcp-server/.env.example index 3c1c0554a..329ca4593 100644 --- a/mcp-server/.env.example +++ b/mcp-server/.env.example @@ -12,16 +12,19 @@ WREN_URL=localhost:8000 # Absolute path to the connection info JSON file for your data source # See README for the required fields per data source type. -CONNECTION_INFO_FILE=/path/to/connection_info.json +# In not set, the connection info must be set at runtime via `setup_connection` tool. +# CONNECTION_INFO_FILE=/path/to/connection_info.json # Absolute path to the MDL JSON file to pre-load on startup (optional) # If not set, MDL must be deployed at runtime via the 'deploy' tool. -MDL_PATH=/path/to/mdl.json +# MDL_PATH=/path/to/mdl.json -# MCP transport: "stdio" (default, for desktop clients) or "sse" (for HTTP) +# MCP transport: "stdio" (default, for desktop clients), "sse" (for HTTP) or "streamable-http" (for HTTP) +# Standalone (wren.py): defaults to "stdio" +# Docker with ENABLE_MCP_SERVER=true (entrypoint.sh): defaults to "streamable-http" # MCP_TRANSPORT=stdio -# Host and port used only when MCP_TRANSPORT=sse +# Host and port used only when MCP_TRANSPORT is "sse" or "streamable-http" # MCP_HOST=0.0.0.0 # MCP_PORT=9000 diff --git a/mcp-server/docker/compose.yaml b/mcp-server/docker/compose.yaml index f65057cfc..fb341204e 100644 --- a/mcp-server/docker/compose.yaml +++ b/mcp-server/docker/compose.yaml @@ -2,8 +2,8 @@ services: ibis-server: image: ghcr.io/canner/wren-engine-ibis:latest ports: - - "8000:8000" - - "9000:9000" + - "${HOST_WREN_PORT:-8000}:8000" + - "${HOST_MCP_PORT:-9000}:9000" environment: ENABLE_MCP_SERVER: "true" MCP_TRANSPORT: "streamable-http" diff --git a/skills/AUTHORING.md b/skills/AUTHORING.md index 8a38cb325..5cd71b130 100644 --- a/skills/AUTHORING.md +++ b/skills/AUTHORING.md @@ -9,7 +9,7 @@ Full specification: https://agentskills.io/specification Each skill is a subdirectory containing a required `SKILL.md` and optional supporting directories: -``` +```text skill-name/ ├── SKILL.md # Required — frontmatter + workflow instructions ├── references/ # Optional — detail files loaded on demand diff --git a/skills/README.md b/skills/README.md index f0342b7f6..747f96e4f 100644 --- a/skills/README.md +++ b/skills/README.md @@ -37,7 +37,7 @@ cp -r skills/generate-mdl skills/mdl-project skills/wren-sql skills/wren-quickst Once installed, invoke a skill by name in your conversation: -``` +```text /generate-mdl /mdl-project /wren-sql diff --git a/skills/mdl-project/SKILL.md b/skills/mdl-project/SKILL.md index d7da2e2ec..2239e2e37 100644 --- a/skills/mdl-project/SKILL.md +++ b/skills/mdl-project/SKILL.md @@ -14,7 +14,7 @@ YAML files use **snake_case** field names for readability. The compiled `target/ ## Project structure -``` +```text my_project/ ├── wren_project.yml # Project metadata (catalog, schema, data_source) ├── connection.yml # Data source connection parameters @@ -196,8 +196,6 @@ After building: When converting between YAML (snake_case) and JSON (camelCase): -| YAML field (snake_case) | JSON field (camelCase) | -|-------------------------|------------------------| **MDL fields:** | YAML field (snake_case) | JSON field (camelCase) | diff --git a/skills/wren-quickstart/SKILL.md b/skills/wren-quickstart/SKILL.md index be905e0f5..8b3460cf6 100644 --- a/skills/wren-quickstart/SKILL.md +++ b/skills/wren-quickstart/SKILL.md @@ -1,7 +1,7 @@ --- name: wren-quickstart -description: Set up Wren Engine MCP server via Docker. Covers pulling the Docker image, configuring the compose file, mounting a workspace, fixing localhost → host.docker.internal for connection info, and registering the MCP server in Claude Code (or other MCP clients) using streamable-http transport. Trigger when a user wants to run Wren MCP in Docker, configure Claude Code MCP, or connect an AI client to a Dockerized Wren Engine. -compatibility: Requires Docker Desktop (or Docker Engine) with BuildKit enabled. +description: Set up Wren Engine MCP server via Docker. Covers pulling the Docker image, running the container with docker run, mounting a workspace, fixing localhost → host.docker.internal for connection info, and registering the MCP server in Claude Code (or other MCP clients) using streamable-http transport. Trigger when a user wants to run Wren MCP in Docker, configure Claude Code MCP, or connect an AI client to a Dockerized Wren Engine. +compatibility: Requires Docker Desktop (or Docker Engine). metadata: author: wren-engine version: "1.0" @@ -19,39 +19,53 @@ Ask the user: > What directory on your host machine should be mounted as the MCP workspace? > This is where MDL files and YAML project directories will be read and written. -> (Default: `mcp-server/workspace` inside the wren-engine repo) +> (Example: `~/wren-workspace`) -If the user has no preference, use `../workspace` relative to the `docker/` directory (i.e. `mcp-server/workspace`). +If the user has no preference, suggest creating a dedicated directory such as `~/wren-workspace`: -Save the answer as `` for use in the next steps. +```bash +mkdir -p ~/wren-workspace +``` ---- +Save the answer as `` (use the absolute path, e.g. `/Users/me/wren-workspace`) for use in the next steps. -## Step 2 — Create the compose env file +--- -Create `mcp-server/docker/.env` with: +## Step 2 — Start the container -``` -MDL_WORKSPACE= -``` +Run the following command, substituting `` with the path from Step 1: -Example: -``` -MDL_WORKSPACE=/Users/me/my-mdl-files +```bash +docker run -d \ + --name wren-mcp \ + -p 8000:8000 \ + -p 9000:9000 \ + -e ENABLE_MCP_SERVER=true \ + -e MCP_TRANSPORT=streamable-http \ + -e MCP_HOST=0.0.0.0 \ + -e MCP_PORT=9000 \ + -e WREN_URL=localhost:8000 \ + -v :/workspace \ + ghcr.io/canner/wren-engine-ibis:latest ``` -If the user accepted the default, no `.env` file is needed — the compose default (`../workspace`) already points to `mcp-server/workspace/`. - ---- - -## Step 3 — Start the container +Example with a concrete path: ```bash -cd mcp-server/docker -docker compose up -d +docker run -d \ + --name wren-mcp \ + -p 8000:8000 \ + -p 9000:9000 \ + -e ENABLE_MCP_SERVER=true \ + -e MCP_TRANSPORT=streamable-http \ + -e MCP_HOST=0.0.0.0 \ + -e MCP_PORT=9000 \ + -e WREN_URL=localhost:8000 \ + -v /Users/me/my-mdl-files:/workspace \ + ghcr.io/canner/wren-engine-ibis:latest ``` -This starts the container using the image `ghcr.io/canner/wren-engine-ibis:mcp-2` with: +This starts the container using the image `ghcr.io/canner/wren-engine-ibis:latest` with: | Service | Port | Purpose | |---------|------|---------| @@ -62,13 +76,13 @@ The workspace directory is mounted at `/workspace` inside the container. Verify the container is running: ```bash -docker compose ps -docker compose logs -f +docker ps --filter name=wren-mcp +docker logs -f wren-mcp ``` --- -## Step 4 — Fix connection info: localhost → host.docker.internal +## Step 3 — Fix connection info: localhost → host.docker.internal **Critical:** The container cannot reach the host's `localhost` directly. @@ -86,7 +100,7 @@ When the user provides connection credentials later (via `setup_connection`), ch --- -## Step 5 — Configure Claude Code MCP +## Step 4 — Configure Claude Code MCP Claude Code uses **streamable-http** transport to connect to the containerized MCP server. @@ -116,7 +130,7 @@ claude mcp list --- -## Step 6 — Configure other MCP clients +## Step 5 — Configure other MCP clients ### Cline / Cursor / VS Code MCP Extension @@ -135,11 +149,11 @@ These clients also support HTTP transport. Add to their MCP settings: ### Claude Desktop (stdio fallback) -Claude Desktop does not support HTTP transport natively. Use a local stdio proxy or run the MCP server locally instead. See the `wren-mcp-usage` skill for the local (non-Docker) stdio setup. +Claude Desktop does not support HTTP transport natively. Use a local stdio proxy or run the MCP server locally instead. --- -## Step 7 — Verify the connection +## Step 6 — Verify the connection Ask the AI agent to run a health check: @@ -158,15 +172,15 @@ If health check fails, see **Troubleshooting** below. ### 1. Check container status and logs ```bash -docker compose ps -docker compose logs ibis-server +docker ps --filter name=wren-mcp +docker logs wren-mcp ``` Look for startup errors or crash loops. If the container exited, `logs` will show the last output before it stopped. ### 2. Port already in use -The container exposes ports **8000** (ibis-server) and **9000** (MCP). If either port is already bound by another process on the host, Docker will silently fail to bind it and the health check will time out. +The container exposes ports **8000** (ibis-server) and **9000** (MCP). If either port is already bound by another process on the host, the `docker run` command will fail with a bind error. **Check what is using the port:** @@ -187,13 +201,22 @@ If another process is occupying the port you have two options: kill $(lsof -ti :9000) ``` -**Option B — remap the ports in `compose.yaml`:** +**Option B — remap to different host ports:** -Change the host-side port (left of the colon): -```yaml -ports: - - "18000:8000" # ibis-server now on host port 18000 - - "19000:9000" # MCP server now on host port 19000 +Stop and remove the existing container first, then re-run with different host-side ports: +```bash +docker rm -f wren-mcp +docker run -d \ + --name wren-mcp \ + -p 18000:8000 \ + -p 19000:9000 \ + -e ENABLE_MCP_SERVER=true \ + -e MCP_TRANSPORT=streamable-http \ + -e MCP_HOST=0.0.0.0 \ + -e MCP_PORT=9000 \ + -e WREN_URL=localhost:8000 \ + -v :/workspace \ + ghcr.io/canner/wren-engine-ibis:latest ``` Then update the MCP client URL to match: @@ -203,8 +226,7 @@ claude mcp add --transport http wren http://localhost:19000/mcp ### 3. Container started but MCP endpoint returns an error -- Confirm `ENABLE_MCP_SERVER: "true"` is set in `compose.yaml` -- Confirm `MCP_TRANSPORT: "streamable-http"` and `MCP_HOST: "0.0.0.0"` are set +- Confirm the container was started with `-e ENABLE_MCP_SERVER=true`, `-e MCP_TRANSPORT=streamable-http`, and `-e MCP_HOST=0.0.0.0` - Try curling the endpoint directly: ```bash curl -v http://localhost:9000/mcp @@ -213,11 +235,11 @@ claude mcp add --transport http wren http://localhost:19000/mcp ### 4. Database connection refused inside the container -If `health_check()` passes but queries fail with a connection error, the database host is likely still set to `localhost`. See **Step 4** above — change it to `host.docker.internal`. +If `health_check()` passes but queries fail with a connection error, the database host is likely still set to `localhost`. See **Step 3** above — change it to `host.docker.internal`. --- -## Step 8 — Load an MDL and start querying +## Step 7 — Load an MDL and start querying Place your MDL JSON file in the workspace directory, then in the AI client: @@ -229,29 +251,6 @@ Or generate a new MDL from scratch using the `generate-mdl` skill. --- -## Reference: compose.yaml - -The full compose file at `mcp-server/docker/compose.yaml`: - -```yaml -services: - ibis-server: - image: ghcr.io/canner/wren-engine-ibis:latest - ports: - - "8000:8000" - - "9000:9000" - environment: - ENABLE_MCP_SERVER: "true" - MCP_TRANSPORT: "streamable-http" - MCP_HOST: "0.0.0.0" - MCP_PORT: "9000" - WREN_URL: "localhost:8000" - volumes: - - ${MDL_WORKSPACE:-../workspace}:/workspace -``` - ---- - ## Reference: connection info by data source When calling `setup_connection`, use these formats. **Replace `localhost` with `host.docker.internal`** if the database runs on your host machine. From 422277e93eccd5539fcd6c1d7647156ad672994c Mon Sep 17 00:00:00 2001 From: Jax Liu Date: Fri, 6 Mar 2026 12:04:36 +0800 Subject: [PATCH 28/28] fix(docker): handle Linux ARM64 aarch64 in docker-build arch detection uname -m returns aarch64 on Linux ARM64, not arm64 (macOS only). --- ibis-server/justfile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ibis-server/justfile b/ibis-server/justfile index 9edd1003d..c62d03834 100644 --- a/ibis-server/justfile +++ b/ibis-server/justfile @@ -64,8 +64,8 @@ docker-build platform="" *args="": set -euo pipefail host_arch=$(uname -m) - host_platform=$([ "$host_arch" = "arm64" ] && echo "linux/arm64" || echo "linux/amd64") - rust_target=$([ "$host_arch" = "arm64" ] && echo "aarch64-unknown-linux-gnu" || echo "x86_64-unknown-linux-gnu") + host_platform=$([[ "$host_arch" = "arm64" || "$host_arch" = "aarch64" ]] && echo "linux/arm64" || echo "linux/amd64") + rust_target=$([[ "$host_arch" = "arm64" || "$host_arch" = "aarch64" ]] && echo "aarch64-unknown-linux-gnu" || echo "x86_64-unknown-linux-gnu") build_platform="{{ platform }}" [ -z "$build_platform" ] && build_platform="$host_platform"