feat(mcp): introduce MCP server

mcp-server/README.md

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+# Wren MCP Server
+
+The **Wren MCP Server** is a **Model Context Protocol (MCP) server** that provides tools for interacting with **Wren Engine** to facilitate AI agent integration.
+
+## Requirements
+
+Before setting up the Wren MCP Server, ensure you have the following dependency installed:
+
+- **[uv](https://docs.astral.sh/uv/getting-started/installation/#installing-uv)** - A fast and efficient Python package manager.
+
+## Environment Variables
+
+The server requires the following environment variables to be set:
+
+| Variable | Description |
+|----------|------------|
+| `WREN_URL` | The URL of the **Wren Ibis server**. |
+| `CONNECTION_INFO_FILE` | The path to the **required connection info file**. |
+| `MDL_PATH` | The path to the **MDL file**. |
+
+### Connection Info
+
+The following JSON is a connection info of a Postgres. You can find the requried fields for each data source in the [source code](https://github.com/Canner/wren-engine/blob/4ac283ee0754b12a8c3b0a6f13b32c935fcb7b0d/ibis-server/app/model/__init__.py#L75).
+```json
+{
+    "host": "localhost",
+    "port": "5432",
+    "user": "test",
+    "password": "test",
+    "database": "test"
+}
+```
+
+### The `dataSource` field is requried.
+
+In the MDL, the `dataSource` field is required to indicate which data source should be connected. 
+
+### `.env` File Support
+
+Wren MCP Server supports an `.env` file for easier environment configuration. You can define all the required environment variables in this file.
+
+---
+
+## Installation & Usage
+
+### 1. Set the Python Envrionment
+
+Use the `uv` command to create a virtual envrionment and activate it:
+```
+> uv venv
+Using CPython 3.11.11
+Creating virtual environment at: .venv
+Activate with: source .venv/bin/activate
+> source .venv/bin/activate   
+> uv run app/wren.py
+Loaded MDL etc/mdl.json
+Loaded connection info etc/pg_conneciton.json
+```
+You would see that the MDL and connection info are loaded. Then, you can use `Ctrl + C` terminate the process.
+
+### 2. Start Wren Engine and Ibis Server
+
+- If you **already have a running Wren Engine**, ensure that `WREN_URL` is correctly set to point to your server.
+- If you **don't have a running engine**, you can start one using Docker:
+
+  ```sh
+  cd docker
+  docker compose up
+  ```
+
+### 3. Set Environment Variables
+
+Make sure all required environment variables are properly configured, either in your system or within a `.env` file.
+
+### 4. Configure the MCP Server
+
+Create a configuration file with the following structure:
+
+```json
+{
+    "mcpServers": {
+        "wren": {
+            "command": "uv",
+            "args": [
+                "--directory",
+                "/ABSOLUTE/PATH/TO/PARENT/FOLDER/wren-engine/mcp-server",
+                "run",
+                "app/wren.py"
+            ],
+            "autoApprove": [],
+            "disabled": false
+        }
+    }
+}
+```
+
+#### Notes:
+- You **may need to provide the full path** to the `uv` executable in the `"command"` field. You can find it using:
+  - **MacOS/Linux**: `which uv`
+  - **Windows**: `where uv`
+- Ensure that the **absolute path** to the MCP server directory is used in the configuration.
+- For more details, refer to the [MCP Server Guide](https://modelcontextprotocol.io/quickstart/server#test-with-commands).
+
+### 5. Choose an AI Agent That Supports MCP Server
+
+The following AI agents are compatible with Wren MCP Server and deploy the MCP config:
+
+- **[Claude Desktop](https://modelcontextprotocol.io/quickstart/user)**  
+- **[Cline](https://docs.cline.bot/mcp-servers/mcp-quickstart)**  
+
+### 6. Check the Wren Engine is Connected
+
+You can ask the AI agent to perform a health check for Wren Engine.
+
+### 7. Start the Conversation
+
+Now, you can start asking questions through your AI agent and interact with Wren Engine.
+
+---
+
+## Additional Resources
+
+- **Wren Engine Documentation**: [Wren AI](https://getwren.ai/)  
+- **MCP Protocol Guide**: [Model Context Protocol](https://modelcontextprotocol.io/)  

mcp-server/app/dto.py

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+from pydantic import BaseModel
+from pydantic.fields import Field
+
+
+class Column(BaseModel):
+    name: str
+    type: str
+    expression: str = None
+    isCalculated: bool = False
+    relationship: str = None
+    description: str = None
+
+
+class TableReference(BaseModel):
+    catalog: str = None
+    mdl_schema: str = Field(alias="schema", default=None)
+    table: str
+
+
+class Model(BaseModel):
+    name: str
+    tableReference: TableReference
+    columns: list[Column]
+    primaryKey: str = None
+    description: str = None
+
+
+class Relationship(BaseModel):
+    name: str
+    models: list[str]
+    join_type: str
+    join_condition: str
+
+
+class View(BaseModel):
+    name: str
+    statement: str
+    description: str = None
+
+
+class Manifest(BaseModel):
+    catalog: str = "wren"
+    mdl_schema: str = Field(alias="schema", default="public")
+    models: list[Model]
+    relationships: list[Relationship]
+    views: list[View]
+    description: str = None
+
+
+class TableColumns(BaseModel):
+    table_name: str
+    column_names: list[str] = None

mcp-server/app/utils.py

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+import base64
+import orjson
+
+
+def dict_to_base64_string(data: dict[str, any]) -> str:
+    return base64.b64encode(orjson.dumps(data)).decode("utf-8")
+
+
+def json_to_base64_string(data: str) -> str:
+    return base64.b64encode(data.encode("utf-8")).decode("utf-8")