From 84f17361dd9777cbf3e19d18e0adcec707e589f1 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Thu, 4 Dec 2025 10:52:35 -0500
Subject: [PATCH 01/15] =?UTF-8?q?MCP=20=E2=86=92=20SDK=20(vocab=20change?=
=?UTF-8?q?=20only)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
src/fastmcp/prompts/prompt.py | 10 +++++-----
src/fastmcp/resources/resource.py | 8 ++++----
src/fastmcp/resources/template.py | 10 +++++-----
src/fastmcp/server/server.py | 16 ++++++++--------
src/fastmcp/tools/tool.py | 6 +++---
tests/server/middleware/test_tool_injection.py | 12 ++++++------
6 files changed, 31 insertions(+), 31 deletions(-)
diff --git a/src/fastmcp/prompts/prompt.py b/src/fastmcp/prompts/prompt.py
index f8498237de..c34056c952 100644
--- a/src/fastmcp/prompts/prompt.py
+++ b/src/fastmcp/prompts/prompt.py
@@ -9,8 +9,8 @@
import pydantic_core
from mcp.types import ContentBlock, Icon, PromptMessage, Role, TextContent
-from mcp.types import Prompt as MCPPrompt
-from mcp.types import PromptArgument as MCPPromptArgument
+from mcp.types import Prompt as SDKPrompt
+from mcp.types import PromptArgument as SDKPromptArgument
from pydantic import Field, TypeAdapter
from fastmcp.exceptions import PromptError
@@ -89,10 +89,10 @@ def to_mcp_prompt(
*,
include_fastmcp_meta: bool | None = None,
**overrides: Any,
- ) -> MCPPrompt:
+ ) -> SDKPrompt:
"""Convert the prompt to an MCP prompt."""
arguments = [
- MCPPromptArgument(
+ SDKPromptArgument(
name=arg.name,
description=arg.description,
required=arg.required,
@@ -100,7 +100,7 @@ def to_mcp_prompt(
for arg in self.arguments or []
]
- return MCPPrompt(
+ return SDKPrompt(
name=overrides.get("name", self.name),
description=overrides.get("description", self.description),
arguments=arguments,
diff --git a/src/fastmcp/resources/resource.py b/src/fastmcp/resources/resource.py
index f27070447a..29632b0cc4 100644
--- a/src/fastmcp/resources/resource.py
+++ b/src/fastmcp/resources/resource.py
@@ -8,7 +8,7 @@
import pydantic_core
from mcp.types import Annotations, Icon
-from mcp.types import Resource as MCPResource
+from mcp.types import Resource as SDKResource
from pydantic import (
AnyUrl,
ConfigDict,
@@ -125,10 +125,10 @@ def to_mcp_resource(
*,
include_fastmcp_meta: bool | None = None,
**overrides: Any,
- ) -> MCPResource:
- """Convert the resource to an MCPResource."""
+ ) -> SDKResource:
+ """Convert the resource to an SDKResource."""
- return MCPResource(
+ return SDKResource(
name=overrides.get("name", self.name),
uri=overrides.get("uri", self.uri),
description=overrides.get("description", self.description),
diff --git a/src/fastmcp/resources/template.py b/src/fastmcp/resources/template.py
index cde1d273cf..18e7529740 100644
--- a/src/fastmcp/resources/template.py
+++ b/src/fastmcp/resources/template.py
@@ -9,7 +9,7 @@
from urllib.parse import parse_qs, unquote
from mcp.types import Annotations, Icon
-from mcp.types import ResourceTemplate as MCPResourceTemplate
+from mcp.types import ResourceTemplate as SDKResourceTemplate
from pydantic import (
Field,
field_validator,
@@ -195,10 +195,10 @@ def to_mcp_template(
*,
include_fastmcp_meta: bool | None = None,
**overrides: Any,
- ) -> MCPResourceTemplate:
- """Convert the resource template to an MCPResourceTemplate."""
+ ) -> SDKResourceTemplate:
+ """Convert the resource template to an SDKResourceTemplate."""
- return MCPResourceTemplate(
+ return SDKResourceTemplate(
name=overrides.get("name", self.name),
uriTemplate=overrides.get("uriTemplate", self.uri_template),
description=overrides.get("description", self.description),
@@ -212,7 +212,7 @@ def to_mcp_template(
)
@classmethod
- def from_mcp_template(cls, mcp_template: MCPResourceTemplate) -> ResourceTemplate:
+ def from_mcp_template(cls, mcp_template: SDKResourceTemplate) -> ResourceTemplate:
"""Creates a FastMCP ResourceTemplate from a raw MCP ResourceTemplate object."""
# Note: This creates a simple ResourceTemplate instance. For function-based templates,
# the original function is lost, which is expected for remote templates.
diff --git a/src/fastmcp/server/server.py b/src/fastmcp/server/server.py
index d7cd94cebb..213b6c3ecc 100644
--- a/src/fastmcp/server/server.py
+++ b/src/fastmcp/server/server.py
@@ -39,10 +39,10 @@
GetPromptResult,
ToolAnnotations,
)
-from mcp.types import Prompt as MCPPrompt
-from mcp.types import Resource as MCPResource
-from mcp.types import ResourceTemplate as MCPResourceTemplate
-from mcp.types import Tool as MCPTool
+from mcp.types import Prompt as SDKPrompt
+from mcp.types import Resource as SDKResource
+from mcp.types import ResourceTemplate as SDKResourceTemplate
+from mcp.types import Tool as SDKTool
from pydantic import AnyUrl
from starlette.middleware import Middleware as ASGIMiddleware
from starlette.requests import Request
@@ -643,7 +643,7 @@ def _get_additional_http_routes(self) -> list[BaseRoute]:
return routes
- async def _list_tools_mcp(self) -> list[MCPTool]:
+ async def _list_tools_mcp(self) -> list[SDKTool]:
"""
List all available tools, in the format expected by the low-level MCP
server.
@@ -727,7 +727,7 @@ async def _list_tools(
return list(all_tools.values())
- async def _list_resources_mcp(self) -> list[MCPResource]:
+ async def _list_resources_mcp(self) -> list[SDKResource]:
"""
List all available resources, in the format expected by the low-level MCP
server.
@@ -816,7 +816,7 @@ async def _list_resources(
return list(all_resources.values())
- async def _list_resource_templates_mcp(self) -> list[MCPResourceTemplate]:
+ async def _list_resource_templates_mcp(self) -> list[SDKResourceTemplate]:
"""
List all available resource templates, in the format expected by the low-level MCP
server.
@@ -910,7 +910,7 @@ async def _list_resource_templates(
return list(all_templates.values())
- async def _list_prompts_mcp(self) -> list[MCPPrompt]:
+ async def _list_prompts_mcp(self) -> list[SDKPrompt]:
"""
List all available prompts, in the format expected by the low-level MCP
server.
diff --git a/src/fastmcp/tools/tool.py b/src/fastmcp/tools/tool.py
index 1f3d88ca34..792d285218 100644
--- a/src/fastmcp/tools/tool.py
+++ b/src/fastmcp/tools/tool.py
@@ -17,7 +17,7 @@
import pydantic_core
from mcp.shared.tool_name_validation import validate_and_warn_tool_name
from mcp.types import CallToolResult, ContentBlock, Icon, TextContent, ToolAnnotations
-from mcp.types import Tool as MCPTool
+from mcp.types import Tool as SDKTool
from pydantic import Field, PydanticSchemaGenerationError, model_validator
from typing_extensions import TypeVar
@@ -158,7 +158,7 @@ def to_mcp_tool(
*,
include_fastmcp_meta: bool | None = None,
**overrides: Any,
- ) -> MCPTool:
+ ) -> SDKTool:
"""Convert the FastMCP tool to an MCP tool."""
title = None
@@ -167,7 +167,7 @@ def to_mcp_tool(
elif self.annotations and self.annotations.title:
title = self.annotations.title
- return MCPTool(
+ return SDKTool(
name=overrides.get("name", self.name),
title=overrides.get("title", title),
description=overrides.get("description", self.description),
diff --git a/tests/server/middleware/test_tool_injection.py b/tests/server/middleware/test_tool_injection.py
index 505d78b018..3e0599414c 100644
--- a/tests/server/middleware/test_tool_injection.py
+++ b/tests/server/middleware/test_tool_injection.py
@@ -5,7 +5,7 @@
import pytest
from inline_snapshot import snapshot
from mcp.types import TextContent
-from mcp.types import Tool as MCPTool
+from mcp.types import Tool as SDKTool
from fastmcp import FastMCP
from fastmcp.client import Client
@@ -68,7 +68,7 @@ async def test_list_tools_includes_injected_tools(self, base_server: FastMCP):
base_server.add_middleware(middleware)
async with Client[FastMCPTransport](base_server) as client:
- tools: list[MCPTool] = await client.list_tools()
+ tools: list[SDKTool] = await client.list_tools()
# Should have all tools: multiply, divide, add, subtract
assert len(tools) == 4
@@ -249,7 +249,7 @@ async def test_injected_tool_bypass_filtering(self, base_server: FastMCP):
base_server.exclude_tags = {"math"}
async with Client[FastMCPTransport](base_server) as client:
- tools: list[MCPTool] = await client.list_tools()
+ tools: list[SDKTool] = await client.list_tools()
tool_names: list[str] = [tool.name for tool in tools]
assert "multiply" in tool_names
@@ -259,7 +259,7 @@ async def test_empty_tool_injection(self, base_server: FastMCP):
base_server.add_middleware(middleware)
async with Client[FastMCPTransport](base_server) as client:
- tools: list[MCPTool] = await client.list_tools()
+ tools: list[SDKTool] = await client.list_tools()
result: CallToolResult = await client.call_tool(
name="add", arguments={"a": 3, "b": 4}
)
@@ -304,7 +304,7 @@ async def test_prompt_tools_added_to_list(self, server_with_prompts: FastMCP):
server_with_prompts.add_middleware(middleware)
async with Client[FastMCPTransport](server_with_prompts) as client:
- tools: list[MCPTool] = await client.list_tools()
+ tools: list[SDKTool] = await client.list_tools()
tool_names: list[str] = [tool.name for tool in tools]
# Should have: add, list_prompts, get_prompt
@@ -428,7 +428,7 @@ async def test_resource_tools_added_to_list(self, server_with_resources: FastMCP
server_with_resources.add_middleware(middleware)
async with Client[FastMCPTransport](server_with_resources) as client:
- tools: list[MCPTool] = await client.list_tools()
+ tools: list[SDKTool] = await client.list_tools()
tool_names: list[str] = [tool.name for tool in tools]
# Should have: add, list_resources, read_resource
From 5fb9103e4c9591772af144d4663df1c00af6bad7 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Thu, 4 Dec 2025 11:39:30 -0500
Subject: [PATCH 02/15] WIP: Sampling API with SamplingResult[T] and
result_type
---
docs/clients/sampling.mdx | 112 ++
docs/servers/sampling.mdx | 414 +++-
.../experimental/sampling/handlers/openai.py | 125 +-
src/fastmcp/server/context.py | 598 +++++-
src/fastmcp/server/proxy.py | 6 +-
src/fastmcp/server/sampling/__init__.py | 10 +
src/fastmcp/server/sampling/handler.py | 9 +-
src/fastmcp/server/sampling/sampling_tool.py | 187 ++
tests/client/test_sampling.py | 766 +++++++-
tests/server/sampling/__init__.py | 0
tests/server/sampling/test_sampling_tool.py | 182 ++
tests/server/test_context.py | 8 +-
uv.lock | 1680 +++++++++--------
13 files changed, 3245 insertions(+), 852 deletions(-)
create mode 100644 src/fastmcp/server/sampling/__init__.py
create mode 100644 src/fastmcp/server/sampling/sampling_tool.py
create mode 100644 tests/server/sampling/__init__.py
create mode 100644 tests/server/sampling/test_sampling_tool.py
diff --git a/docs/clients/sampling.mdx b/docs/clients/sampling.mdx
index dfc21616f3..4f87a8e8c7 100644
--- a/docs/clients/sampling.mdx
+++ b/docs/clients/sampling.mdx
@@ -109,6 +109,14 @@ The sampling handler receives three parameters:
Optional metadata to pass through to the LLM provider.
+
+
+ Optional list of tools the LLM can use during sampling. See [Handling Tool Requests](#handling-tool-requests).
+
+
+
+ Optional control over tool usage behavior (`auto`, `required`, or `none`).
+
@@ -153,4 +161,108 @@ client = Client(
If the client doesn't provide a sampling handler, servers can optionally configure a fallback handler. See [Server Sampling](/servers/sampling#sampling-fallback-handler) for details.
+
+
+## Handling Tool Requests
+
+
+
+Servers may request sampling with tools, allowing the LLM to make tool calls during generation. When tools are provided in `params.tools`, your handler should return a `CreateMessageResultWithTools` object instead of a simple string.
+
+### Checking for Tools
+
+```python
+from fastmcp import Client
+from fastmcp.client.sampling import SamplingMessage, SamplingParams, RequestContext
+from mcp.types import (
+ CreateMessageResultWithTools,
+ TextContent,
+ ToolUseContent,
+ ToolChoice,
+)
+
+async def sampling_handler_with_tools(
+ messages: list[SamplingMessage],
+ params: SamplingParams,
+ context: RequestContext
+) -> str | CreateMessageResultWithTools:
+ # Check if tools were provided
+ if params.tools:
+ # Call your LLM with tool support
+ # Return CreateMessageResultWithTools with appropriate content
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="I'll help you with that.")],
+ model="gpt-4",
+ stopReason="endTurn",
+ )
+
+ # Standard text response when no tools
+ return "Generated response"
+
+client = Client(
+ "my_mcp_server.py",
+ sampling_handler=sampling_handler_with_tools,
+)
+```
+
+### Returning Tool Use
+
+When the LLM wants to call a tool, return a `CreateMessageResultWithTools` with `stopReason="toolUse"` and `ToolUseContent` in the content:
+
+```python
+from mcp.types import CreateMessageResultWithTools, ToolUseContent
+
+async def sampling_handler_with_tools(
+ messages: list[SamplingMessage],
+ params: SamplingParams,
+ context: RequestContext
+) -> str | CreateMessageResultWithTools:
+ if params.tools:
+ # Your LLM decided to call a tool
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="toolUse",
+ id="call_123",
+ name="search",
+ input={"query": "Python tutorials"},
+ )
+ ],
+ model="gpt-4",
+ stopReason="toolUse", # Indicates tool use
+ )
+
+ return "Response without tools"
+```
+
+### Tool Choice
+
+The `params.toolChoice` field indicates how the server wants tools to be used:
+
+- **`auto`**: The LLM decides whether to use tools
+- **`required`**: The LLM must use at least one tool
+- **`none`**: The LLM should not use any tools
+
+```python
+async def sampling_handler_with_tools(
+ messages: list[SamplingMessage],
+ params: SamplingParams,
+ context: RequestContext
+) -> str | CreateMessageResultWithTools:
+ if params.tools and params.toolChoice:
+ if params.toolChoice.mode == "required":
+ # Must return a tool call
+ pass
+ elif params.toolChoice.mode == "none":
+ # Should not return tool calls even though tools are available
+ pass
+ # "auto" - let the LLM decide
+
+ return "Response"
+```
+
+
+Tool execution happens on the server side. The client's role is to pass tools to the LLM and return the LLM's response (which may include tool use requests). The server then executes the tools and may send follow-up sampling requests with tool results.
\ No newline at end of file
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index b126d05622..f36e2073f3 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -9,7 +9,7 @@ import { VersionBadge } from '/snippets/version-badge.mdx'
-LLM sampling allows MCP tools to request LLM text generation based on provided messages. By default, sampling requests are sent to the client's LLM, but you can also configure a fallback handler or always use a specific LLM provider. This is useful when tools need to leverage LLM capabilities to process data, generate responses, or perform text-based analysis.
+LLM sampling allows MCP tools to request LLM completions based on provided messages. By default, sampling requests are sent to the client's LLM, but you can also configure a fallback handler or always use a specific LLM provider.
## Why Use LLM Sampling?
@@ -17,14 +17,15 @@ LLM sampling enables tools to:
- **Leverage AI capabilities**: Use the client's LLM for text generation and analysis
- **Offload complex reasoning**: Let the LLM handle tasks requiring natural language understanding
+- **Execute agentic workflows**: Let the LLM call tools during sampling to accomplish complex tasks
+- **Get structured output**: Request validated, typed responses using `result_type`
- **Generate dynamic content**: Create responses, summaries, or transformations based on data
-- **Maintain context**: Use the same LLM instance that the user is already interacting with
-### Basic Usage
+## Basic Usage
-Use `ctx.sample()` to request text generation from the client's LLM:
+Use `ctx.sample()` to request text generation from the client's LLM. The method returns a `SamplingResult` with `.text`, `.result`, and `.history` attributes:
-```python {14}
+```python
from fastmcp import FastMCP, Context
mcp = FastMCP("SamplingDemo")
@@ -32,25 +33,24 @@ mcp = FastMCP("SamplingDemo")
@mcp.tool
async def analyze_sentiment(text: str, ctx: Context) -> dict:
"""Analyze the sentiment of text using the client's LLM."""
- prompt = f"""Analyze the sentiment of the following text as positive, negative, or neutral.
+ prompt = f"""Analyze the sentiment of the following text as positive, negative, or neutral.
Just output a single word - 'positive', 'negative', or 'neutral'.
-
+
Text to analyze: {text}"""
-
- # Request LLM analysis
+
+ # Request LLM analysis - returns SamplingResult
response = await ctx.sample(prompt)
-
- # Process the LLM's response
+
+ # Access the text via .text or .result (same for text responses)
sentiment = response.text.strip().lower()
-
- # Map to standard sentiment values
+
if "positive" in sentiment:
sentiment = "positive"
elif "negative" in sentiment:
sentiment = "negative"
else:
sentiment = "neutral"
-
+
return {"text": text, "sentiment": sentiment}
```
@@ -59,32 +59,51 @@ async def analyze_sentiment(text: str, ctx: Context) -> dict:
Request text generation from the client's LLM
-
+
A string or list of strings/message objects to send to the LLM
-
+
Optional system prompt to guide the LLM's behavior
-
+
Optional sampling temperature (controls randomness, typically 0.0-1.0)
-
+
Optional maximum number of tokens to generate
-
+
Optional model selection preferences (e.g., model hint string, list of hints, or ModelPreferences object)
+
+
+ Optional list of tools the LLM can use during sampling. See [Sampling with Tools](#sampling-with-tools).
+
+
+
+ Optional control over tool usage behavior (`auto`, `required`, or `none`)
+
+
+
+ Maximum number of LLM calls before returning. When tools are provided, FastMCP automatically executes tool calls and continues the conversation. On the last iteration, forces a text response (or `final_response` if using `result_type`).
+
+
+
+ Optional type for structured output. When specified, creates a synthetic `final_response` tool and validates the LLM's response against this type. See [Structured Output](#structured-output).
+
-
+
-
- The LLM's response content (typically TextContent with a .text attribute)
+
+ Always returns a `SamplingResult` with:
+ - `.text`: The text representation (raw text, or JSON for structured output). `None` if the LLM requested a tool on the last iteration.
+ - `.result`: The typed result. For text responses, equals `.text`. For structured output, the validated object.
+ - `.history`: List of all messages exchanged during sampling, useful for continuing conversations.
@@ -96,12 +115,12 @@ async def analyze_sentiment(text: str, ctx: Context) -> dict:
Generate text with simple string prompts:
-```python {6}
+```python
@mcp.tool
async def generate_summary(content: str, ctx: Context) -> str:
"""Generate a summary of the provided content."""
prompt = f"Please provide a concise summary of the following content:\n\n{content}"
-
+
response = await ctx.sample(prompt)
return response.text
```
@@ -110,7 +129,7 @@ async def generate_summary(content: str, ctx: Context) -> str:
Use system prompts to guide the LLM's behavior:
-```python {4-9}
+```python
@mcp.tool
async def generate_code_example(concept: str, ctx: Context) -> str:
"""Generate a Python code example for a given concept."""
@@ -120,9 +139,8 @@ async def generate_code_example(concept: str, ctx: Context) -> str:
temperature=0.7,
max_tokens=300
)
-
- code_example = response.text
- return f"```python\n{code_example}\n```"
+
+ return f"```python\n{response.text}\n```"
```
@@ -130,18 +148,17 @@ async def generate_code_example(concept: str, ctx: Context) -> str:
Specify model preferences for different use cases:
-```python {4-8, 17-22}
+```python
@mcp.tool
async def creative_writing(topic: str, ctx: Context) -> str:
"""Generate creative content using a specific model."""
response = await ctx.sample(
messages=f"Write a creative short story about {topic}",
- model_preferences="claude-3-sonnet", # Prefer a specific model
- include_context="thisServer", # Use the server's context
+ model_preferences="claude-haiku-4-5", # Prefer a specific model
temperature=0.9, # High creativity
max_tokens=1000
)
-
+
return response.text
@mcp.tool
@@ -153,7 +170,7 @@ async def technical_analysis(data: str, ctx: Context) -> str:
temperature=0.2, # Low randomness for consistency
max_tokens=800
)
-
+
return response.text
```
@@ -161,7 +178,7 @@ async def technical_analysis(data: str, ctx: Context) -> str:
Use structured messages for more complex interactions:
-```python {1, 6-10}
+```python
from fastmcp.client.sampling import SamplingMessage
@mcp.tool
@@ -172,16 +189,255 @@ async def multi_turn_analysis(user_query: str, context_data: str, ctx: Context)
SamplingMessage(role="assistant", content="I can see your data. What would you like me to analyze?"),
SamplingMessage(role="user", content=user_query)
]
-
+
response = await ctx.sample(
messages=messages,
system_prompt="You are a data analyst. Provide detailed insights based on the conversation context.",
temperature=0.3
)
-
+
return response.text
```
+## Structured Output
+
+
+
+Use `result_type` to get validated, typed responses from the LLM. When you specify a `result_type`, FastMCP creates a synthetic `final_response` tool that the LLM calls to provide its structured response.
+
+```python
+from pydantic import BaseModel
+from fastmcp import FastMCP, Context
+
+mcp = FastMCP()
+
+class SentimentResult(BaseModel):
+ sentiment: str
+ confidence: float
+ reasoning: str
+
+@mcp.tool
+async def analyze_sentiment(text: str, ctx: Context) -> SentimentResult:
+ """Analyze sentiment with structured output."""
+ result = await ctx.sample(
+ messages=f"Analyze the sentiment of: {text}",
+ result_type=SentimentResult,
+ )
+
+ # result.result is a validated SentimentResult object
+ return result.result
+```
+
+### How Structured Output Works
+
+When you pass `result_type`:
+
+1. FastMCP creates a synthetic `final_response` tool with a schema derived from your type
+2. A hint is added to the system prompt: "Call final_response when you have completed the task."
+3. The LLM calls `final_response` with its structured response
+4. FastMCP validates the input against your type
+5. If validation fails, the error is sent back to the LLM for retry
+6. On success, `result.result` contains the validated object and `result.text` contains the JSON
+
+## Sampling with Tools
+
+
+
+Sampling with tools enables agentic workflows where the LLM can request tool calls during sampling. FastMCP automatically executes the tools and continues the conversation until the LLM provides a final response.
+
+### Creating Sampling Tools
+
+Use the `@sampling_tool` decorator to create tools for use during sampling:
+
+```python
+from fastmcp import FastMCP, Context
+from fastmcp.server.sampling import sampling_tool
+
+@sampling_tool
+def search(query: str) -> str:
+ """Search the web for information."""
+ return f"Search results for: {query}"
+
+@sampling_tool
+async def calculate(expression: str) -> float:
+ """Evaluate a mathematical expression."""
+ return eval(expression) # In production, use a safe math parser
+```
+
+You can also pass existing FastMCP tools directly to `ctx.sample()`:
+
+```python
+mcp = FastMCP()
+
+@mcp.tool
+def get_weather(city: str) -> str:
+ """Get the current weather for a city."""
+ return f"Weather in {city}: 72°F, sunny"
+
+@mcp.tool
+async def weather_agent(question: str, ctx: Context) -> str:
+ """Answer weather questions using the weather tool."""
+ result = await ctx.sample(
+ messages=question,
+ tools=[get_weather], # FastMCP tools work directly
+ )
+ return result.text
+```
+
+### Using Tools in Sampling
+
+Pass tools to `ctx.sample()` to enable agentic tool use. FastMCP automatically handles the tool execution loop:
+
+```python
+from fastmcp import FastMCP, Context
+from fastmcp.server.sampling import sampling_tool
+
+@sampling_tool
+def search(query: str) -> str:
+ """Search the web."""
+ return f"Results for: {query}"
+
+@sampling_tool
+def calculate(expression: str) -> float:
+ """Calculate a math expression."""
+ return eval(expression)
+
+mcp = FastMCP()
+
+@mcp.tool
+async def research_assistant(question: str, ctx: Context) -> str:
+ """Answer questions using search and calculation tools."""
+ result = await ctx.sample(
+ messages=question,
+ system_prompt="You are a research assistant. Use the available tools to answer questions.",
+ tools=[search, calculate],
+ )
+
+ return result.text
+```
+
+### Tool Loop
+
+When you call `ctx.sample()` with tools, FastMCP automatically handles the execution loop:
+
+1. The LLM receives your prompt and the available tools
+2. If the LLM calls a tool, FastMCP executes it and sends the result back
+3. This continues until the LLM provides a final text response
+
+Tool errors are automatically caught and sent back to the LLM, allowing it to handle failures gracefully.
+
+Use `max_iterations` to limit the number of LLM calls (default is 10). On the last iteration, FastMCP forces the LLM to return a final response rather than continuing to call tools:
+
+```python
+# Allow up to 5 tool-use cycles
+result = await ctx.sample(
+ messages=prompt,
+ tools=[search, calculate],
+ max_iterations=5,
+)
+
+# Single iteration mode - forces text on first call
+# Useful for building your own loop with result.history
+result = await ctx.sample(
+ messages=prompt,
+ tools=[search, calculate],
+ max_iterations=1,
+)
+```
+
+### Tool Choice Options
+
+Control how the LLM uses tools with `tool_choice`:
+
+```python
+from mcp.types import ToolChoice
+
+# Let the LLM decide (default)
+result = await ctx.sample(messages=prompt, tools=tools, tool_choice=ToolChoice(mode="auto"))
+
+# Force the LLM to use a tool
+result = await ctx.sample(messages=prompt, tools=tools, tool_choice=ToolChoice(mode="required"))
+
+# Prevent tool use (useful for final response)
+result = await ctx.sample(messages=prompt, tools=tools, tool_choice=ToolChoice(mode="none"))
+```
+
+### Structured Output
+
+Combine `result_type` with tools for agentic workflows that return structured data:
+
+```python
+from pydantic import BaseModel
+from fastmcp import FastMCP, Context
+from fastmcp.server.sampling import sampling_tool
+
+class ResearchResult(BaseModel):
+ summary: str
+ sources: list[str]
+ confidence: float
+
+@sampling_tool
+def search(query: str) -> str:
+ """Search for information."""
+ return f"Found information about: {query}"
+
+@sampling_tool
+def fetch_url(url: str) -> str:
+ """Fetch content from a URL."""
+ return f"Content from {url}"
+
+mcp = FastMCP()
+
+@mcp.tool
+async def research(topic: str, ctx: Context) -> ResearchResult:
+ """Research a topic and return structured results."""
+ result = await ctx.sample(
+ messages=f"Research this topic thoroughly: {topic}",
+ tools=[search, fetch_url],
+ result_type=ResearchResult,
+ )
+
+ # The LLM uses search/fetch_url as needed, then calls final_response
+ # result.result is the validated ResearchResult
+ return result.result
+```
+
+The LLM can call your tools as many times as needed, and when it's ready to provide the final answer, it calls `final_response` with the structured data.
+
+### Building Custom Loops
+
+The automatic tool loop handles most use cases, but sometimes you need finer control. Setting `max_iterations=1` runs a single sampling iteration and returns immediately, giving you access to `result.history` so you can continue the conversation yourself.
+
+This is useful when you need to:
+- Dynamically change which tools are available based on intermediate results
+- Inject additional context or instructions mid-conversation
+- Implement custom termination conditions beyond "LLM stopped calling tools"
+- Add logging, metrics, or checkpointing between iterations
+
+```python
+@mcp.tool
+async def custom_agent(question: str, ctx: Context) -> str:
+ """Agent with custom loop logic."""
+ history = []
+ tools = [search, calculate]
+
+ while True:
+ result = await ctx.sample(
+ messages=history or question,
+ tools=tools,
+ max_iterations=1,
+ )
+ history = result.history
+
+ # Custom logic: check conditions, change tools, etc.
+ if some_condition:
+ tools = [different_tool]
+
+ # When we get a text response, we're done
+ if result.text:
+ return result.text
+```
+
## Sampling Fallback Handler
Client support for sampling is optional. If the client does not support sampling, the server will report an error indicating that the client does not support sampling.
@@ -191,7 +447,27 @@ However, you can provide a `sampling_handler` to the FastMCP server, which sends
- **`"fallback"`** (default): Uses the handler only when the client doesn't support sampling. Requests go to the client first, falling back to the handler if needed.
- **`"always"`**: Always uses the handler, bypassing the client entirely. Useful when you want full control over the LLM used for sampling.
-Sampling handlers can be implemented using any LLM provider, but a sample implementation for OpenAI is provided as a Contrib module. Sampling lacks the full capabilities of typical LLM completions. For this reason, the OpenAI sampling handler, pointed at a third-party provider's OpenAI-compatible API, is often sufficient to implement a sampling handler.
+### OpenAI Handler
+
+FastMCP provides an OpenAI-compatible sampling handler that supports both basic sampling and tool use:
+
+```python
+import os
+from openai import OpenAI
+from fastmcp import FastMCP
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+
+server = FastMCP(
+ name="Sampling Server",
+ sampling_handler=OpenAISamplingHandler(
+ default_model="gpt-4o-mini",
+ client=OpenAI(api_key=os.getenv("OPENAI_API_KEY")),
+ ),
+ sampling_handler_behavior="fallback",
+)
+```
+
+The OpenAI handler automatically handles tool conversion - when you pass tools to `ctx.sample()`, they're converted to OpenAI's function calling format.
### Fallback Mode (Default)
@@ -200,13 +476,10 @@ Uses the handler only when the client doesn't support sampling:
```python
import asyncio
import os
-
-from mcp.types import ContentBlock
from openai import OpenAI
-from fastmcp import FastMCP
+from fastmcp import FastMCP, Context
from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
-from fastmcp.server.context import Context
async def async_main():
@@ -214,20 +487,18 @@ async def async_main():
name="OpenAI Sampling Fallback Example",
sampling_handler=OpenAISamplingHandler(
default_model="gpt-4o-mini",
- client=OpenAI(
- api_key=os.getenv("API_KEY"),
- base_url=os.getenv("BASE_URL"),
- ),
+ client=OpenAI(api_key=os.getenv("OPENAI_API_KEY")),
),
- sampling_handler_behavior="fallback", # Default - only use when client doesn't support sampling
+ sampling_handler_behavior="fallback",
)
@server.tool
- async def test_sample_fallback(ctx: Context) -> ContentBlock:
- # Will use client's LLM if available, otherwise falls back to the handler
- return await ctx.sample(
- messages=["hello world!"],
+ async def summarize(text: str, ctx: Context) -> str:
+ # Uses client's LLM if available, falls back to OpenAI
+ result = await ctx.sample(
+ messages=f"Summarize: {text}",
)
+ return result.text
await server.run_http_async()
@@ -247,12 +518,12 @@ server = FastMCP(
default_model="gpt-4o-mini",
client=OpenAI(api_key=os.getenv("API_KEY")),
),
- sampling_handler_behavior="always", # Always use the handler, never the client
+ sampling_handler_behavior="always",
)
@server.tool
async def analyze_data(data: str, ctx: Context) -> str:
- # Will ALWAYS use the server's configured LLM, not the client's
+ # Always uses the server's configured LLM
result = await ctx.sample(
messages=f"Analyze this data: {data}",
system_prompt="You are a data analyst.",
@@ -260,6 +531,41 @@ async def analyze_data(data: str, ctx: Context) -> str:
return result.text
```
+### Fallback with Tools
+
+The fallback handler fully supports sampling with tools:
+
+```python
+from fastmcp import FastMCP, Context
+from fastmcp.server.sampling import sampling_tool
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+from openai import OpenAI
+import os
+
+@sampling_tool
+def search(query: str) -> str:
+ """Search for information."""
+ return f"Results for: {query}"
+
+server = FastMCP(
+ name="Agentic Server",
+ sampling_handler=OpenAISamplingHandler(
+ default_model="gpt-4o-mini",
+ client=OpenAI(api_key=os.getenv("OPENAI_API_KEY")),
+ ),
+ sampling_handler_behavior="always",
+)
+
+@server.tool
+async def research(question: str, ctx: Context) -> str:
+ """Research a topic using tools."""
+ result = await ctx.sample(
+ messages=question,
+ tools=[search],
+ )
+ return result.text
+```
+
## Client Requirements
By default, LLM sampling requires client support:
@@ -268,3 +574,7 @@ By default, LLM sampling requires client support:
- If the client doesn't support sampling and no fallback handler is configured, `ctx.sample()` will raise an error
- Configure a `sampling_handler` with `sampling_handler_behavior="fallback"` to automatically handle clients that don't support sampling
- Use `sampling_handler_behavior="always"` to completely bypass the client and control which LLM is used
+
+
+Sampling with tools requires either a client that advertises the `sampling.tools` capability, or a fallback handler configured with `sampling_handler_behavior="always"`. Most current MCP clients don't yet advertise tool support, so using a fallback handler is the recommended approach for tool-enabled sampling.
+
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index 0ff6108350..87a891888b 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -1,26 +1,35 @@
+import json
from collections.abc import Iterator, Sequence
-from typing import get_args
+from typing import Any, get_args
from mcp import ClientSession, ServerSession
from mcp.shared.context import LifespanContextT, RequestContext
from mcp.types import CreateMessageRequestParams as SamplingParams
from mcp.types import (
CreateMessageResult,
+ CreateMessageResultWithTools,
ModelPreferences,
SamplingMessage,
+ StopReason,
TextContent,
+ Tool,
+ ToolChoice,
+ ToolUseContent,
)
try:
- from openai import NOT_GIVEN, OpenAI
+ from openai import NOT_GIVEN, NotGiven, OpenAI
from openai.types.chat import (
ChatCompletion,
ChatCompletionAssistantMessageParam,
ChatCompletionMessageParam,
ChatCompletionSystemMessageParam,
+ ChatCompletionToolChoiceOptionParam,
+ ChatCompletionToolParam,
ChatCompletionUserMessageParam,
)
from openai.types.shared.chat_model import ChatModel
+ from openai.types.shared_params import FunctionDefinition
except ImportError as e:
raise ImportError(
"The `openai` package is not installed. Please install `fastmcp[openai]` or add `openai` to your dependencies manually."
@@ -43,7 +52,7 @@ async def __call__(
params: SamplingParams,
context: RequestContext[ServerSession, LifespanContextT]
| RequestContext[ClientSession, LifespanContextT],
- ) -> CreateMessageResult:
+ ) -> CreateMessageResult | CreateMessageResultWithTools:
openai_messages: list[ChatCompletionMessageParam] = (
self._convert_to_openai_messages(
system_prompt=params.systemPrompt,
@@ -53,14 +62,29 @@ async def __call__(
model: ChatModel = self._select_model_from_preferences(params.modelPreferences)
+ # Convert MCP tools to OpenAI format
+ openai_tools: list[ChatCompletionToolParam] | NotGiven = NOT_GIVEN
+ if params.tools:
+ openai_tools = self._convert_tools_to_openai(params.tools)
+
+ # Convert tool_choice to OpenAI format
+ openai_tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN
+ if params.toolChoice:
+ openai_tool_choice = self._convert_tool_choice_to_openai(params.toolChoice)
+
response = self.client.chat.completions.create(
model=model,
messages=openai_messages,
temperature=params.temperature or NOT_GIVEN,
max_tokens=params.maxTokens,
stop=params.stopSequences or NOT_GIVEN,
+ tools=openai_tools,
+ tool_choice=openai_tool_choice,
)
+ # Return appropriate result type based on whether tools were provided
+ if params.tools:
+ return self._chat_completion_to_result_with_tools(response)
return self._chat_completion_to_create_message_result(response)
@staticmethod
@@ -168,3 +192,98 @@ def _select_model_from_preferences(
return chosen_model
return self.default_model
+
+ @staticmethod
+ def _convert_tools_to_openai(tools: list[Tool]) -> list[ChatCompletionToolParam]:
+ """Convert MCP tools to OpenAI tool format."""
+ openai_tools: list[ChatCompletionToolParam] = []
+ for tool in tools:
+ # Build parameters dict, ensuring required fields
+ parameters: dict[str, Any] = dict(tool.inputSchema)
+ if "type" not in parameters:
+ parameters["type"] = "object"
+
+ openai_tools.append(
+ ChatCompletionToolParam(
+ type="function",
+ function=FunctionDefinition(
+ name=tool.name,
+ description=tool.description or "",
+ parameters=parameters,
+ ),
+ )
+ )
+ return openai_tools
+
+ @staticmethod
+ def _convert_tool_choice_to_openai(
+ tool_choice: ToolChoice,
+ ) -> ChatCompletionToolChoiceOptionParam:
+ """Convert MCP tool_choice to OpenAI format."""
+ if tool_choice.mode == "auto":
+ return "auto"
+ elif tool_choice.mode == "required":
+ return "required"
+ elif tool_choice.mode == "none":
+ return "none"
+ else:
+ # Unknown mode, default to auto
+ return "auto"
+
+ @staticmethod
+ def _chat_completion_to_result_with_tools(
+ chat_completion: ChatCompletion,
+ ) -> CreateMessageResultWithTools:
+ """Convert OpenAI response to CreateMessageResultWithTools."""
+ if len(chat_completion.choices) == 0:
+ raise ValueError("No response for completion")
+
+ first_choice = chat_completion.choices[0]
+ message = first_choice.message
+
+ # Determine stop reason
+ stop_reason: StopReason
+ if first_choice.finish_reason == "tool_calls":
+ stop_reason = "toolUse"
+ elif first_choice.finish_reason == "stop":
+ stop_reason = "endTurn"
+ elif first_choice.finish_reason == "length":
+ stop_reason = "maxTokens"
+ else:
+ stop_reason = "endTurn"
+
+ # Build content list
+ content: list[TextContent | ToolUseContent] = []
+
+ # Add text content if present
+ if message.content:
+ content.append(TextContent(type="text", text=message.content))
+
+ # Add tool calls if present
+ if message.tool_calls:
+ for tool_call in message.tool_calls:
+ # Parse the arguments JSON string
+ try:
+ arguments = json.loads(tool_call.function.arguments)
+ except json.JSONDecodeError:
+ arguments = {}
+
+ content.append(
+ ToolUseContent(
+ type="tool_use",
+ id=tool_call.id,
+ name=tool_call.function.name,
+ input=arguments,
+ )
+ )
+
+ # Must have at least some content
+ if not content:
+ raise ValueError("No content in response from completion")
+
+ return CreateMessageResultWithTools(
+ content=content,
+ role="assistant",
+ model=chat_completion.model,
+ stopReason=stop_reason,
+ )
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index ef1256c683..a3739102ac 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -2,6 +2,7 @@
import copy
import inspect
+import json
import logging
import weakref
from collections.abc import Generator, Mapping, Sequence
@@ -10,7 +11,7 @@
from dataclasses import dataclass
from enum import Enum
from logging import Logger
-from typing import Any, Literal, cast, get_origin, overload
+from typing import Any, Generic, Literal, cast, get_origin, overload
import anyio
from mcp import LoggingLevel, ServerSession
@@ -20,6 +21,7 @@
from mcp.types import (
ClientCapabilities,
CreateMessageResult,
+ CreateMessageResultWithTools,
GetPromptResult,
IncludeContext,
ModelHint,
@@ -28,11 +30,16 @@
SamplingCapability,
SamplingMessage,
SamplingMessageContentBlock,
+ SamplingToolsCapability,
TextContent,
+ ToolChoice,
+ ToolResultContent,
+ ToolUseContent,
)
from mcp.types import CreateMessageRequestParams as SamplingParams
-from mcp.types import Prompt as MCPPrompt
-from mcp.types import Resource as MCPResource
+from mcp.types import Prompt as SDKPrompt
+from mcp.types import Resource as SDKResource
+from mcp.types import Tool as SDKTool
from pydantic.networks import AnyUrl
from starlette.requests import Request
from typing_extensions import TypeVar
@@ -44,7 +51,10 @@
ScalarElicitationType,
get_elicitation_schema,
)
+from fastmcp.server.sampling import SamplingTool
from fastmcp.server.server import FastMCP
+from fastmcp.tools.tool import Tool as FastMCPTool
+from fastmcp.utilities.json_schema import compress_schema
from fastmcp.utilities.logging import _clamp_logger, get_logger
from fastmcp.utilities.types import get_cached_typeadapter
@@ -58,8 +68,33 @@
T = TypeVar("T", default=Any)
+ResultT = TypeVar("ResultT", default=str)
_current_context: ContextVar[Context | None] = ContextVar("context", default=None) # type: ignore[assignment]
+
+
+@dataclass
+class SamplingResult(Generic[ResultT]):
+ """Result from ctx.sample() containing the response and history.
+
+ This is a generic class where ResultT defaults to str for text responses.
+ When a result_type is specified, ResultT is that type.
+
+ Attributes:
+ text: The text representation of the result. For text responses, this is
+ the raw text. For structured responses, this is the JSON representation.
+ None if the response was a tool use (for manual loop building).
+ result: The typed result. When ResultT is str, this equals text. When ResultT
+ is a custom type, this is the validated/parsed object.
+ history: List of all messages exchanged during sampling, including tool calls
+ and results. Useful for continuing conversations or debugging.
+ """
+
+ text: str | None
+ result: ResultT
+ history: list[SamplingMessage]
+
+
_flush_lock = anyio.Lock()
@@ -233,7 +268,7 @@ async def report_progress(
related_request_id=self.request_id,
)
- async def list_resources(self) -> list[MCPResource]:
+ async def list_resources(self) -> list[SDKResource]:
"""List all available resources from the server.
Returns:
@@ -241,7 +276,7 @@ async def list_resources(self) -> list[MCPResource]:
"""
return await self.fastmcp._list_resources_mcp()
- async def list_prompts(self) -> list[MCPPrompt]:
+ async def list_prompts(self) -> list[SDKPrompt]:
"""List all available prompts from the server.
Returns:
@@ -471,21 +506,96 @@ async def send_prompt_list_changed(self) -> None:
"""Send a prompt list changed notification to the client."""
await self.session.send_prompt_list_changed()
+ @overload
async def sample(
self,
messages: str | Sequence[str | SamplingMessage],
+ *,
system_prompt: str | None = None,
include_context: IncludeContext | None = None,
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
- ) -> SamplingMessageContentBlock | list[SamplingMessageContentBlock]:
+ tools: Sequence[SamplingTool | FastMCPTool] | None = None,
+ tool_choice: ToolChoice | None = None,
+ max_iterations: int = 10,
+ result_type: type[ResultT],
+ ) -> SamplingResult[ResultT]:
+ """Overload: With result_type, returns SamplingResult[ResultT]."""
+
+ @overload
+ async def sample(
+ self,
+ messages: str | Sequence[str | SamplingMessage],
+ *,
+ system_prompt: str | None = None,
+ include_context: IncludeContext | None = None,
+ temperature: float | None = None,
+ max_tokens: int | None = None,
+ model_preferences: ModelPreferences | str | list[str] | None = None,
+ tools: Sequence[SamplingTool | FastMCPTool] | None = None,
+ tool_choice: ToolChoice | None = None,
+ max_iterations: int = 10,
+ result_type: None = None,
+ ) -> SamplingResult[str]:
+ """Overload: Without result_type, returns SamplingResult[str]."""
+
+ async def sample(
+ self,
+ messages: str | Sequence[str | SamplingMessage],
+ *,
+ system_prompt: str | None = None,
+ include_context: IncludeContext | None = None,
+ temperature: float | None = None,
+ max_tokens: int | None = None,
+ model_preferences: ModelPreferences | str | list[str] | None = None,
+ tools: Sequence[SamplingTool | FastMCPTool] | None = None,
+ tool_choice: ToolChoice | None = None,
+ max_iterations: int = 10,
+ result_type: type[ResultT] | None = None,
+ ) -> SamplingResult[ResultT] | SamplingResult[str]:
"""
Send a sampling request to the client and await the response.
Call this method at any time to have the server request an LLM
completion from the client. The client must be appropriately configured,
or the request will error.
+
+ When tools are provided, the method automatically executes a tool loop:
+ if the LLM returns a tool use request, the tools are executed and the
+ results are sent back to the LLM. This continues until the LLM provides
+ a final response or max_iterations is reached.
+
+ When result_type is specified (not str), a synthetic `final_response` tool
+ is created. The LLM calls this tool to provide the structured response,
+ which is validated against the result_type and returned as `.result`.
+
+ Args:
+ messages: The message(s) to send. Can be a string, list of strings,
+ or list of SamplingMessage objects.
+ system_prompt: Optional system prompt for the LLM.
+ include_context: Optional context inclusion setting.
+ temperature: Optional sampling temperature.
+ max_tokens: Maximum tokens to generate. Defaults to 512.
+ model_preferences: Optional model preferences.
+ tools: Optional list of tools the LLM can use. Accepts both
+ SamplingTools and FastMCP Tools (which are auto-converted).
+ When provided, the method automatically handles tool execution
+ and returns the final response after all tool calls complete.
+ tool_choice: Optional control over tool usage behavior. Only valid
+ when tools are provided.
+ max_iterations: Maximum number of LLM calls before returning.
+ Defaults to 10. Set to 1 for single-iteration mode where you
+ can build your own loop using the history.
+ result_type: Optional type for structured output. When specified,
+ a synthetic `final_response` tool is created and the LLM's
+ response is validated against this type.
+
+ Returns:
+ SamplingResult[T] containing:
+ - .text: The text representation (raw text or JSON for structured)
+ - .result: The typed result (str for text, parsed object for structured)
+ - .history: All messages exchanged during sampling
"""
if max_tokens is None:
@@ -505,6 +615,38 @@ async def sample(
for m in messages
]
+ # Convert tools to SamplingTools, then to SDK tools
+ sampling_tools: list[SamplingTool] = []
+ if tools is not None:
+ for t in tools:
+ if isinstance(t, SamplingTool):
+ sampling_tools.append(t)
+ elif isinstance(t, FastMCPTool):
+ sampling_tools.append(SamplingTool.from_mcp_tool(t))
+ else:
+ raise TypeError(
+ f"Expected SamplingTool or FastMCP Tool, got {type(t)}"
+ )
+
+ # Create synthetic final_response tool for structured output
+ final_response_tool: SamplingTool | None = None
+ if result_type is not None and result_type is not str:
+ final_response_tool = _create_final_response_tool(result_type)
+ sampling_tools.append(final_response_tool)
+
+ # Add hint to system prompt
+ hint = "Call final_response when you have completed the task."
+ system_prompt = f"{system_prompt}\n\n{hint}" if system_prompt else hint
+
+ sdk_tools: list[SDKTool] | None = None
+ if sampling_tools:
+ sdk_tools = [t._to_sdk_tool() for t in sampling_tools]
+
+ # Build a tool lookup for execution
+ tool_map: dict[str, SamplingTool] = {}
+ if sampling_tools:
+ tool_map = {t.name: t for t in sampling_tools}
+
should_fallback = (
self.fastmcp.sampling_handler_behavior == "fallback"
and not self.session.check_client_capability(
@@ -516,14 +658,113 @@ async def sample(
if self.fastmcp.sampling_handler is None:
raise ValueError("Client does not support sampling")
+ return await self._sample_with_fallback_handler(
+ sampling_messages=sampling_messages,
+ system_prompt=system_prompt,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ model_preferences=model_preferences,
+ sdk_tools=sdk_tools,
+ tool_choice=tool_choice,
+ tool_map=tool_map,
+ max_iterations=max_iterations,
+ result_type=result_type,
+ final_response_tool=final_response_tool,
+ )
+
+ # When tools are provided, we need the client to support sampling.tools
+ if sampling_tools:
+ has_tools_capability = self.session.check_client_capability(
+ capability=ClientCapabilities(
+ sampling=SamplingCapability(tools=SamplingToolsCapability())
+ )
+ )
+ if not has_tools_capability:
+ raise ValueError(
+ "Client does not support sampling with tools. "
+ "The client must advertise the sampling.tools capability."
+ )
+
+ return await self._sample_with_client(
+ sampling_messages=sampling_messages,
+ system_prompt=system_prompt,
+ include_context=include_context,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ model_preferences=model_preferences,
+ sdk_tools=sdk_tools,
+ tool_choice=tool_choice,
+ tool_map=tool_map,
+ max_iterations=max_iterations,
+ result_type=result_type,
+ final_response_tool=final_response_tool,
+ )
+
+ # Simple case: no tools, no structured output
+ result = await self.session.create_message(
+ messages=sampling_messages,
+ system_prompt=system_prompt,
+ include_context=include_context,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ model_preferences=_parse_model_preferences(model_preferences),
+ related_request_id=self.request_id,
+ )
+
+ # Extract text from content
+ text = _extract_text_from_content(result.content)
+
+ return SamplingResult(
+ text=text,
+ result=cast(ResultT, text),
+ history=list(sampling_messages),
+ )
+
+ async def _sample_with_fallback_handler(
+ self,
+ sampling_messages: list[SamplingMessage],
+ system_prompt: str | None,
+ temperature: float | None,
+ max_tokens: int,
+ model_preferences: ModelPreferences | str | list[str] | None,
+ sdk_tools: list[SDKTool] | None,
+ tool_choice: ToolChoice | None,
+ tool_map: dict[str, SamplingTool],
+ max_iterations: int,
+ result_type: type[ResultT] | None,
+ final_response_tool: SamplingTool | None,
+ ) -> SamplingResult[ResultT] | SamplingResult[str]:
+ """Execute sampling with fallback handler, including automatic tool loop."""
+ assert self.fastmcp.sampling_handler is not None
+
+ current_messages = list(sampling_messages)
+ iteration = 0
+ has_tools = bool(sdk_tools)
+ has_result_type = result_type is not None and result_type is not str
+
+ while True:
+ # On last iteration, force completion
+ effective_tool_choice = tool_choice
+ if iteration == max_iterations - 1:
+ if has_result_type and final_response_tool is not None:
+ # Force final_response tool on last iteration
+ effective_tool_choice = ToolChoice(
+ mode="required", name="final_response"
+ )
+ elif has_tools:
+ # Force text response (no tools) on last iteration
+ effective_tool_choice = ToolChoice(mode="none")
+
create_message_result = self.fastmcp.sampling_handler(
- sampling_messages,
+ current_messages,
SamplingParams(
systemPrompt=system_prompt,
- messages=sampling_messages,
+ messages=current_messages,
temperature=temperature,
maxTokens=max_tokens,
modelPreferences=_parse_model_preferences(model_preferences),
+ tools=sdk_tools,
+ toolChoice=effective_tool_choice,
),
self.request_context,
)
@@ -531,28 +772,276 @@ async def sample(
if inspect.isawaitable(create_message_result):
create_message_result = await create_message_result
+ iteration += 1
+
+ # Handle simple string/content responses
if isinstance(create_message_result, str):
- return TextContent(text=create_message_result, type="text")
+ if has_tools:
+ raise ValueError(
+ "Sampling handler returned a string, but tools were provided. "
+ "Handler must return CreateMessageResultWithTools when tools are used."
+ )
+ return SamplingResult(
+ text=create_message_result,
+ result=cast(ResultT, create_message_result),
+ history=current_messages,
+ )
if isinstance(create_message_result, CreateMessageResult):
- return create_message_result.content
+ if has_tools:
+ raise ValueError(
+ "Sampling handler returned CreateMessageResult, but tools were provided. "
+ "Handler must return CreateMessageResultWithTools when tools are used."
+ )
+ text = _extract_text_from_content(create_message_result.content)
+ return SamplingResult(
+ text=text,
+ result=cast(ResultT, text),
+ history=current_messages,
+ )
- else:
- raise ValueError(
- f"Unexpected sampling handler result: {create_message_result}"
+ if isinstance(create_message_result, CreateMessageResultWithTools):
+ # If not a tool use, this is the final response
+ if create_message_result.stopReason != "toolUse" or not tool_map:
+ text = _extract_text_from_content(create_message_result.content)
+ return SamplingResult(
+ text=text,
+ result=cast(ResultT, text),
+ history=current_messages,
+ )
+
+ # Check if we've hit max iterations
+ if iteration >= max_iterations:
+ # Return what we have with None text (tool use without completion)
+ return SamplingResult(
+ text=None,
+ result=cast(ResultT, None),
+ history=current_messages,
+ )
+
+ # Execute tool calls
+ result = await self._execute_tool_calls(
+ result=create_message_result,
+ current_messages=current_messages,
+ tool_map=tool_map,
+ result_type=result_type,
+ final_response_tool=final_response_tool,
)
- result: CreateMessageResult = await self.session.create_message(
- messages=sampling_messages,
- system_prompt=system_prompt,
- include_context=include_context,
- temperature=temperature,
- max_tokens=max_tokens,
- model_preferences=_parse_model_preferences(model_preferences),
- related_request_id=self.request_id,
+ if isinstance(result, SamplingResult):
+ # final_response was called, return the structured result
+ return result
+
+ # result is a tuple of (messages, should_continue)
+ current_messages = cast(list[SamplingMessage], result[0])
+ should_continue = result[1]
+ if not should_continue:
+ text = _extract_text_from_content(create_message_result.content)
+ return SamplingResult(
+ text=text,
+ result=cast(ResultT, text),
+ history=current_messages,
+ )
+ continue
+
+ raise ValueError(
+ f"Unexpected sampling handler result: {create_message_result}"
+ )
+
+ async def _sample_with_client(
+ self,
+ sampling_messages: list[SamplingMessage],
+ system_prompt: str | None,
+ include_context: IncludeContext | None,
+ temperature: float | None,
+ max_tokens: int,
+ model_preferences: ModelPreferences | str | list[str] | None,
+ sdk_tools: list[SDKTool] | None,
+ tool_choice: ToolChoice | None,
+ tool_map: dict[str, SamplingTool],
+ max_iterations: int,
+ result_type: type[ResultT] | None,
+ final_response_tool: SamplingTool | None,
+ ) -> SamplingResult[ResultT] | SamplingResult[str]:
+ """Execute sampling with client, including automatic tool loop."""
+ current_messages = list(sampling_messages)
+ iteration = 0
+ has_tools = bool(sdk_tools)
+ has_result_type = result_type is not None and result_type is not str
+
+ while True:
+ # On last iteration, force completion
+ effective_tool_choice = tool_choice
+ if iteration == max_iterations - 1:
+ if has_result_type and final_response_tool is not None:
+ # Force final_response tool on last iteration
+ effective_tool_choice = ToolChoice(
+ mode="required", name="final_response"
+ )
+ elif has_tools:
+ # Force text response (no tools) on last iteration
+ effective_tool_choice = ToolChoice(mode="none")
+
+ result = await self.session.create_message(
+ messages=current_messages,
+ system_prompt=system_prompt,
+ include_context=include_context,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ model_preferences=_parse_model_preferences(model_preferences),
+ tools=sdk_tools,
+ tool_choice=effective_tool_choice,
+ related_request_id=self.request_id,
+ )
+ iteration += 1
+
+ # If not a tool use or no tools to execute, return text result
+ if result.stopReason != "toolUse" or not tool_map:
+ text = _extract_text_from_content(result.content)
+ return SamplingResult(
+ text=text,
+ result=cast(ResultT, text),
+ history=current_messages,
+ )
+
+ # Check if we've hit max iterations
+ if iteration >= max_iterations:
+ # Return what we have with None text (tool use without completion)
+ return SamplingResult(
+ text=None,
+ result=cast(ResultT, None),
+ history=current_messages,
+ )
+
+ # Execute tool calls
+ tool_result = await self._execute_tool_calls(
+ result=result,
+ current_messages=current_messages,
+ tool_map=tool_map,
+ result_type=result_type,
+ final_response_tool=final_response_tool,
+ )
+
+ if isinstance(tool_result, SamplingResult):
+ # final_response was called, return the structured result
+ return tool_result
+
+ # tool_result is a tuple of (messages, should_continue)
+ current_messages = cast(list[SamplingMessage], tool_result[0])
+ should_continue = tool_result[1]
+ if not should_continue:
+ text = _extract_text_from_content(result.content)
+ return SamplingResult(
+ text=text,
+ result=cast(ResultT, text),
+ history=current_messages,
+ )
+
+ async def _execute_tool_calls(
+ self,
+ result: CreateMessageResultWithTools,
+ current_messages: list[SamplingMessage],
+ tool_map: dict[str, SamplingTool],
+ result_type: type[ResultT] | None,
+ final_response_tool: SamplingTool | None,
+ ) -> (
+ tuple[list[SamplingMessage], bool]
+ | SamplingResult[ResultT]
+ | SamplingResult[str]
+ ):
+ """Execute tool calls from an LLM response and return updated messages.
+
+ Returns:
+ - Tuple of (updated_messages, should_continue) for normal tool calls
+ - SamplingResult when final_response tool is called with valid input
+ """
+ # Find all tool use content blocks
+ content_list = (
+ result.content if isinstance(result.content, list) else [result.content]
+ )
+ tool_use_blocks = [c for c in content_list if isinstance(c, ToolUseContent)]
+
+ if not tool_use_blocks:
+ return current_messages, False
+
+ # Add the assistant's response to messages
+ current_messages.append(
+ SamplingMessage(role="assistant", content=result.content)
)
- return result.content
+ # Execute each tool and collect results
+ for tool_use in tool_use_blocks:
+ # Check if this is the final_response tool
+ if (
+ tool_use.name == "final_response"
+ and final_response_tool is not None
+ and result_type is not None
+ and result_type is not str
+ ):
+ # Validate and parse the input as the result type
+ try:
+ type_adapter = get_cached_typeadapter(result_type)
+ validated_result = type_adapter.validate_python(tool_use.input)
+ # Convert to JSON for .text
+ text = json.dumps(
+ type_adapter.dump_python(validated_result, mode="json")
+ )
+ return SamplingResult(
+ text=text,
+ result=validated_result,
+ history=current_messages,
+ )
+ except Exception as e:
+ # Validation failed - add error and continue the loop
+ tool_result = ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[
+ TextContent(
+ type="text",
+ text=f"Validation error: {e}. Please try again.",
+ )
+ ],
+ isError=True,
+ )
+ current_messages.append(
+ SamplingMessage(role="user", content=tool_result)
+ )
+ continue
+
+ tool = tool_map.get(tool_use.name)
+ if tool is None:
+ # Tool not found - add error result
+ tool_result = ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[
+ TextContent(
+ type="text", text=f"Error: Unknown tool '{tool_use.name}'"
+ )
+ ],
+ isError=True,
+ )
+ else:
+ try:
+ result_value = await tool.run(tool_use.input)
+ tool_result = ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[TextContent(type="text", text=str(result_value))],
+ )
+ except Exception as e:
+ tool_result = ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[TextContent(type="text", text=f"Error: {e}")],
+ isError=True,
+ )
+
+ # Add tool result to messages
+ current_messages.append(SamplingMessage(role="user", content=tool_result))
+
+ return current_messages, True
@overload
async def elicit(
@@ -589,11 +1078,12 @@ async def elicit(
async def elicit(
self,
message: str,
- response_type: type[T] | list[str] | None = None,
+ response_type: type[T] | list[str] | dict[str, dict[str, str]] | None = None,
) -> (
AcceptedElicitation[T]
| AcceptedElicitation[dict[str, Any]]
| AcceptedElicitation[str]
+ | AcceptedElicitation[list[str]]
| DeclinedElicitation
| CancelledElicitation
):
@@ -653,6 +1143,25 @@ async def elicit(
if result.action == "accept":
if response_type is not None:
+ # Handle dict-based enum responses (direct value extraction)
+ if isinstance(response_type, dict):
+ # Single-select: result.content is {"value": "selected_value"}
+ value = result.content.get("value") if result.content else None
+ return AcceptedElicitation[str](data=cast(str, value))
+ elif isinstance(response_type, list) and len(response_type) == 1:
+ if isinstance(response_type[0], dict):
+ # Multi-select titled: result.content is {"value": ["selected1", "selected2"]}
+ value = result.content.get("value") if result.content else None
+ return AcceptedElicitation[list[str]](
+ data=cast(list[str], value)
+ )
+ elif isinstance(response_type[0], list):
+ # Multi-select untitled: result.content is {"value": ["selected1", "selected2"]}
+ value = result.content.get("value") if result.content else None
+ return AcceptedElicitation[list[str]](
+ data=cast(list[str], value)
+ )
+
type_adapter = get_cached_typeadapter(response_type)
validated_data = cast(
T | ScalarElicitationType[T],
@@ -783,3 +1292,46 @@ async def _log_to_server_and_client(
logger=logger_name,
related_request_id=related_request_id,
)
+
+
+def _create_final_response_tool(result_type: type) -> SamplingTool:
+ """Create a synthetic 'final_response' tool for structured output.
+
+ This tool is used to capture structured responses from the LLM.
+ The tool's schema is derived from the result_type.
+ """
+ type_adapter = get_cached_typeadapter(result_type)
+ schema = type_adapter.json_schema()
+ schema = compress_schema(schema, prune_titles=True)
+
+ # The fn just returns the input as-is (validation happens in the loop)
+ def final_response(**kwargs: Any) -> dict[str, Any]:
+ return kwargs
+
+ return SamplingTool(
+ name="final_response",
+ description=(
+ "Call this tool to provide your final response. "
+ "Use this when you have completed the task and are ready to return the result."
+ ),
+ parameters=schema,
+ fn=final_response,
+ )
+
+
+def _extract_text_from_content(
+ content: SamplingMessageContentBlock | list[SamplingMessageContentBlock],
+) -> str | None:
+ """Extract text from content block(s).
+
+ Returns the text if content is a TextContent or list containing TextContent,
+ otherwise returns None.
+ """
+ if isinstance(content, list):
+ for block in content:
+ if hasattr(block, "text"):
+ return block.text
+ return None
+ elif hasattr(content, "text"):
+ return content.text
+ return None
diff --git a/src/fastmcp/server/proxy.py b/src/fastmcp/server/proxy.py
index 90798a8c5b..a54cf256b4 100644
--- a/src/fastmcp/server/proxy.py
+++ b/src/fastmcp/server/proxy.py
@@ -555,15 +555,15 @@ async def default_sampling_handler(
A handler that forwards the sampling request from the remote server to the proxy's connected clients and relays the response back to the remote server.
"""
ctx = get_context()
- content = await ctx.sample(
+ result = await ctx.sample(
list(messages),
system_prompt=params.systemPrompt,
temperature=params.temperature,
max_tokens=params.maxTokens,
model_preferences=params.modelPreferences,
)
- if isinstance(content, mcp.types.ResourceLink | mcp.types.EmbeddedResource):
- raise RuntimeError("Content is not supported")
+ # Create TextContent from the result text
+ content = mcp.types.TextContent(type="text", text=result.text or "")
return mcp.types.CreateMessageResult(
role="assistant",
model="fastmcp-client",
diff --git a/src/fastmcp/server/sampling/__init__.py b/src/fastmcp/server/sampling/__init__.py
new file mode 100644
index 0000000000..f5238d2306
--- /dev/null
+++ b/src/fastmcp/server/sampling/__init__.py
@@ -0,0 +1,10 @@
+"""Sampling module for FastMCP servers."""
+
+from fastmcp.server.sampling.handler import ServerSamplingHandler
+from fastmcp.server.sampling.sampling_tool import SamplingTool, sampling_tool
+
+__all__ = [
+ "SamplingTool",
+ "ServerSamplingHandler",
+ "sampling_tool",
+]
diff --git a/src/fastmcp/server/sampling/handler.py b/src/fastmcp/server/sampling/handler.py
index 40dda9fc73..fe462c3b1e 100644
--- a/src/fastmcp/server/sampling/handler.py
+++ b/src/fastmcp/server/sampling/handler.py
@@ -5,8 +5,11 @@
from mcp.server.session import ServerSession
from mcp.shared.context import LifespanContextT, RequestContext
from mcp.types import CreateMessageRequestParams as SamplingParams
-from mcp.types import (
- SamplingMessage,
+from mcp.types import CreateMessageResultWithTools, SamplingMessage
+
+# Result type that handlers can return
+SamplingHandlerResult: TypeAlias = (
+ str | CreateMessageResult | CreateMessageResultWithTools
)
ServerSamplingHandler: TypeAlias = Callable[
@@ -15,5 +18,5 @@
SamplingParams,
RequestContext[ServerSession, LifespanContextT],
],
- str | CreateMessageResult | Awaitable[str | CreateMessageResult],
+ SamplingHandlerResult | Awaitable[SamplingHandlerResult],
]
diff --git a/src/fastmcp/server/sampling/sampling_tool.py b/src/fastmcp/server/sampling/sampling_tool.py
new file mode 100644
index 0000000000..85ce624270
--- /dev/null
+++ b/src/fastmcp/server/sampling/sampling_tool.py
@@ -0,0 +1,187 @@
+"""SamplingTool for use during LLM sampling requests."""
+
+from __future__ import annotations
+
+import inspect
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any, overload
+
+from mcp.types import Tool as SDKTool
+from pydantic import BaseModel, ConfigDict
+
+from fastmcp.tools.tool import ParsedFunction
+
+if TYPE_CHECKING:
+ from fastmcp.tools.tool import Tool
+
+
+class SamplingTool(BaseModel):
+ """A tool that can be used during LLM sampling.
+
+ SamplingTools bundle a tool's schema (name, description, parameters) with
+ an executor function, enabling servers to execute agentic workflows where
+ the LLM can request tool calls during sampling.
+
+ Create a SamplingTool using the @sampling_tool decorator or class methods:
+
+ @sampling_tool
+ def search(query: str) -> str:
+ '''Search the web.'''
+ return web_search(query)
+
+ # Or from an existing FastMCP Tool
+ sampling_tool = SamplingTool.from_mcp_tool(existing_tool)
+
+ # Then use in sampling
+ result = await context.sample(
+ messages="Find info about Python",
+ tools=[search],
+ )
+ """
+
+ name: str
+ description: str | None = None
+ parameters: dict[str, Any]
+ fn: Callable[..., Any]
+
+ model_config = ConfigDict(arbitrary_types_allowed=True)
+
+ async def run(self, arguments: dict[str, Any] | None = None) -> Any:
+ """Execute the tool with the given arguments.
+
+ Args:
+ arguments: Dictionary of arguments to pass to the tool function.
+
+ Returns:
+ The result of executing the tool function.
+ """
+ if arguments is None:
+ arguments = {}
+
+ result = self.fn(**arguments)
+ if inspect.isawaitable(result):
+ result = await result
+ return result
+
+ def _to_sdk_tool(self) -> SDKTool:
+ """Convert to an mcp.types.Tool for SDK compatibility.
+
+ This is used internally when passing tools to the MCP SDK's
+ create_message() method.
+ """
+ return SDKTool(
+ name=self.name,
+ description=self.description,
+ inputSchema=self.parameters,
+ )
+
+ @classmethod
+ def from_mcp_tool(cls, tool: Tool) -> SamplingTool:
+ """Create a SamplingTool from a FastMCP Tool.
+
+ Args:
+ tool: A FastMCP Tool instance (must have an fn attribute).
+
+ Returns:
+ A SamplingTool with the same schema and executor.
+
+ Raises:
+ ValueError: If the tool doesn't have an fn attribute.
+ """
+ if not hasattr(tool, "fn"):
+ raise ValueError(
+ f"Tool {tool.name!r} does not have an fn attribute. "
+ "Only FunctionTools can be converted to SamplingTools."
+ )
+
+ return cls(
+ name=tool.name,
+ description=tool.description,
+ parameters=tool.parameters,
+ fn=tool.fn,
+ )
+
+ @classmethod
+ def from_function(
+ cls,
+ fn: Callable[..., Any],
+ *,
+ name: str | None = None,
+ description: str | None = None,
+ ) -> SamplingTool:
+ """Create a SamplingTool from a function.
+
+ The function's signature is analyzed to generate a JSON schema for
+ the tool's parameters. Type hints are used to determine parameter types.
+
+ Args:
+ fn: The function to create a tool from.
+ name: Optional name override. Defaults to the function's name.
+ description: Optional description override. Defaults to the function's docstring.
+
+ Returns:
+ A SamplingTool wrapping the function.
+
+ Raises:
+ ValueError: If the function is a lambda without a name override.
+ """
+ parsed = ParsedFunction.from_function(fn, validate=True)
+
+ if name is None and parsed.name == "":
+ raise ValueError("You must provide a name for lambda functions")
+
+ return cls(
+ name=name or parsed.name,
+ description=description or parsed.description,
+ parameters=parsed.input_schema,
+ fn=parsed.fn,
+ )
+
+
+@overload
+def sampling_tool(fn: Callable[..., Any]) -> SamplingTool: ...
+
+
+@overload
+def sampling_tool(
+ *,
+ name: str | None = None,
+ description: str | None = None,
+) -> Callable[[Callable[..., Any]], SamplingTool]: ...
+
+
+def sampling_tool(
+ fn: Callable[..., Any] | None = None,
+ *,
+ name: str | None = None,
+ description: str | None = None,
+) -> SamplingTool | Callable[[Callable[..., Any]], SamplingTool]:
+ """Decorator to create a SamplingTool from a function.
+
+ Can be used with or without arguments:
+
+ @sampling_tool
+ def search(query: str) -> str:
+ '''Search the web.'''
+ return web_search(query)
+
+ @sampling_tool(name="web_search", description="Search the internet")
+ def search(query: str) -> str:
+ return web_search(query)
+
+ Args:
+ fn: The function to wrap (when used without parentheses).
+ name: Optional name override for the tool.
+ description: Optional description override for the tool.
+
+ Returns:
+ A SamplingTool if called directly on a function, or a decorator
+ if called with arguments.
+ """
+ if fn is not None:
+ return SamplingTool.from_function(fn)
+
+ def decorator(fn: Callable[..., Any]) -> SamplingTool:
+ return SamplingTool.from_function(fn, name=name, description=description)
+
+ return decorator
diff --git a/tests/client/test_sampling.py b/tests/client/test_sampling.py
index e131c0545e..dc470e55ab 100644
--- a/tests/client/test_sampling.py
+++ b/tests/client/test_sampling.py
@@ -3,11 +3,12 @@
from unittest.mock import AsyncMock
import pytest
-from mcp.types import TextContent
+from mcp.types import TextContent, ToolChoice
from pydantic_core import to_json
from fastmcp import Client, Context, FastMCP
from fastmcp.client.sampling import RequestContext, SamplingMessage, SamplingParams
+from fastmcp.server.sampling import SamplingTool, sampling_tool
from fastmcp.utilities.types import Image
@@ -163,3 +164,766 @@ def sampling_handler(
"_meta": None,
},
]
+
+
+class TestSamplingWithTools:
+ """Tests for sampling with tools functionality."""
+
+ async def test_sampling_with_tools_requires_capability(self):
+ """Test that sampling with tools raises error when client lacks capability."""
+ from fastmcp.exceptions import ToolError
+
+ mcp = FastMCP()
+
+ @sampling_tool
+ def search(query: str) -> str:
+ """Search the web."""
+ return f"Results for: {query}"
+
+ @mcp.tool
+ async def sample_with_tool(context: Context) -> str:
+ # This should fail because the client doesn't advertise tools capability
+ result = await context.sample(
+ messages="Search for Python tutorials",
+ tools=[search],
+ )
+ return str(result)
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> str:
+ return "Response"
+
+ async with Client(mcp, sampling_handler=sampling_handler) as client:
+ with pytest.raises(ToolError, match="sampling.tools capability"):
+ await client.call_tool("sample_with_tool", {})
+
+ async def test_sampling_with_tools_fallback_handler_must_return_correct_type(self):
+ """Test that fallback handler must return CreateMessageResultWithTools when tools provided."""
+ from fastmcp.exceptions import ToolError
+
+ # This handler returns a string, which is invalid when tools are provided
+ invalid_handler = AsyncMock(return_value="Fallback response")
+
+ mcp = FastMCP(sampling_handler=invalid_handler)
+
+ @sampling_tool
+ def search(query: str) -> str:
+ """Search the web."""
+ return f"Results for: {query}"
+
+ @mcp.tool
+ async def sample_with_tool(context: Context) -> str:
+ result = await context.sample(
+ messages="Search for Python tutorials",
+ tools=[search],
+ )
+ return str(result)
+
+ # Client without sampling handler - will use server's fallback
+ async with Client(mcp) as client:
+ # Handler returns string but tools were provided - should error
+ with pytest.raises(
+ ToolError, match="must return CreateMessageResultWithTools"
+ ):
+ await client.call_tool("sample_with_tool", {})
+
+ def test_sampling_tool_schema(self):
+ """Test that SamplingTool generates correct schema."""
+
+ @sampling_tool
+ def search(query: str, limit: int = 10) -> str:
+ """Search the web for results."""
+ return f"Results for: {query}"
+
+ assert search.name == "search"
+ assert search.description == "Search the web for results."
+ assert "query" in search.parameters.get("properties", {})
+ assert "limit" in search.parameters.get("properties", {})
+
+ async def test_sampling_tool_run(self):
+ """Test that SamplingTool.run() executes correctly."""
+
+ @sampling_tool
+ def add(a: int, b: int) -> int:
+ """Add two numbers."""
+ return a + b
+
+ result = await add.run({"a": 5, "b": 3})
+ assert result == 8
+
+ async def test_sampling_tool_run_async(self):
+ """Test that SamplingTool.run() works with async functions."""
+
+ @sampling_tool
+ async def async_multiply(a: int, b: int) -> int:
+ """Multiply two numbers."""
+ return a * b
+
+ result = await async_multiply.run({"a": 4, "b": 7})
+ assert result == 28
+
+ def test_sampling_tool_from_mcp_tool(self):
+ """Test creating SamplingTool from FastMCP Tool."""
+ from fastmcp.tools.tool import Tool
+
+ def original_fn(x: int, y: int) -> int:
+ """Add x and y."""
+ return x + y
+
+ mcp_tool = Tool.from_function(original_fn)
+ sampling = SamplingTool.from_mcp_tool(mcp_tool)
+
+ assert sampling.name == "original_fn"
+ assert sampling.description == "Add x and y."
+ assert sampling.fn is mcp_tool.fn
+
+ def test_tool_choice_parameter(self):
+ """Test that tool_choice parameter is accepted."""
+ # This is a basic type check - full integration requires tools capability
+
+ @sampling_tool
+ def search(query: str) -> str:
+ """Search."""
+ return query
+
+ # Just verify the ToolChoice type works
+ choice = ToolChoice(mode="required")
+ assert choice.mode == "required"
+
+ choice_auto = ToolChoice(mode="auto")
+ assert choice_auto.mode == "auto"
+
+ choice_none = ToolChoice(mode="none")
+ assert choice_none.mode == "none"
+
+
+class TestAutomaticToolLoop:
+ """Tests for automatic tool execution loop in ctx.sample()."""
+
+ async def test_automatic_tool_loop_executes_tools(self):
+ """Test that ctx.sample() automatically executes tool calls."""
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+
+ call_count = 0
+ tool_was_called = False
+
+ @sampling_tool
+ def get_weather(city: str) -> str:
+ """Get weather for a city."""
+ nonlocal tool_was_called
+ tool_was_called = True
+ return f"Weather in {city}: sunny, 72°F"
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ nonlocal call_count
+ call_count += 1
+
+ if call_count == 1:
+ # First call: return tool use
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="get_weather",
+ input={"city": "Seattle"},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+ else:
+ # Second call: return final response
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="The weather is sunny!")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def weather_assistant(question: str, context: Context) -> str:
+ result = await context.sample(
+ messages=question,
+ tools=[get_weather],
+ )
+ # Get text from SamplingResult
+ return result.text or ""
+
+ async with Client(mcp) as client:
+ result = await client.call_tool(
+ "weather_assistant", {"question": "What's the weather?"}
+ )
+
+ assert tool_was_called
+ assert call_count == 2
+ assert result.data == "The weather is sunny!"
+
+ async def test_automatic_tool_loop_multiple_tools(self):
+ """Test that multiple tool calls in one response are all executed."""
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+
+ executed_tools: list[str] = []
+
+ @sampling_tool
+ def tool_a(x: int) -> int:
+ """Tool A."""
+ executed_tools.append(f"tool_a({x})")
+ return x * 2
+
+ @sampling_tool
+ def tool_b(y: int) -> int:
+ """Tool B."""
+ executed_tools.append(f"tool_b({y})")
+ return y + 10
+
+ call_count = 0
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ nonlocal call_count
+ call_count += 1
+
+ if call_count == 1:
+ # Return multiple tool calls
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use", id="call_a", name="tool_a", input={"x": 5}
+ ),
+ ToolUseContent(
+ type="tool_use", id="call_b", name="tool_b", input={"y": 3}
+ ),
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+ else:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="Done!")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def multi_tool(context: Context) -> str:
+ result = await context.sample(messages="Run tools", tools=[tool_a, tool_b])
+ return result.text or ""
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("multi_tool", {})
+
+ assert executed_tools == ["tool_a(5)", "tool_b(3)"]
+ assert result.data == "Done!"
+
+ async def test_automatic_tool_loop_max_iterations(self):
+ """Test that max_iterations prevents infinite loops.
+
+ On the last iteration, tool_choice is set to 'none' to force text response.
+ """
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+
+ call_count = 0
+ received_tool_choices: list = []
+
+ @sampling_tool
+ def looping_tool() -> str:
+ """A tool that always gets called again."""
+ return "keep going"
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ nonlocal call_count
+ call_count += 1
+ received_tool_choices.append(params.toolChoice)
+
+ # On last iteration (when tool_choice=none), return text
+ if params.toolChoice and params.toolChoice.mode == "none":
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="Forced to stop")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ # Otherwise keep returning tool use
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id=f"call_{call_count}",
+ name="looping_tool",
+ input={},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def infinite_loop(context: Context) -> str:
+ result = await context.sample(
+ messages="Start", tools=[looping_tool], max_iterations=3
+ )
+ return result.text or "no text"
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("infinite_loop", {})
+
+ # Should complete after 3 iterations with forced text response
+ assert call_count == 3
+ assert result.data == "Forced to stop"
+ # Last call should have tool_choice=none
+ assert received_tool_choices[-1].mode == "none"
+
+ async def test_automatic_tool_loop_handles_unknown_tool(self):
+ """Test that unknown tool names result in error being passed to LLM."""
+ from mcp.types import (
+ CreateMessageResultWithTools,
+ ToolResultContent,
+ ToolUseContent,
+ )
+
+ @sampling_tool
+ def known_tool() -> str:
+ """A known tool."""
+ return "known result"
+
+ messages_received: list[list[SamplingMessage]] = []
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ messages_received.append(list(messages))
+
+ if len(messages_received) == 1:
+ # Request unknown tool
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="unknown_tool",
+ input={},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+ else:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="Handled error")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def test_unknown(context: Context) -> str:
+ result = await context.sample(messages="Test", tools=[known_tool])
+ return result.text or ""
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("test_unknown", {})
+
+ # Check that error was passed back in messages
+ assert len(messages_received) == 2
+ last_messages = messages_received[1]
+ # Find the tool result message
+ tool_result_msg = None
+ for msg in last_messages:
+ if isinstance(msg.content, ToolResultContent):
+ tool_result_msg = msg
+ break
+ assert tool_result_msg is not None
+ assert tool_result_msg.content.isError is True # type: ignore[union-attr]
+ # Content is list of TextContent objects
+ error_text = tool_result_msg.content.content[0].text # type: ignore[union-attr]
+ assert "Unknown tool" in error_text
+ assert result.data == "Handled error"
+
+ async def test_automatic_tool_loop_handles_tool_exception(self):
+ """Test that tool exceptions are caught and passed to LLM as errors."""
+ from mcp.types import (
+ CreateMessageResultWithTools,
+ ToolResultContent,
+ ToolUseContent,
+ )
+
+ @sampling_tool
+ def failing_tool() -> str:
+ """A tool that raises an exception."""
+ raise ValueError("Tool failed intentionally")
+
+ messages_received: list[list[SamplingMessage]] = []
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ messages_received.append(list(messages))
+
+ if len(messages_received) == 1:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="failing_tool",
+ input={},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+ else:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="Handled error")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def test_exception(context: Context) -> str:
+ result = await context.sample(messages="Test", tools=[failing_tool])
+ return result.text or ""
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("test_exception", {})
+
+ # Check that error was passed back
+ assert len(messages_received) == 2
+ last_messages = messages_received[1]
+ tool_result_msg = None
+ for msg in last_messages:
+ if isinstance(msg.content, ToolResultContent):
+ tool_result_msg = msg
+ break
+ assert tool_result_msg is not None
+ assert tool_result_msg.content.isError is True # type: ignore[union-attr]
+ # Content is list of TextContent objects
+ error_text = tool_result_msg.content.content[0].text # type: ignore[union-attr]
+ assert "Tool failed intentionally" in error_text
+ assert result.data == "Handled error"
+
+ async def test_max_iterations_one_for_manual_loop(self):
+ """Test that max_iterations=1 forces text on first call for manual loop building.
+
+ With max_iterations=1, tool_choice is set to 'none' on the first (and only) call,
+ forcing a text response so users can build their own loop using history.
+ """
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+
+ received_tool_choices: list = []
+
+ @sampling_tool
+ def my_tool() -> str:
+ """A tool."""
+ return "result"
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ received_tool_choices.append(params.toolChoice)
+
+ # With tool_choice=none, return text response
+ if params.toolChoice and params.toolChoice.mode == "none":
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="Forced text response")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ # Otherwise return tool use (shouldn't happen with max_iterations=1)
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use", id="call_1", name="my_tool", input={}
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def single_iteration(context: Context) -> str:
+ result = await context.sample(
+ messages="Test", tools=[my_tool], max_iterations=1
+ )
+ return result.text or "no text"
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("single_iteration", {})
+
+ # Should get forced text response
+ assert result.data == "Forced text response"
+ # First and only call should have tool_choice=none
+ assert len(received_tool_choices) == 1
+ assert received_tool_choices[0].mode == "none"
+
+
+class TestSamplingResultType:
+ """Tests for result_type parameter (structured output)."""
+
+ async def test_result_type_creates_final_response_tool(self):
+ """Test that result_type creates a synthetic final_response tool."""
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+ from pydantic import BaseModel
+
+ class MathResult(BaseModel):
+ answer: int
+ explanation: str
+
+ received_tools: list = []
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ received_tools.extend(params.tools or [])
+
+ # Return the final_response tool call
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="final_response",
+ input={"answer": 42, "explanation": "The meaning of life"},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def math_tool(context: Context) -> str:
+ result = await context.sample(
+ messages="What is 6 * 7?",
+ result_type=MathResult,
+ )
+ # result.result should be a MathResult object
+ return f"{result.result.answer}: {result.result.explanation}" # type: ignore[attr-defined]
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("math_tool", {})
+
+ # Check that final_response tool was added
+ tool_names = [t.name for t in received_tools]
+ assert "final_response" in tool_names
+
+ # Check the result
+ assert result.data == "42: The meaning of life"
+
+ async def test_result_type_with_user_tools(self):
+ """Test result_type works alongside user-provided tools."""
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+ from pydantic import BaseModel
+
+ class SearchResult(BaseModel):
+ summary: str
+ sources: list[str]
+
+ @sampling_tool
+ def search(query: str) -> str:
+ """Search for information."""
+ return f"Found info about: {query}"
+
+ call_count = 0
+ tool_was_called = False
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ nonlocal call_count, tool_was_called
+ call_count += 1
+
+ if call_count == 1:
+ # First call: use the search tool
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="search",
+ input={"query": "Python tutorials"},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+ else:
+ # Second call: call final_response
+ tool_was_called = True
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_2",
+ name="final_response",
+ input={
+ "summary": "Python is great",
+ "sources": ["python.org", "docs.python.org"],
+ },
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def research(context: Context) -> str:
+ result = await context.sample(
+ messages="Research Python",
+ tools=[search],
+ result_type=SearchResult,
+ )
+ return f"{result.result.summary} - {len(result.result.sources)} sources" # type: ignore[attr-defined]
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("research", {})
+
+ assert tool_was_called
+ assert result.data == "Python is great - 2 sources"
+
+ async def test_result_type_validation_error_retries(self):
+ """Test that validation errors are sent back to LLM for retry."""
+ from mcp.types import (
+ CreateMessageResultWithTools,
+ ToolResultContent,
+ ToolUseContent,
+ )
+ from pydantic import BaseModel
+
+ class StrictResult(BaseModel):
+ value: int # Must be an int
+
+ messages_received: list[list[SamplingMessage]] = []
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ messages_received.append(list(messages))
+
+ if len(messages_received) == 1:
+ # First call: invalid type
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="final_response",
+ input={"value": "not_an_int"}, # Wrong type
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+ else:
+ # Second call: valid type after seeing error
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_2",
+ name="final_response",
+ input={"value": 42}, # Correct type
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def validate_tool(context: Context) -> str:
+ result = await context.sample(
+ messages="Give me a number",
+ result_type=StrictResult,
+ )
+ return str(result.result.value) # type: ignore[attr-defined]
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("validate_tool", {})
+
+ # Should have retried after validation error
+ assert len(messages_received) == 2
+
+ # Check that error was passed back
+ last_messages = messages_received[1]
+ tool_result_msg = None
+ for msg in last_messages:
+ if isinstance(msg.content, ToolResultContent):
+ tool_result_msg = msg
+ break
+ assert tool_result_msg is not None
+ assert tool_result_msg.content.isError is True # type: ignore[union-attr]
+ error_text = tool_result_msg.content.content[0].text # type: ignore[union-attr]
+ assert "Validation error" in error_text
+
+ # Final result should be correct
+ assert result.data == "42"
+
+ async def test_sampling_result_has_text_and_history(self):
+ """Test that SamplingResult has text, result, and history attributes."""
+ from mcp.types import CreateMessageResultWithTools
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="Hello world")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def check_result(context: Context) -> str:
+ result = await context.sample(messages="Say hello")
+ # Check all attributes exist
+ assert result.text == "Hello world"
+ assert result.result == "Hello world"
+ assert len(result.history) >= 1
+ return "ok"
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("check_result", {})
+
+ assert result.data == "ok"
diff --git a/tests/server/sampling/__init__.py b/tests/server/sampling/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/server/sampling/test_sampling_tool.py b/tests/server/sampling/test_sampling_tool.py
new file mode 100644
index 0000000000..19808ecd26
--- /dev/null
+++ b/tests/server/sampling/test_sampling_tool.py
@@ -0,0 +1,182 @@
+"""Tests for SamplingTool."""
+
+import pytest
+
+from fastmcp.server.sampling import SamplingTool, sampling_tool
+from fastmcp.tools.tool import Tool
+
+
+class TestSamplingToolFromFunction:
+ """Tests for SamplingTool.from_function()."""
+
+ def test_from_simple_function(self):
+ def search(query: str) -> str:
+ """Search the web."""
+ return f"Results for: {query}"
+
+ tool = SamplingTool.from_function(search)
+
+ assert tool.name == "search"
+ assert tool.description == "Search the web."
+ assert "query" in tool.parameters.get("properties", {})
+ assert tool.fn is search
+
+ def test_from_function_with_overrides(self):
+ def search(query: str) -> str:
+ return f"Results for: {query}"
+
+ tool = SamplingTool.from_function(
+ search,
+ name="web_search",
+ description="Search the internet",
+ )
+
+ assert tool.name == "web_search"
+ assert tool.description == "Search the internet"
+
+ def test_from_lambda_requires_name(self):
+ with pytest.raises(ValueError, match="must provide a name for lambda"):
+ SamplingTool.from_function(lambda x: x)
+
+ def test_from_lambda_with_name(self):
+ tool = SamplingTool.from_function(lambda x: x * 2, name="double")
+
+ assert tool.name == "double"
+
+ def test_from_async_function(self):
+ async def async_search(query: str) -> str:
+ """Async search."""
+ return f"Async results for: {query}"
+
+ tool = SamplingTool.from_function(async_search)
+
+ assert tool.name == "async_search"
+ assert tool.description == "Async search."
+
+ def test_multiple_parameters(self):
+ def search(query: str, limit: int = 10, include_images: bool = False) -> str:
+ """Search with options."""
+ return f"Results for: {query}"
+
+ tool = SamplingTool.from_function(search)
+ props = tool.parameters.get("properties", {})
+
+ assert "query" in props
+ assert "limit" in props
+ assert "include_images" in props
+
+
+class TestSamplingToolFromMCPTool:
+ """Tests for SamplingTool.from_mcp_tool()."""
+
+ def test_from_function_tool(self):
+ def original(x: int) -> int:
+ """Double a number."""
+ return x * 2
+
+ mcp_tool = Tool.from_function(original)
+ sampling = SamplingTool.from_mcp_tool(mcp_tool)
+
+ assert sampling.name == "original"
+ assert sampling.description == "Double a number."
+ assert sampling.fn is mcp_tool.fn
+
+ def test_from_tool_without_fn_raises(self):
+ # Create a base Tool without fn (not a FunctionTool)
+ tool = Tool(
+ name="test",
+ description="Test tool",
+ parameters={"type": "object"},
+ tags=set(),
+ )
+
+ with pytest.raises(ValueError, match="does not have an fn attribute"):
+ SamplingTool.from_mcp_tool(tool)
+
+
+class TestSamplingToolDecorator:
+ """Tests for the @sampling_tool decorator."""
+
+ def test_decorator_without_args(self):
+ @sampling_tool
+ def search(query: str) -> str:
+ """Search the web."""
+ return f"Results for: {query}"
+
+ assert isinstance(search, SamplingTool)
+ assert search.name == "search"
+ assert search.description == "Search the web."
+
+ def test_decorator_with_args(self):
+ @sampling_tool(name="web_search", description="Custom description")
+ def search(query: str) -> str:
+ return f"Results for: {query}"
+
+ assert isinstance(search, SamplingTool)
+ assert search.name == "web_search"
+ assert search.description == "Custom description"
+
+ def test_decorator_with_partial_args(self):
+ @sampling_tool(name="custom_name")
+ def search(query: str) -> str:
+ """Original docstring."""
+ return f"Results for: {query}"
+
+ assert search.name == "custom_name"
+ assert search.description == "Original docstring."
+
+
+class TestSamplingToolRun:
+ """Tests for SamplingTool.run()."""
+
+ async def test_run_sync_function(self):
+ @sampling_tool
+ def add(a: int, b: int) -> int:
+ """Add two numbers."""
+ return a + b
+
+ result = await add.run({"a": 2, "b": 3})
+ assert result == 5
+
+ async def test_run_async_function(self):
+ @sampling_tool
+ async def async_add(a: int, b: int) -> int:
+ """Add two numbers asynchronously."""
+ return a + b
+
+ result = await async_add.run({"a": 2, "b": 3})
+ assert result == 5
+
+ async def test_run_with_no_arguments(self):
+ @sampling_tool
+ def get_value() -> str:
+ """Return a fixed value."""
+ return "hello"
+
+ result = await get_value.run()
+ assert result == "hello"
+
+ async def test_run_with_none_arguments(self):
+ @sampling_tool
+ def get_value() -> str:
+ """Return a fixed value."""
+ return "hello"
+
+ result = await get_value.run(None)
+ assert result == "hello"
+
+
+class TestSamplingToolSDKConversion:
+ """Tests for SamplingTool._to_sdk_tool() internal method."""
+
+ def test_to_sdk_tool(self):
+ @sampling_tool
+ def search(query: str) -> str:
+ """Search the web."""
+ return f"Results for: {query}"
+
+ sdk_tool = search._to_sdk_tool()
+
+ assert sdk_tool.name == "search"
+ assert sdk_tool.description == "Search the web."
+ assert "query" in sdk_tool.inputSchema.get("properties", {})
diff --git a/tests/server/test_context.py b/tests/server/test_context.py
index f53797326c..45275f835a 100644
--- a/tests/server/test_context.py
+++ b/tests/server/test_context.py
@@ -17,16 +17,16 @@ def context():
class TestParseModelPreferences:
def test_parse_model_preferences_string(self, context):
- mp = _parse_model_preferences("claude-3-sonnet")
+ mp = _parse_model_preferences("claude-haiku-4-5")
assert isinstance(mp, ModelPreferences)
assert mp.hints is not None
- assert mp.hints[0].name == "claude-3-sonnet"
+ assert mp.hints[0].name == "claude-haiku-4-5"
def test_parse_model_preferences_list(self, context):
- mp = _parse_model_preferences(["claude-3-sonnet", "claude"])
+ mp = _parse_model_preferences(["claude-haiku-4-5", "claude"])
assert isinstance(mp, ModelPreferences)
assert mp.hints is not None
- assert [h.name for h in mp.hints] == ["claude-3-sonnet", "claude"]
+ assert [h.name for h in mp.hints] == ["claude-haiku-4-5", "claude"]
def test_parse_model_preferences_object(self, context):
obj = ModelPreferences(hints=[])
diff --git a/uv.lock b/uv.lock
index 808099901d..fdc38f3cd6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -6,6 +6,15 @@ resolution-markers = [
"python_full_version < '3.11'",
]
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
[[package]]
name = "annotated-types"
version = "0.7.0"
@@ -17,35 +26,34 @@ wheels = [
[[package]]
name = "anyio"
-version = "4.10.0"
+version = "4.12.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "exceptiongroup", marker = "python_full_version < '3.11'" },
{ name = "idna" },
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{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
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+ { url = "https://files.pythonhosted.org/packages/ee/d9/d88e73ca598f4f6ff671fb5fde8a32925c2e08a637303a1d12883c7305fa/uvicorn-0.38.0-py3-none-any.whl", hash = "sha256:48c0afd214ceb59340075b4a052ea1ee91c16fbc2a9b1469cca0e54566977b02", size = 68109, upload-time = "2025-10-18T13:46:42.958Z" },
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name = "wcwidth"
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From 0d8679c0ad32ae65224f7b049de399ae8873e27a Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Thu, 4 Dec 2025 12:41:19 -0500
Subject: [PATCH 03/15] SEP-1577: Sampling with tools
- Add tools and result_type parameters to ctx.sample()
- Update OpenAI handler for tool content types
- Client advertises sampling.tools capability by default
- Collect tool results into single message with list content
---
docs/clients/sampling.mdx | 4 +
docs/servers/sampling.mdx | 37 ++---
examples/advanced_sampling/README.md | 28 ++++
.../advanced_sampling/structured_output.py | 68 ++++++++++
examples/advanced_sampling/tool_use.py | 88 ++++++++++++
pyproject.toml | 2 +-
src/fastmcp/client/client.py | 19 ++-
src/fastmcp/client/transports.py | 1 +
.../experimental/sampling/handlers/openai.py | 126 ++++++++++++++++--
src/fastmcp/server/context.py | 97 ++++++++------
tests/client/test_sampling.py | 97 ++++++++------
11 files changed, 460 insertions(+), 107 deletions(-)
create mode 100644 examples/advanced_sampling/README.md
create mode 100644 examples/advanced_sampling/structured_output.py
create mode 100644 examples/advanced_sampling/tool_use.py
diff --git a/docs/clients/sampling.mdx b/docs/clients/sampling.mdx
index 4f87a8e8c7..11a9a1bd23 100644
--- a/docs/clients/sampling.mdx
+++ b/docs/clients/sampling.mdx
@@ -163,6 +163,10 @@ client = Client(
If the client doesn't provide a sampling handler, servers can optionally configure a fallback handler. See [Server Sampling](/servers/sampling#sampling-fallback-handler) for details.
+
+When you provide a `sampling_handler`, FastMCP automatically advertises full sampling capabilities including tool support to the server.
+
+
## Handling Tool Requests
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index f36e2073f3..c52bf7ea5e 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -3,6 +3,7 @@ title: LLM Sampling
sidebarTitle: Sampling
description: Request LLM text generation from the client or a configured provider through the MCP context.
icon: robot
+tag: NEW
---
import { VersionBadge } from '/snippets/version-badge.mdx'
@@ -85,8 +86,8 @@ async def analyze_sentiment(text: str, ctx: Context) -> dict:
Optional list of tools the LLM can use during sampling. See [Sampling with Tools](#sampling-with-tools).
-
- Optional control over tool usage behavior (`auto`, `required`, or `none`)
+
+ Optional control over tool usage behavior: `"auto"`, `"required"`, or `"none"`
@@ -259,9 +260,10 @@ def search(query: str) -> str:
return f"Search results for: {query}"
@sampling_tool
-async def calculate(expression: str) -> float:
- """Evaluate a mathematical expression."""
- return eval(expression) # In production, use a safe math parser
+def get_current_time() -> str:
+ """Get the current time."""
+ from datetime import datetime
+ return datetime.now().strftime("%H:%M:%S")
```
You can also pass existing FastMCP tools directly to `ctx.sample()`:
@@ -298,9 +300,10 @@ def search(query: str) -> str:
return f"Results for: {query}"
@sampling_tool
-def calculate(expression: str) -> float:
- """Calculate a math expression."""
- return eval(expression)
+def get_time() -> str:
+ """Get the current time."""
+ from datetime import datetime
+ return datetime.now().strftime("%H:%M:%S")
mcp = FastMCP()
@@ -310,7 +313,7 @@ async def research_assistant(question: str, ctx: Context) -> str:
result = await ctx.sample(
messages=question,
system_prompt="You are a research assistant. Use the available tools to answer questions.",
- tools=[search, calculate],
+ tools=[search, get_time],
)
return result.text
@@ -332,7 +335,7 @@ Use `max_iterations` to limit the number of LLM calls (default is 10). On the la
# Allow up to 5 tool-use cycles
result = await ctx.sample(
messages=prompt,
- tools=[search, calculate],
+ tools=[search, get_time],
max_iterations=5,
)
@@ -340,7 +343,7 @@ result = await ctx.sample(
# Useful for building your own loop with result.history
result = await ctx.sample(
messages=prompt,
- tools=[search, calculate],
+ tools=[search, get_time],
max_iterations=1,
)
```
@@ -350,16 +353,14 @@ result = await ctx.sample(
Control how the LLM uses tools with `tool_choice`:
```python
-from mcp.types import ToolChoice
-
# Let the LLM decide (default)
-result = await ctx.sample(messages=prompt, tools=tools, tool_choice=ToolChoice(mode="auto"))
+result = await ctx.sample(messages=prompt, tools=tools, tool_choice="auto")
# Force the LLM to use a tool
-result = await ctx.sample(messages=prompt, tools=tools, tool_choice=ToolChoice(mode="required"))
+result = await ctx.sample(messages=prompt, tools=tools, tool_choice="required")
# Prevent tool use (useful for final response)
-result = await ctx.sample(messages=prompt, tools=tools, tool_choice=ToolChoice(mode="none"))
+result = await ctx.sample(messages=prompt, tools=tools, tool_choice="none")
```
### Structured Output
@@ -419,7 +420,7 @@ This is useful when you need to:
async def custom_agent(question: str, ctx: Context) -> str:
"""Agent with custom loop logic."""
history = []
- tools = [search, calculate]
+ tools = [search, get_time]
while True:
result = await ctx.sample(
@@ -576,5 +577,5 @@ By default, LLM sampling requires client support:
- Use `sampling_handler_behavior="always"` to completely bypass the client and control which LLM is used
-Sampling with tools requires either a client that advertises the `sampling.tools` capability, or a fallback handler configured with `sampling_handler_behavior="always"`. Most current MCP clients don't yet advertise tool support, so using a fallback handler is the recommended approach for tool-enabled sampling.
+Sampling with tools requires a client that advertises the `sampling.tools` capability. FastMCP clients automatically advertise this capability when a `sampling_handler` is provided. For external clients that don't support tool-enabled sampling, configure a fallback handler with `sampling_handler_behavior="always"`.
diff --git a/examples/advanced_sampling/README.md b/examples/advanced_sampling/README.md
new file mode 100644
index 0000000000..4ffa247c7f
--- /dev/null
+++ b/examples/advanced_sampling/README.md
@@ -0,0 +1,28 @@
+# Advanced Sampling Examples
+
+These examples demonstrate FastMCP's sampling API with real LLM backends.
+
+## Prerequisites
+
+```bash
+pip install fastmcp[openai]
+export OPENAI_API_KEY=your-key
+```
+
+## Examples
+
+### Structured Output (`structured_output.py`)
+
+Uses `result_type` to get validated Pydantic models from the LLM:
+
+```bash
+python examples/advanced_sampling/structured_output.py
+```
+
+### Tool Use (`tool_use.py`)
+
+Gives the LLM tools to use during sampling, with automatic tool execution:
+
+```bash
+python examples/advanced_sampling/tool_use.py
+```
diff --git a/examples/advanced_sampling/structured_output.py b/examples/advanced_sampling/structured_output.py
new file mode 100644
index 0000000000..6a27568178
--- /dev/null
+++ b/examples/advanced_sampling/structured_output.py
@@ -0,0 +1,68 @@
+"""
+Structured Output Example
+
+This example demonstrates using `result_type` to get structured responses from an LLM.
+The server exposes a tool that uses sampling to analyze text sentiment, returning
+a structured Pydantic model instead of raw text.
+
+Prerequisites:
+ pip install fastmcp[openai]
+ export OPENAI_API_KEY=your-key
+
+Run:
+ python examples/advanced_sampling/structured_output.py
+"""
+
+import asyncio
+
+from pydantic import BaseModel
+
+from fastmcp import Client, Context, FastMCP
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+
+
+# Define a structured output model
+class SentimentAnalysis(BaseModel):
+ sentiment: str # "positive", "negative", or "neutral"
+ confidence: float # 0.0 to 1.0
+ keywords: list[str] # Key words that influenced the analysis
+
+
+# Create an MCP server with a sampling tool
+mcp = FastMCP("Sentiment Analyzer")
+
+
+@mcp.tool
+async def analyze_sentiment(text: str, ctx: Context) -> dict:
+ """Analyze the sentiment of the given text."""
+ result = await ctx.sample(
+ messages=f"Analyze the sentiment of this text:\n\n{text}",
+ system_prompt="You are a sentiment analysis expert. Analyze text and return structured results.",
+ result_type=SentimentAnalysis,
+ )
+ # result.result is a validated SentimentAnalysis instance
+ return result.result.model_dump() # type: ignore[attr-defined]
+
+
+async def main():
+ # Create an OpenAI-backed sampling handler
+ sampling_handler = OpenAISamplingHandler(default_model="gpt-4o-mini")
+
+ # Connect to the server with the sampling handler
+ async with Client(mcp, sampling_handler=sampling_handler) as client:
+ # Call the tool with some test text
+ result = await client.call_tool(
+ "analyze_sentiment",
+ {
+ "text": "I absolutely love this product! It exceeded all my expectations."
+ },
+ )
+
+ print("Analysis Result:")
+ print(f" Sentiment: {result.data['sentiment']}")
+ print(f" Confidence: {result.data['confidence']:.1%}")
+ print(f" Keywords: {', '.join(result.data['keywords'])}")
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/examples/advanced_sampling/tool_use.py b/examples/advanced_sampling/tool_use.py
new file mode 100644
index 0000000000..8629f0fb3d
--- /dev/null
+++ b/examples/advanced_sampling/tool_use.py
@@ -0,0 +1,88 @@
+"""
+Tool Use Example
+
+This example demonstrates sampling with tools, where the LLM can use helper
+functions to complete a task. The server exposes a "research assistant" tool
+that uses sampling with search capabilities.
+
+Prerequisites:
+ pip install fastmcp[openai]
+ export OPENAI_API_KEY=your-key
+
+Run:
+ python examples/advanced_sampling/tool_use.py
+"""
+
+import asyncio
+
+from pydantic import BaseModel
+
+from fastmcp import Client, Context, FastMCP
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+from fastmcp.server.sampling import sampling_tool
+
+
+# Define sampling tools (available to the LLM during sampling)
+@sampling_tool
+def search_web(query: str) -> str:
+ """Search the web for information."""
+ # Simulated search results
+ results = {
+ "python async": "Python's asyncio provides async/await syntax for concurrent code.",
+ "fastmcp": "FastMCP is a framework for building MCP servers and clients in Python.",
+ "mcp protocol": "MCP (Model Context Protocol) enables AI models to use tools and resources.",
+ }
+ for key, value in results.items():
+ if key in query.lower():
+ return value
+ return f"No results found for: {query}"
+
+
+@sampling_tool
+def get_word_count(text: str) -> str:
+ """Count words in text."""
+ return str(len(text.split()))
+
+
+# Structured output for the final response
+class ResearchReport(BaseModel):
+ summary: str
+ sources_used: list[str]
+ confidence: float
+
+
+# Create the MCP server
+mcp = FastMCP("Research Assistant")
+
+
+@mcp.tool
+async def research(question: str, ctx: Context) -> dict:
+ """Research a question using available tools and return a structured report."""
+ result = await ctx.sample(
+ messages=f"Research this question and provide a comprehensive answer:\n\n{question}",
+ system_prompt="You are a research assistant. Use the available tools to gather information, then call final_response with your structured report.",
+ tools=[search_web, get_word_count],
+ result_type=ResearchReport,
+ max_iterations=5,
+ )
+
+ return result.result.model_dump() # type: ignore[attr-defined]
+
+
+async def main():
+ sampling_handler = OpenAISamplingHandler(default_model="gpt-4o-mini")
+
+ async with Client(mcp, sampling_handler=sampling_handler) as client:
+ result = await client.call_tool(
+ "research",
+ {"question": "What is FastMCP and how does it relate to the MCP protocol?"},
+ )
+
+ print("Research Report:")
+ print(f" Summary: {result.data['summary']}")
+ print(f" Sources: {', '.join(result.data['sources_used'])}")
+ print(f" Confidence: {result.data['confidence']}%")
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/pyproject.toml b/pyproject.toml
index 93b5043de2..35c7abb640 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ dependencies = [
"python-dotenv>=1.1.0",
"exceptiongroup>=1.2.2",
"httpx>=0.28.1",
- "mcp>=1.23.1",
+ "mcp>=1.23.3",
"openapi-pydantic>=0.5.1",
"platformdirs>=4.0.0",
"rich>=13.9.4",
diff --git a/src/fastmcp/client/client.py b/src/fastmcp/client/client.py
index 90bbd32d97..1e946595c4 100644
--- a/src/fastmcp/client/client.py
+++ b/src/fastmcp/client/client.py
@@ -227,6 +227,7 @@ def __init__(
name: str | None = None,
roots: RootsList | RootsHandler | None = None,
sampling_handler: ClientSamplingHandler | None = None,
+ sampling_capabilities: mcp.types.SamplingCapability | None = None,
elicitation_handler: ElicitationHandler | None = None,
log_handler: LogHandler | None = None,
message_handler: MessageHandlerT | MessageHandler | None = None,
@@ -278,6 +279,13 @@ def __init__(
self._session_kwargs["sampling_callback"] = create_sampling_callback(
sampling_handler
)
+ # Default to tools-enabled capabilities unless explicitly overridden
+ self._session_kwargs["sampling_capabilities"] = (
+ sampling_capabilities
+ or mcp.types.SamplingCapability(
+ tools=mcp.types.SamplingToolsCapability()
+ )
+ )
if elicitation_handler is not None:
self._session_kwargs["elicitation_callback"] = create_elicitation_callback(
@@ -306,11 +314,20 @@ def set_roots(self, roots: RootsList | RootsHandler) -> None:
"""Set the roots for the client. This does not automatically call `send_roots_list_changed`."""
self._session_kwargs["list_roots_callback"] = create_roots_callback(roots)
- def set_sampling_callback(self, sampling_callback: ClientSamplingHandler) -> None:
+ def set_sampling_callback(
+ self,
+ sampling_callback: ClientSamplingHandler,
+ sampling_capabilities: mcp.types.SamplingCapability | None = None,
+ ) -> None:
"""Set the sampling callback for the client."""
self._session_kwargs["sampling_callback"] = create_sampling_callback(
sampling_callback
)
+ # Default to tools-enabled capabilities unless explicitly overridden
+ self._session_kwargs["sampling_capabilities"] = (
+ sampling_capabilities
+ or mcp.types.SamplingCapability(tools=mcp.types.SamplingToolsCapability())
+ )
def set_elicitation_callback(
self, elicitation_callback: ElicitationHandler
diff --git a/src/fastmcp/client/transports.py b/src/fastmcp/client/transports.py
index 81afc9c88b..7a3d99b08a 100644
--- a/src/fastmcp/client/transports.py
+++ b/src/fastmcp/client/transports.py
@@ -65,6 +65,7 @@ class SessionKwargs(TypedDict, total=False):
read_timeout_seconds: datetime.timedelta | None
sampling_callback: SamplingFnT | None
+ sampling_capabilities: mcp.types.SamplingCapability | None
list_roots_callback: ListRootsFnT | None
logging_callback: LoggingFnT | None
elicitation_callback: ElicitationFnT | None
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index 87a891888b..10744dd1e5 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -14,6 +14,7 @@
TextContent,
Tool,
ToolChoice,
+ ToolResultContent,
ToolUseContent,
)
@@ -23,8 +24,10 @@
ChatCompletion,
ChatCompletionAssistantMessageParam,
ChatCompletionMessageParam,
+ ChatCompletionMessageToolCallParam,
ChatCompletionSystemMessageParam,
ChatCompletionToolChoiceOptionParam,
+ ChatCompletionToolMessageParam,
ChatCompletionToolParam,
ChatCompletionUserMessageParam,
)
@@ -145,23 +148,126 @@ def _convert_to_openai_messages(
)
continue
- if not isinstance(message.content, TextContent):
- raise ValueError("Only text content is supported")
+ content = message.content
+
+ # Handle list content (from CreateMessageResultWithTools)
+ if isinstance(content, list):
+ # Collect tool calls and text from the list
+ tool_calls: list[ChatCompletionMessageToolCallParam] = []
+ text_parts: list[str] = []
+
+ for item in content:
+ if isinstance(item, ToolUseContent):
+ tool_calls.append(
+ ChatCompletionMessageToolCallParam(
+ id=item.id,
+ type="function",
+ function={
+ "name": item.name,
+ "arguments": json.dumps(item.input),
+ },
+ )
+ )
+ elif isinstance(item, TextContent):
+ text_parts.append(item.text)
+ elif isinstance(item, ToolResultContent):
+ # Tool results in a list - add as separate messages
+ result_text = ""
+ if item.content:
+ for sub_item in item.content:
+ if isinstance(sub_item, TextContent):
+ result_text += sub_item.text
+ openai_messages.append(
+ ChatCompletionToolMessageParam(
+ role="tool",
+ tool_call_id=item.toolUseId,
+ content=result_text,
+ )
+ )
+
+ # Add assistant message with tool calls if present
+ if tool_calls or text_parts:
+ msg_content = "\n".join(text_parts) if text_parts else None
+ if tool_calls:
+ openai_messages.append(
+ ChatCompletionAssistantMessageParam(
+ role="assistant",
+ content=msg_content,
+ tool_calls=tool_calls,
+ )
+ )
+ elif msg_content:
+ if message.role == "user":
+ openai_messages.append(
+ ChatCompletionUserMessageParam(
+ role="user",
+ content=msg_content,
+ )
+ )
+ else:
+ openai_messages.append(
+ ChatCompletionAssistantMessageParam(
+ role="assistant",
+ content=msg_content,
+ )
+ )
+ continue
- if message.role == "user":
+ # Handle ToolUseContent (assistant's tool calls)
+ if isinstance(content, ToolUseContent):
openai_messages.append(
- ChatCompletionUserMessageParam(
- role="user",
- content=message.content.text,
+ ChatCompletionAssistantMessageParam(
+ role="assistant",
+ tool_calls=[
+ ChatCompletionMessageToolCallParam(
+ id=content.id,
+ type="function",
+ function={
+ "name": content.name,
+ "arguments": json.dumps(content.input),
+ },
+ )
+ ],
)
)
- else:
+ continue
+
+ # Handle ToolResultContent (user's tool results)
+ if isinstance(content, ToolResultContent):
+ # Extract text from the content list
+ result_text = ""
+ if content.content:
+ for item in content.content:
+ if isinstance(item, TextContent):
+ result_text += item.text
openai_messages.append(
- ChatCompletionAssistantMessageParam(
- role="assistant",
- content=message.content.text,
+ ChatCompletionToolMessageParam(
+ role="tool",
+ tool_call_id=content.toolUseId,
+ content=result_text,
)
)
+ continue
+
+ # Handle TextContent
+ if isinstance(content, TextContent):
+ if message.role == "user":
+ openai_messages.append(
+ ChatCompletionUserMessageParam(
+ role="user",
+ content=content.text,
+ )
+ )
+ else:
+ openai_messages.append(
+ ChatCompletionAssistantMessageParam(
+ role="assistant",
+ content=content.text,
+ )
+ )
+ continue
+
+ raise ValueError(f"Unsupported content type: {type(content)}")
return openai_messages
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index a3739102ac..7650b00b63 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -70,6 +70,9 @@
T = TypeVar("T", default=Any)
ResultT = TypeVar("ResultT", default=str)
+# Simplified tool choice type - just the mode string instead of the full MCP object
+ToolChoiceOption = Literal["auto", "required", "none"]
+
_current_context: ContextVar[Context | None] = ContextVar("context", default=None) # type: ignore[assignment]
@@ -517,7 +520,7 @@ async def sample(
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | FastMCPTool] | None = None,
- tool_choice: ToolChoice | None = None,
+ tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: type[ResultT],
) -> SamplingResult[ResultT]:
@@ -534,7 +537,7 @@ async def sample(
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | FastMCPTool] | None = None,
- tool_choice: ToolChoice | None = None,
+ tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: None = None,
) -> SamplingResult[str]:
@@ -550,7 +553,7 @@ async def sample(
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | FastMCPTool] | None = None,
- tool_choice: ToolChoice | None = None,
+ tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: type[ResultT] | None = None,
) -> SamplingResult[ResultT] | SamplingResult[str]:
@@ -728,7 +731,7 @@ async def _sample_with_fallback_handler(
max_tokens: int,
model_preferences: ModelPreferences | str | list[str] | None,
sdk_tools: list[SDKTool] | None,
- tool_choice: ToolChoice | None,
+ tool_choice: ToolChoiceOption | None,
tool_map: dict[str, SamplingTool],
max_iterations: int,
result_type: type[ResultT] | None,
@@ -744,7 +747,9 @@ async def _sample_with_fallback_handler(
while True:
# On last iteration, force completion
- effective_tool_choice = tool_choice
+ effective_tool_choice: ToolChoice | None = (
+ ToolChoice(mode=tool_choice) if tool_choice else None
+ )
if iteration == max_iterations - 1:
if has_result_type and final_response_tool is not None:
# Force final_response tool on last iteration
@@ -857,7 +862,7 @@ async def _sample_with_client(
max_tokens: int,
model_preferences: ModelPreferences | str | list[str] | None,
sdk_tools: list[SDKTool] | None,
- tool_choice: ToolChoice | None,
+ tool_choice: ToolChoiceOption | None,
tool_map: dict[str, SamplingTool],
max_iterations: int,
result_type: type[ResultT] | None,
@@ -871,7 +876,9 @@ async def _sample_with_client(
while True:
# On last iteration, force completion
- effective_tool_choice = tool_choice
+ effective_tool_choice: ToolChoice | None = (
+ ToolChoice(mode=tool_choice) if tool_choice else None
+ )
if iteration == max_iterations - 1:
if has_result_type and final_response_tool is not None:
# Force final_response tool on last iteration
@@ -969,7 +976,9 @@ async def _execute_tool_calls(
SamplingMessage(role="assistant", content=result.content)
)
- # Execute each tool and collect results
+ # Execute each tool and collect results into a single list
+ tool_results: list[ToolResultContent] = []
+
for tool_use in tool_use_blocks:
# Check if this is the final_response tool
if (
@@ -993,53 +1002,65 @@ async def _execute_tool_calls(
)
except Exception as e:
# Validation failed - add error and continue the loop
- tool_result = ToolResultContent(
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[
+ TextContent(
+ type="text",
+ text=f"Validation error: {e}. Please try again.",
+ )
+ ],
+ isError=True,
+ )
+ )
+ continue
+
+ tool = tool_map.get(tool_use.name)
+ if tool is None:
+ # Tool not found - add error result
+ tool_results.append(
+ ToolResultContent(
type="tool_result",
toolUseId=tool_use.id,
content=[
TextContent(
type="text",
- text=f"Validation error: {e}. Please try again.",
+ text=f"Error: Unknown tool '{tool_use.name}'",
)
],
isError=True,
)
- current_messages.append(
- SamplingMessage(role="user", content=tool_result)
- )
- continue
-
- tool = tool_map.get(tool_use.name)
- if tool is None:
- # Tool not found - add error result
- tool_result = ToolResultContent(
- type="tool_result",
- toolUseId=tool_use.id,
- content=[
- TextContent(
- type="text", text=f"Error: Unknown tool '{tool_use.name}'"
- )
- ],
- isError=True,
)
else:
try:
result_value = await tool.run(tool_use.input)
- tool_result = ToolResultContent(
- type="tool_result",
- toolUseId=tool_use.id,
- content=[TextContent(type="text", text=str(result_value))],
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[TextContent(type="text", text=str(result_value))],
+ )
)
except Exception as e:
- tool_result = ToolResultContent(
- type="tool_result",
- toolUseId=tool_use.id,
- content=[TextContent(type="text", text=f"Error: {e}")],
- isError=True,
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[TextContent(type="text", text=f"Error: {e}")],
+ isError=True,
+ )
)
- # Add tool result to messages
- current_messages.append(SamplingMessage(role="user", content=tool_result))
+ # Add all tool results as a single user message with list content
+ if tool_results:
+ current_messages.append(
+ SamplingMessage(
+ role="user",
+ content=tool_results, # type: ignore[arg-type]
+ )
+ )
return current_messages, True
diff --git a/tests/client/test_sampling.py b/tests/client/test_sampling.py
index dc470e55ab..6c0bf43e61 100644
--- a/tests/client/test_sampling.py
+++ b/tests/client/test_sampling.py
@@ -3,7 +3,7 @@
from unittest.mock import AsyncMock
import pytest
-from mcp.types import TextContent, ToolChoice
+from mcp.types import TextContent
from pydantic_core import to_json
from fastmcp import Client, Context, FastMCP
@@ -171,16 +171,18 @@ class TestSamplingWithTools:
async def test_sampling_with_tools_requires_capability(self):
"""Test that sampling with tools raises error when client lacks capability."""
+ import mcp.types as mcp_types
+
from fastmcp.exceptions import ToolError
- mcp = FastMCP()
+ server = FastMCP()
@sampling_tool
def search(query: str) -> str:
"""Search the web."""
return f"Results for: {query}"
- @mcp.tool
+ @server.tool
async def sample_with_tool(context: Context) -> str:
# This should fail because the client doesn't advertise tools capability
result = await context.sample(
@@ -194,7 +196,12 @@ def sampling_handler(
) -> str:
return "Response"
- async with Client(mcp, sampling_handler=sampling_handler) as client:
+ # Explicitly disable tools capability by passing SamplingCapability without tools
+ async with Client(
+ server,
+ sampling_handler=sampling_handler,
+ sampling_capabilities=mcp_types.SamplingCapability(), # No tools
+ ) as client:
with pytest.raises(ToolError, match="sampling.tools capability"):
await client.call_tool("sample_with_tool", {})
@@ -279,23 +286,15 @@ def original_fn(x: int, y: int) -> int:
assert sampling.fn is mcp_tool.fn
def test_tool_choice_parameter(self):
- """Test that tool_choice parameter is accepted."""
- # This is a basic type check - full integration requires tools capability
-
- @sampling_tool
- def search(query: str) -> str:
- """Search."""
- return query
-
- # Just verify the ToolChoice type works
- choice = ToolChoice(mode="required")
- assert choice.mode == "required"
-
- choice_auto = ToolChoice(mode="auto")
- assert choice_auto.mode == "auto"
+ """Test that tool_choice parameter accepts string literals."""
+ from fastmcp.server.context import ToolChoiceOption
- choice_none = ToolChoice(mode="none")
- assert choice_none.mode == "none"
+ # Verify ToolChoiceOption type accepts the valid string values
+ choices: list[ToolChoiceOption] = ["auto", "required", "none"]
+ assert len(choices) == 3
+ assert "auto" in choices
+ assert "required" in choices
+ assert "none" in choices
class TestAutomaticToolLoop:
@@ -547,16 +546,22 @@ async def test_unknown(context: Context) -> str:
# Check that error was passed back in messages
assert len(messages_received) == 2
last_messages = messages_received[1]
- # Find the tool result message
- tool_result_msg = None
+ # Find the tool result in list content
+ tool_result = None
for msg in last_messages:
- if isinstance(msg.content, ToolResultContent):
- tool_result_msg = msg
+ # Tool results are now in a list
+ if isinstance(msg.content, list):
+ for item in msg.content:
+ if isinstance(item, ToolResultContent):
+ tool_result = item
+ break
+ elif isinstance(msg.content, ToolResultContent):
+ tool_result = msg.content
break
- assert tool_result_msg is not None
- assert tool_result_msg.content.isError is True # type: ignore[union-attr]
+ assert tool_result is not None
+ assert tool_result.isError is True
# Content is list of TextContent objects
- error_text = tool_result_msg.content.content[0].text # type: ignore[union-attr]
+ error_text = tool_result.content[0].text # type: ignore[union-attr]
assert "Unknown tool" in error_text
assert result.data == "Handled error"
@@ -615,15 +620,22 @@ async def test_exception(context: Context) -> str:
# Check that error was passed back
assert len(messages_received) == 2
last_messages = messages_received[1]
- tool_result_msg = None
+ # Find the tool result in list content
+ tool_result = None
for msg in last_messages:
- if isinstance(msg.content, ToolResultContent):
- tool_result_msg = msg
+ # Tool results are now in a list
+ if isinstance(msg.content, list):
+ for item in msg.content:
+ if isinstance(item, ToolResultContent):
+ tool_result = item
+ break
+ elif isinstance(msg.content, ToolResultContent):
+ tool_result = msg.content
break
- assert tool_result_msg is not None
- assert tool_result_msg.content.isError is True # type: ignore[union-attr]
+ assert tool_result is not None
+ assert tool_result.isError is True
# Content is list of TextContent objects
- error_text = tool_result_msg.content.content[0].text # type: ignore[union-attr]
+ error_text = tool_result.content[0].text # type: ignore[union-attr]
assert "Tool failed intentionally" in error_text
assert result.data == "Handled error"
@@ -885,14 +897,21 @@ async def validate_tool(context: Context) -> str:
# Check that error was passed back
last_messages = messages_received[1]
- tool_result_msg = None
+ # Find the tool result in list content
+ tool_result = None
for msg in last_messages:
- if isinstance(msg.content, ToolResultContent):
- tool_result_msg = msg
+ # Tool results are now in a list
+ if isinstance(msg.content, list):
+ for item in msg.content:
+ if isinstance(item, ToolResultContent):
+ tool_result = item
+ break
+ elif isinstance(msg.content, ToolResultContent):
+ tool_result = msg.content
break
- assert tool_result_msg is not None
- assert tool_result_msg.content.isError is True # type: ignore[union-attr]
- error_text = tool_result_msg.content.content[0].text # type: ignore[union-attr]
+ assert tool_result is not None
+ assert tool_result.isError is True
+ error_text = tool_result.content[0].text # type: ignore[union-attr]
assert "Validation error" in error_text
# Final result should be correct
From ead47304238daf762e40a312042c2ebefa4d71a5 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Thu, 4 Dec 2025 17:20:05 -0500
Subject: [PATCH 04/15] Fix tool result content handling in OpenAI handler
---
.../experimental/sampling/handlers/openai.py | 20 +++++++++++--------
1 file changed, 12 insertions(+), 8 deletions(-)
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index 10744dd1e5..26cc22e6aa 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -171,17 +171,19 @@ def _convert_to_openai_messages(
elif isinstance(item, TextContent):
text_parts.append(item.text)
elif isinstance(item, ToolResultContent):
- # Tool results in a list - add as separate messages
- result_text = ""
+ # Each tool result becomes a separate tool message
+ content_parts: list[dict[str, str]] = []
if item.content:
for sub_item in item.content:
if isinstance(sub_item, TextContent):
- result_text += sub_item.text
+ content_parts.append(
+ {"type": "text", "text": sub_item.text}
+ )
openai_messages.append(
ChatCompletionToolMessageParam(
role="tool",
tool_call_id=item.toolUseId,
- content=result_text,
+ content=content_parts if content_parts else "",
)
)
@@ -234,17 +236,19 @@ def _convert_to_openai_messages(
# Handle ToolResultContent (user's tool results)
if isinstance(content, ToolResultContent):
- # Extract text from the content list
- result_text = ""
+ # Extract text parts from the content list
+ result_parts: list[dict[str, str]] = []
if content.content:
for item in content.content:
if isinstance(item, TextContent):
- result_text += item.text
+ result_parts.append(
+ {"type": "text", "text": item.text}
+ )
openai_messages.append(
ChatCompletionToolMessageParam(
role="tool",
tool_call_id=content.toolUseId,
- content=result_text,
+ content=result_parts if result_parts else "",
)
)
continue
From 5c0f05511bf8dfa5539a5322fcc817a3704b11aa Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Thu, 4 Dec 2025 22:19:27 -0500
Subject: [PATCH 05/15] Remove @sampling_tool decorator - pass functions
directly to sample()
Functions passed to ctx.sample(tools=[...]) are now auto-converted
via SamplingTool.from_function(). Users can still use that method
directly for custom name/description overrides.
---
docs/servers/sampling.mdx | 29 +++++----
examples/advanced_sampling/tool_use.py | 5 +-
src/fastmcp/server/context.py | 16 +++--
src/fastmcp/server/sampling/__init__.py | 3 +-
src/fastmcp/server/sampling/sampling_tool.py | 68 ++++----------------
tests/client/test_sampling.py | 30 +++------
tests/server/sampling/test_sampling_tool.py | 54 ++++------------
7 files changed, 60 insertions(+), 145 deletions(-)
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index c52bf7ea5e..f4573f0ffe 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -248,24 +248,37 @@ Sampling with tools enables agentic workflows where the LLM can request tool cal
### Creating Sampling Tools
-Use the `@sampling_tool` decorator to create tools for use during sampling:
+Define regular Python functions and pass them directly to `ctx.sample()`:
```python
from fastmcp import FastMCP, Context
-from fastmcp.server.sampling import sampling_tool
-@sampling_tool
def search(query: str) -> str:
"""Search the web for information."""
return f"Search results for: {query}"
-@sampling_tool
def get_current_time() -> str:
"""Get the current time."""
from datetime import datetime
return datetime.now().strftime("%H:%M:%S")
```
+
+To create a tool with a custom name or description, use `SamplingTool.from_function()`:
+
+```python
+from fastmcp.server.sampling import SamplingTool
+
+tool = SamplingTool.from_function(
+ my_search_function,
+ name="web_search",
+ description="Search the web for information"
+)
+
+result = await ctx.sample(messages="...", tools=[tool])
+```
+
+
You can also pass existing FastMCP tools directly to `ctx.sample()`:
```python
@@ -292,14 +305,11 @@ Pass tools to `ctx.sample()` to enable agentic tool use. FastMCP automatically h
```python
from fastmcp import FastMCP, Context
-from fastmcp.server.sampling import sampling_tool
-@sampling_tool
def search(query: str) -> str:
"""Search the web."""
return f"Results for: {query}"
-@sampling_tool
def get_time() -> str:
"""Get the current time."""
from datetime import datetime
@@ -370,19 +380,16 @@ Combine `result_type` with tools for agentic workflows that return structured da
```python
from pydantic import BaseModel
from fastmcp import FastMCP, Context
-from fastmcp.server.sampling import sampling_tool
class ResearchResult(BaseModel):
summary: str
sources: list[str]
confidence: float
-@sampling_tool
def search(query: str) -> str:
"""Search for information."""
return f"Found information about: {query}"
-@sampling_tool
def fetch_url(url: str) -> str:
"""Fetch content from a URL."""
return f"Content from {url}"
@@ -538,12 +545,10 @@ The fallback handler fully supports sampling with tools:
```python
from fastmcp import FastMCP, Context
-from fastmcp.server.sampling import sampling_tool
from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
from openai import OpenAI
import os
-@sampling_tool
def search(query: str) -> str:
"""Search for information."""
return f"Results for: {query}"
diff --git a/examples/advanced_sampling/tool_use.py b/examples/advanced_sampling/tool_use.py
index 8629f0fb3d..a3302e47a3 100644
--- a/examples/advanced_sampling/tool_use.py
+++ b/examples/advanced_sampling/tool_use.py
@@ -19,11 +19,9 @@
from fastmcp import Client, Context, FastMCP
from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
-from fastmcp.server.sampling import sampling_tool
-# Define sampling tools (available to the LLM during sampling)
-@sampling_tool
+# Define tools (available to the LLM during sampling)
def search_web(query: str) -> str:
"""Search the web for information."""
# Simulated search results
@@ -38,7 +36,6 @@ def search_web(query: str) -> str:
return f"No results found for: {query}"
-@sampling_tool
def get_word_count(text: str) -> str:
"""Count words in text."""
return str(len(text.split()))
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index 7650b00b63..2c8de0e224 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -5,7 +5,7 @@
import json
import logging
import weakref
-from collections.abc import Generator, Mapping, Sequence
+from collections.abc import Callable, Generator, Mapping, Sequence
from contextlib import contextmanager
from contextvars import ContextVar, Token
from dataclasses import dataclass
@@ -519,7 +519,7 @@ async def sample(
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
- tools: Sequence[SamplingTool | FastMCPTool] | None = None,
+ tools: Sequence[SamplingTool | FastMCPTool | Callable[..., Any]] | None = None,
tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: type[ResultT],
@@ -536,7 +536,7 @@ async def sample(
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
- tools: Sequence[SamplingTool | FastMCPTool] | None = None,
+ tools: Sequence[SamplingTool | FastMCPTool | Callable[..., Any]] | None = None,
tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: None = None,
@@ -552,7 +552,7 @@ async def sample(
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
- tools: Sequence[SamplingTool | FastMCPTool] | None = None,
+ tools: Sequence[SamplingTool | FastMCPTool | Callable[..., Any]] | None = None,
tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: type[ResultT] | None = None,
@@ -581,8 +581,8 @@ async def sample(
temperature: Optional sampling temperature.
max_tokens: Maximum tokens to generate. Defaults to 512.
model_preferences: Optional model preferences.
- tools: Optional list of tools the LLM can use. Accepts both
- SamplingTools and FastMCP Tools (which are auto-converted).
+ tools: Optional list of tools the LLM can use. Accepts plain
+ functions, SamplingTools, or FastMCP Tools (all are auto-converted).
When provided, the method automatically handles tool execution
and returns the final response after all tool calls complete.
tool_choice: Optional control over tool usage behavior. Only valid
@@ -626,9 +626,11 @@ async def sample(
sampling_tools.append(t)
elif isinstance(t, FastMCPTool):
sampling_tools.append(SamplingTool.from_mcp_tool(t))
+ elif callable(t):
+ sampling_tools.append(SamplingTool.from_function(t))
else:
raise TypeError(
- f"Expected SamplingTool or FastMCP Tool, got {type(t)}"
+ f"Expected SamplingTool, FastMCP Tool, or callable, got {type(t)}"
)
# Create synthetic final_response tool for structured output
diff --git a/src/fastmcp/server/sampling/__init__.py b/src/fastmcp/server/sampling/__init__.py
index f5238d2306..6485e658ec 100644
--- a/src/fastmcp/server/sampling/__init__.py
+++ b/src/fastmcp/server/sampling/__init__.py
@@ -1,10 +1,9 @@
"""Sampling module for FastMCP servers."""
from fastmcp.server.sampling.handler import ServerSamplingHandler
-from fastmcp.server.sampling.sampling_tool import SamplingTool, sampling_tool
+from fastmcp.server.sampling.sampling_tool import SamplingTool
__all__ = [
"SamplingTool",
"ServerSamplingHandler",
- "sampling_tool",
]
diff --git a/src/fastmcp/server/sampling/sampling_tool.py b/src/fastmcp/server/sampling/sampling_tool.py
index 85ce624270..fad0b706b2 100644
--- a/src/fastmcp/server/sampling/sampling_tool.py
+++ b/src/fastmcp/server/sampling/sampling_tool.py
@@ -4,7 +4,7 @@
import inspect
from collections.abc import Callable
-from typing import TYPE_CHECKING, Any, overload
+from typing import TYPE_CHECKING, Any
from mcp.types import Tool as SDKTool
from pydantic import BaseModel, ConfigDict
@@ -22,21 +22,24 @@ class SamplingTool(BaseModel):
an executor function, enabling servers to execute agentic workflows where
the LLM can request tool calls during sampling.
- Create a SamplingTool using the @sampling_tool decorator or class methods:
+ In most cases, pass functions directly to ctx.sample():
- @sampling_tool
def search(query: str) -> str:
'''Search the web.'''
return web_search(query)
- # Or from an existing FastMCP Tool
- sampling_tool = SamplingTool.from_mcp_tool(existing_tool)
-
- # Then use in sampling
result = await context.sample(
messages="Find info about Python",
- tools=[search],
+ tools=[search], # Plain functions work directly
)
+
+ Create a SamplingTool explicitly when you need custom name/description:
+
+ tool = SamplingTool.from_function(search, name="web_search")
+
+ Or from an existing FastMCP Tool:
+
+ sampling_tool = SamplingTool.from_mcp_tool(existing_tool)
"""
name: str
@@ -136,52 +139,3 @@ def from_function(
parameters=parsed.input_schema,
fn=parsed.fn,
)
-
-
-@overload
-def sampling_tool(fn: Callable[..., Any]) -> SamplingTool: ...
-
-
-@overload
-def sampling_tool(
- *,
- name: str | None = None,
- description: str | None = None,
-) -> Callable[[Callable[..., Any]], SamplingTool]: ...
-
-
-def sampling_tool(
- fn: Callable[..., Any] | None = None,
- *,
- name: str | None = None,
- description: str | None = None,
-) -> SamplingTool | Callable[[Callable[..., Any]], SamplingTool]:
- """Decorator to create a SamplingTool from a function.
-
- Can be used with or without arguments:
-
- @sampling_tool
- def search(query: str) -> str:
- '''Search the web.'''
- return web_search(query)
-
- @sampling_tool(name="web_search", description="Search the internet")
- def search(query: str) -> str:
- return web_search(query)
-
- Args:
- fn: The function to wrap (when used without parentheses).
- name: Optional name override for the tool.
- description: Optional description override for the tool.
-
- Returns:
- A SamplingTool if called directly on a function, or a decorator
- if called with arguments.
- """
- if fn is not None:
- return SamplingTool.from_function(fn)
-
- def decorator(fn: Callable[..., Any]) -> SamplingTool:
- return SamplingTool.from_function(fn, name=name, description=description)
-
- return decorator
diff --git a/tests/client/test_sampling.py b/tests/client/test_sampling.py
index 6c0bf43e61..915c1ce90e 100644
--- a/tests/client/test_sampling.py
+++ b/tests/client/test_sampling.py
@@ -8,7 +8,7 @@
from fastmcp import Client, Context, FastMCP
from fastmcp.client.sampling import RequestContext, SamplingMessage, SamplingParams
-from fastmcp.server.sampling import SamplingTool, sampling_tool
+from fastmcp.server.sampling import SamplingTool
from fastmcp.utilities.types import Image
@@ -177,7 +177,6 @@ async def test_sampling_with_tools_requires_capability(self):
server = FastMCP()
- @sampling_tool
def search(query: str) -> str:
"""Search the web."""
return f"Results for: {query}"
@@ -214,7 +213,6 @@ async def test_sampling_with_tools_fallback_handler_must_return_correct_type(sel
mcp = FastMCP(sampling_handler=invalid_handler)
- @sampling_tool
def search(query: str) -> str:
"""Search the web."""
return f"Results for: {query}"
@@ -238,36 +236,36 @@ async def sample_with_tool(context: Context) -> str:
def test_sampling_tool_schema(self):
"""Test that SamplingTool generates correct schema."""
- @sampling_tool
def search(query: str, limit: int = 10) -> str:
"""Search the web for results."""
return f"Results for: {query}"
- assert search.name == "search"
- assert search.description == "Search the web for results."
- assert "query" in search.parameters.get("properties", {})
- assert "limit" in search.parameters.get("properties", {})
+ tool = SamplingTool.from_function(search)
+ assert tool.name == "search"
+ assert tool.description == "Search the web for results."
+ assert "query" in tool.parameters.get("properties", {})
+ assert "limit" in tool.parameters.get("properties", {})
async def test_sampling_tool_run(self):
"""Test that SamplingTool.run() executes correctly."""
- @sampling_tool
def add(a: int, b: int) -> int:
"""Add two numbers."""
return a + b
- result = await add.run({"a": 5, "b": 3})
+ tool = SamplingTool.from_function(add)
+ result = await tool.run({"a": 5, "b": 3})
assert result == 8
async def test_sampling_tool_run_async(self):
"""Test that SamplingTool.run() works with async functions."""
- @sampling_tool
async def async_multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
return a * b
- result = await async_multiply.run({"a": 4, "b": 7})
+ tool = SamplingTool.from_function(async_multiply)
+ result = await tool.run({"a": 4, "b": 7})
assert result == 28
def test_sampling_tool_from_mcp_tool(self):
@@ -307,7 +305,6 @@ async def test_automatic_tool_loop_executes_tools(self):
call_count = 0
tool_was_called = False
- @sampling_tool
def get_weather(city: str) -> str:
"""Get weather for a city."""
nonlocal tool_was_called
@@ -370,13 +367,11 @@ async def test_automatic_tool_loop_multiple_tools(self):
executed_tools: list[str] = []
- @sampling_tool
def tool_a(x: int) -> int:
"""Tool A."""
executed_tools.append(f"tool_a({x})")
return x * 2
- @sampling_tool
def tool_b(y: int) -> int:
"""Tool B."""
executed_tools.append(f"tool_b({y})")
@@ -436,7 +431,6 @@ async def test_automatic_tool_loop_max_iterations(self):
call_count = 0
received_tool_choices: list = []
- @sampling_tool
def looping_tool() -> str:
"""A tool that always gets called again."""
return "keep going"
@@ -498,7 +492,6 @@ async def test_automatic_tool_loop_handles_unknown_tool(self):
ToolUseContent,
)
- @sampling_tool
def known_tool() -> str:
"""A known tool."""
return "known result"
@@ -573,7 +566,6 @@ async def test_automatic_tool_loop_handles_tool_exception(self):
ToolUseContent,
)
- @sampling_tool
def failing_tool() -> str:
"""A tool that raises an exception."""
raise ValueError("Tool failed intentionally")
@@ -649,7 +641,6 @@ async def test_max_iterations_one_for_manual_loop(self):
received_tool_choices: list = []
- @sampling_tool
def my_tool() -> str:
"""A tool."""
return "result"
@@ -763,7 +754,6 @@ class SearchResult(BaseModel):
summary: str
sources: list[str]
- @sampling_tool
def search(query: str) -> str:
"""Search for information."""
return f"Found info about: {query}"
diff --git a/tests/server/sampling/test_sampling_tool.py b/tests/server/sampling/test_sampling_tool.py
index 19808ecd26..1025084a65 100644
--- a/tests/server/sampling/test_sampling_tool.py
+++ b/tests/server/sampling/test_sampling_tool.py
@@ -2,7 +2,7 @@
import pytest
-from fastmcp.server.sampling import SamplingTool, sampling_tool
+from fastmcp.server.sampling import SamplingTool
from fastmcp.tools.tool import Tool
@@ -94,75 +94,43 @@ def test_from_tool_without_fn_raises(self):
SamplingTool.from_mcp_tool(tool)
-class TestSamplingToolDecorator:
- """Tests for the @sampling_tool decorator."""
-
- def test_decorator_without_args(self):
- @sampling_tool
- def search(query: str) -> str:
- """Search the web."""
- return f"Results for: {query}"
-
- assert isinstance(search, SamplingTool)
- assert search.name == "search"
- assert search.description == "Search the web."
-
- def test_decorator_with_args(self):
- @sampling_tool(name="web_search", description="Custom description")
- def search(query: str) -> str:
- return f"Results for: {query}"
-
- assert isinstance(search, SamplingTool)
- assert search.name == "web_search"
- assert search.description == "Custom description"
-
- def test_decorator_with_partial_args(self):
- @sampling_tool(name="custom_name")
- def search(query: str) -> str:
- """Original docstring."""
- return f"Results for: {query}"
-
- assert search.name == "custom_name"
- assert search.description == "Original docstring."
-
-
class TestSamplingToolRun:
"""Tests for SamplingTool.run()."""
async def test_run_sync_function(self):
- @sampling_tool
def add(a: int, b: int) -> int:
"""Add two numbers."""
return a + b
- result = await add.run({"a": 2, "b": 3})
+ tool = SamplingTool.from_function(add)
+ result = await tool.run({"a": 2, "b": 3})
assert result == 5
async def test_run_async_function(self):
- @sampling_tool
async def async_add(a: int, b: int) -> int:
"""Add two numbers asynchronously."""
return a + b
- result = await async_add.run({"a": 2, "b": 3})
+ tool = SamplingTool.from_function(async_add)
+ result = await tool.run({"a": 2, "b": 3})
assert result == 5
async def test_run_with_no_arguments(self):
- @sampling_tool
def get_value() -> str:
"""Return a fixed value."""
return "hello"
- result = await get_value.run()
+ tool = SamplingTool.from_function(get_value)
+ result = await tool.run()
assert result == "hello"
async def test_run_with_none_arguments(self):
- @sampling_tool
def get_value() -> str:
"""Return a fixed value."""
return "hello"
- result = await get_value.run(None)
+ tool = SamplingTool.from_function(get_value)
+ result = await tool.run(None)
assert result == "hello"
@@ -170,12 +138,12 @@ class TestSamplingToolSDKConversion:
"""Tests for SamplingTool._to_sdk_tool() internal method."""
def test_to_sdk_tool(self):
- @sampling_tool
def search(query: str) -> str:
"""Search the web."""
return f"Results for: {query}"
- sdk_tool = search._to_sdk_tool()
+ tool = SamplingTool.from_function(search)
+ sdk_tool = tool._to_sdk_tool()
assert sdk_tool.name == "search"
assert sdk_tool.description == "Search the web."
From 4b5bfe613063591325e909694b11edf0f37ad897 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Fri, 12 Dec 2025 07:26:47 -0500
Subject: [PATCH 06/15] Remove auto-conversion of MCP tools to sampling tools
Users want MCP tools passed to ctx.sample() to go through the full MCP
machinery (middleware, native responses) rather than being auto-converted
to direct function calls. Now only SamplingTool and plain callables are
accepted - passing a FastMCP Tool raises a clear TypeError.
Also bumps mcp dependency to >=1.24.0 for required sampling features.
---
docs/servers/sampling.mdx | 22 +-----------
pyproject.toml | 2 +-
src/fastmcp/server/context.py | 22 +++++-------
src/fastmcp/server/sampling/sampling_tool.py | 35 +-------------------
tests/client/test_sampling.py | 15 ---------
tests/server/sampling/test_sampling_tool.py | 29 ----------------
6 files changed, 12 insertions(+), 113 deletions(-)
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index f4573f0ffe..81cb535c79 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -82,7 +82,7 @@ async def analyze_sentiment(text: str, ctx: Context) -> dict:
Optional model selection preferences (e.g., model hint string, list of hints, or ModelPreferences object)
-
+
Optional list of tools the LLM can use during sampling. See [Sampling with Tools](#sampling-with-tools).
@@ -279,26 +279,6 @@ result = await ctx.sample(messages="...", tools=[tool])
```
-You can also pass existing FastMCP tools directly to `ctx.sample()`:
-
-```python
-mcp = FastMCP()
-
-@mcp.tool
-def get_weather(city: str) -> str:
- """Get the current weather for a city."""
- return f"Weather in {city}: 72°F, sunny"
-
-@mcp.tool
-async def weather_agent(question: str, ctx: Context) -> str:
- """Answer weather questions using the weather tool."""
- result = await ctx.sample(
- messages=question,
- tools=[get_weather], # FastMCP tools work directly
- )
- return result.text
-```
-
### Using Tools in Sampling
Pass tools to `ctx.sample()` to enable agentic tool use. FastMCP automatically handles the tool execution loop:
diff --git a/pyproject.toml b/pyproject.toml
index eb1a1f42b6..ab7cc886f5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ dependencies = [
"python-dotenv>=1.1.0",
"exceptiongroup>=1.2.2",
"httpx>=0.28.1",
- "mcp>=1.23.3",
+ "mcp>=1.24.0",
"openapi-pydantic>=0.5.1",
"platformdirs>=4.0.0",
"pydocket>=0.15.2",
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index 2a2908fcd1..ed2a9f2a71 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -10,7 +10,7 @@
from contextvars import ContextVar, Token
from dataclasses import dataclass
from logging import Logger
-from typing import Any, Generic, Literal, cast, get_origin, overload
+from typing import Any, Generic, Literal, cast, overload
import anyio
from mcp import LoggingLevel, ServerSession
@@ -52,9 +52,9 @@
)
from fastmcp.server.sampling import SamplingTool
from fastmcp.server.server import FastMCP
-from fastmcp.tools.tool import Tool as FastMCPTool
from fastmcp.utilities.json_schema import compress_schema
from fastmcp.utilities.logging import _clamp_logger, get_logger
+from fastmcp.utilities.types import get_cached_typeadapter
logger: Logger = get_logger(name=__name__)
to_client_logger: Logger = logger.getChild(suffix="to_client")
@@ -571,7 +571,7 @@ async def sample(
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
- tools: Sequence[SamplingTool | FastMCPTool | Callable[..., Any]] | None = None,
+ tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: type[ResultT],
@@ -588,7 +588,7 @@ async def sample(
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
- tools: Sequence[SamplingTool | FastMCPTool | Callable[..., Any]] | None = None,
+ tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: None = None,
@@ -604,7 +604,7 @@ async def sample(
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
- tools: Sequence[SamplingTool | FastMCPTool | Callable[..., Any]] | None = None,
+ tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
tool_choice: ToolChoiceOption | None = None,
max_iterations: int = 10,
result_type: type[ResultT] | None = None,
@@ -634,9 +634,9 @@ async def sample(
max_tokens: Maximum tokens to generate. Defaults to 512.
model_preferences: Optional model preferences.
tools: Optional list of tools the LLM can use. Accepts plain
- functions, SamplingTools, or FastMCP Tools (all are auto-converted).
- When provided, the method automatically handles tool execution
- and returns the final response after all tool calls complete.
+ functions or SamplingTools. When provided, the method automatically
+ handles tool execution and returns the final response after all
+ tool calls complete.
tool_choice: Optional control over tool usage behavior. Only valid
when tools are provided.
max_iterations: Maximum number of LLM calls before returning.
@@ -676,14 +676,10 @@ async def sample(
for t in tools:
if isinstance(t, SamplingTool):
sampling_tools.append(t)
- elif isinstance(t, FastMCPTool):
- sampling_tools.append(SamplingTool.from_mcp_tool(t))
elif callable(t):
sampling_tools.append(SamplingTool.from_function(t))
else:
- raise TypeError(
- f"Expected SamplingTool, FastMCP Tool, or callable, got {type(t)}"
- )
+ raise TypeError(f"Expected SamplingTool or callable, got {type(t)}")
# Create synthetic final_response tool for structured output
final_response_tool: SamplingTool | None = None
diff --git a/src/fastmcp/server/sampling/sampling_tool.py b/src/fastmcp/server/sampling/sampling_tool.py
index fad0b706b2..8bab07389f 100644
--- a/src/fastmcp/server/sampling/sampling_tool.py
+++ b/src/fastmcp/server/sampling/sampling_tool.py
@@ -4,16 +4,13 @@
import inspect
from collections.abc import Callable
-from typing import TYPE_CHECKING, Any
+from typing import Any
from mcp.types import Tool as SDKTool
from pydantic import BaseModel, ConfigDict
from fastmcp.tools.tool import ParsedFunction
-if TYPE_CHECKING:
- from fastmcp.tools.tool import Tool
-
class SamplingTool(BaseModel):
"""A tool that can be used during LLM sampling.
@@ -36,10 +33,6 @@ def search(query: str) -> str:
Create a SamplingTool explicitly when you need custom name/description:
tool = SamplingTool.from_function(search, name="web_search")
-
- Or from an existing FastMCP Tool:
-
- sampling_tool = SamplingTool.from_mcp_tool(existing_tool)
"""
name: str
@@ -78,32 +71,6 @@ def _to_sdk_tool(self) -> SDKTool:
inputSchema=self.parameters,
)
- @classmethod
- def from_mcp_tool(cls, tool: Tool) -> SamplingTool:
- """Create a SamplingTool from a FastMCP Tool.
-
- Args:
- tool: A FastMCP Tool instance (must have an fn attribute).
-
- Returns:
- A SamplingTool with the same schema and executor.
-
- Raises:
- ValueError: If the tool doesn't have an fn attribute.
- """
- if not hasattr(tool, "fn"):
- raise ValueError(
- f"Tool {tool.name!r} does not have an fn attribute. "
- "Only FunctionTools can be converted to SamplingTools."
- )
-
- return cls(
- name=tool.name,
- description=tool.description,
- parameters=tool.parameters,
- fn=tool.fn,
- )
-
@classmethod
def from_function(
cls,
diff --git a/tests/client/test_sampling.py b/tests/client/test_sampling.py
index 915c1ce90e..65e2b834f4 100644
--- a/tests/client/test_sampling.py
+++ b/tests/client/test_sampling.py
@@ -268,21 +268,6 @@ async def async_multiply(a: int, b: int) -> int:
result = await tool.run({"a": 4, "b": 7})
assert result == 28
- def test_sampling_tool_from_mcp_tool(self):
- """Test creating SamplingTool from FastMCP Tool."""
- from fastmcp.tools.tool import Tool
-
- def original_fn(x: int, y: int) -> int:
- """Add x and y."""
- return x + y
-
- mcp_tool = Tool.from_function(original_fn)
- sampling = SamplingTool.from_mcp_tool(mcp_tool)
-
- assert sampling.name == "original_fn"
- assert sampling.description == "Add x and y."
- assert sampling.fn is mcp_tool.fn
-
def test_tool_choice_parameter(self):
"""Test that tool_choice parameter accepts string literals."""
from fastmcp.server.context import ToolChoiceOption
diff --git a/tests/server/sampling/test_sampling_tool.py b/tests/server/sampling/test_sampling_tool.py
index 1025084a65..8c2dba7a8e 100644
--- a/tests/server/sampling/test_sampling_tool.py
+++ b/tests/server/sampling/test_sampling_tool.py
@@ -3,7 +3,6 @@
import pytest
from fastmcp.server.sampling import SamplingTool
-from fastmcp.tools.tool import Tool
class TestSamplingToolFromFunction:
@@ -66,34 +65,6 @@ def search(query: str, limit: int = 10, include_images: bool = False) -> str:
assert "include_images" in props
-class TestSamplingToolFromMCPTool:
- """Tests for SamplingTool.from_mcp_tool()."""
-
- def test_from_function_tool(self):
- def original(x: int) -> int:
- """Double a number."""
- return x * 2
-
- mcp_tool = Tool.from_function(original)
- sampling = SamplingTool.from_mcp_tool(mcp_tool)
-
- assert sampling.name == "original"
- assert sampling.description == "Double a number."
- assert sampling.fn is mcp_tool.fn
-
- def test_from_tool_without_fn_raises(self):
- # Create a base Tool without fn (not a FunctionTool)
- tool = Tool(
- name="test",
- description="Test tool",
- parameters={"type": "object"},
- tags=set(),
- )
-
- with pytest.raises(ValueError, match="does not have an fn attribute"):
- SamplingTool.from_mcp_tool(tool)
-
-
class TestSamplingToolRun:
"""Tests for SamplingTool.run()."""
From 2cdc9b6b28e89e057346f1c1ae8524e5de38818f Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sat, 13 Dec 2025 22:19:37 -0500
Subject: [PATCH 07/15] Refactor sampling API: replace sample_iter() with
sample_step()
Replace the mutable SampleRun/sample_iter() pattern with a simpler stateless
sample_step() function. sample_step() makes a single LLM call and returns a
SampleStep with the response and history. sample() now loops sample_step()
internally.
Key changes:
- Add sample_step() for fine-grained control over the sampling loop
- Remove SampleRun class and sample_iter() method
- Structured output uses tool description only (no prompt modification)
- execute_tools parameter controls automatic vs manual tool execution
---
docs/servers/sampling.mdx | 598 ++++++--------
examples/advanced_sampling/tool_use.py | 1 -
.../experimental/sampling/handlers/openai.py | 28 +-
src/fastmcp/server/context.py | 775 +++++++-----------
src/fastmcp/server/sampling/__init__.py | 14 +
src/fastmcp/server/sampling/run.py | 290 +++++++
tests/client/test_sampling.py | 332 +++++---
tests/server/proxy/test_proxy_client.py | 4 +-
uv.lock | 8 +-
9 files changed, 1034 insertions(+), 1016 deletions(-)
create mode 100644 src/fastmcp/server/sampling/run.py
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index 81cb535c79..5bee7ee341 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -6,205 +6,177 @@ icon: robot
tag: NEW
---
-import { VersionBadge } from '/snippets/version-badge.mdx'
+import { VersionBadge } from "/snippets/version-badge.mdx";
-LLM sampling allows MCP tools to request LLM completions based on provided messages. By default, sampling requests are sent to the client's LLM, but you can also configure a fallback handler or always use a specific LLM provider.
+LLM sampling allows your MCP tools to request text generation from an LLM during execution. This enables tools to leverage AI capabilities for analysis, generation, reasoning, and more—without the client needing to orchestrate multiple calls.
-## Why Use LLM Sampling?
+By default, sampling requests are routed to the client's LLM. You can also configure a fallback handler to use a specific provider (like OpenAI) when the client doesn't support sampling, or to always use your own LLM regardless of client capabilities.
-LLM sampling enables tools to:
+## Method Reference
-- **Leverage AI capabilities**: Use the client's LLM for text generation and analysis
-- **Offload complex reasoning**: Let the LLM handle tasks requiring natural language understanding
-- **Execute agentic workflows**: Let the LLM call tools during sampling to accomplish complex tasks
-- **Get structured output**: Request validated, typed responses using `result_type`
-- **Generate dynamic content**: Create responses, summaries, or transformations based on data
-
-## Basic Usage
-
-Use `ctx.sample()` to request text generation from the client's LLM. The method returns a `SamplingResult` with `.text`, `.result`, and `.history` attributes:
-
-```python
-from fastmcp import FastMCP, Context
-
-mcp = FastMCP("SamplingDemo")
-
-@mcp.tool
-async def analyze_sentiment(text: str, ctx: Context) -> dict:
- """Analyze the sentiment of text using the client's LLM."""
- prompt = f"""Analyze the sentiment of the following text as positive, negative, or neutral.
- Just output a single word - 'positive', 'negative', or 'neutral'.
-
- Text to analyze: {text}"""
-
- # Request LLM analysis - returns SamplingResult
- response = await ctx.sample(prompt)
-
- # Access the text via .text or .result (same for text responses)
- sentiment = response.text.strip().lower()
-
- if "positive" in sentiment:
- sentiment = "positive"
- elif "negative" in sentiment:
- sentiment = "negative"
- else:
- sentiment = "neutral"
-
- return {"text": text, "sentiment": sentiment}
-```
-
-## Method Signature
-
-
+
- Request text generation from the client's LLM
+ Request text generation from the LLM, running to completion automatically.
- A string or list of strings/message objects to send to the LLM
+ The prompt to send. Can be a simple string or a list of messages for multi-turn conversations.
- Optional system prompt to guide the LLM's behavior
+ Instructions that establish the LLM's role and behavior.
- Optional sampling temperature (controls randomness, typically 0.0-1.0)
+ Controls randomness (0.0 = deterministic, 1.0 = creative).
- Optional maximum number of tokens to generate
+ Maximum tokens to generate.
-
- Optional model selection preferences (e.g., model hint string, list of hints, or ModelPreferences object)
+
+ Hints for which model the client should use.
-
- Optional list of tools the LLM can use during sampling. See [Sampling with Tools](#sampling-with-tools).
+
+ Functions the LLM can call during sampling.
-
- Optional control over tool usage behavior: `"auto"`, `"required"`, or `"none"`
+
+ A Pydantic model for validated structured output.
+
+
+
+
+
+
+ - `.text`: The raw text response (or JSON for structured output)
+ - `.result`: The typed result—same as `.text` for plain text, or a validated Pydantic object for structured output
+ - `.history`: All messages exchanged during sampling
+
+
+
+
+
+
+ Make a single LLM sampling call. Use this for fine-grained control over the sampling loop.
-
- Maximum number of LLM calls before returning. When tools are provided, FastMCP automatically executes tool calls and continues the conversation. On the last iteration, forces a text response (or `final_response` if using `result_type`).
+
+ Same as `sample()`, plus:
+
+
+ Controls tool usage: `"auto"`, `"required"`, or `"none"`.
-
- Optional type for structured output. When specified, creates a synthetic `final_response` tool and validates the LLM's response against this type. See [Structured Output](#structured-output).
+
+ If True, execute tool calls and append results to history. If False, return immediately with tool calls available for manual execution.
+
-
- Always returns a `SamplingResult` with:
- - `.text`: The text representation (raw text, or JSON for structured output). `None` if the LLM requested a tool on the last iteration.
- - `.result`: The typed result. For text responses, equals `.text`. For structured output, the validated object.
- - `.history`: List of all messages exchanged during sampling, useful for continuing conversations.
+
+ - `.response`: The raw LLM response
+ - `.history`: Messages including input, assistant response, and tool results
+ - `.is_tool_use` / `.is_text`: Check what kind of response
+ - `.tool_calls`: List of tool calls (if any)
+ - `.text`: The text content (if any)
-## Simple Text Generation
-
-### Basic Prompting
+## Basic Sampling
-Generate text with simple string prompts:
+The simplest use of sampling is passing a prompt string to `ctx.sample()`. The method sends the prompt to the LLM, waits for the complete response, and returns a `SamplingResult`. You can access the generated text through the `.text` attribute.
```python
+from fastmcp import FastMCP, Context
+
+mcp = FastMCP()
+
@mcp.tool
-async def generate_summary(content: str, ctx: Context) -> str:
+async def summarize(content: str, ctx: Context) -> str:
"""Generate a summary of the provided content."""
- prompt = f"Please provide a concise summary of the following content:\n\n{content}"
-
- response = await ctx.sample(prompt)
- return response.text
+ result = await ctx.sample(f"Please summarize this:\n\n{content}")
+ return result.text
```
-### System Prompt
+The `SamplingResult` also provides `.result` (identical to `.text` for plain text responses) and `.history` containing the full message exchange—useful if you need to continue the conversation or debug the interaction.
-Use system prompts to guide the LLM's behavior:
+### System Prompts
-```python
+System prompts let you establish the LLM's role and behavioral guidelines before it processes your request. This is useful for controlling tone, enforcing constraints, or providing context that shouldn't clutter the user-facing prompt.
+
+````python
@mcp.tool
-async def generate_code_example(concept: str, ctx: Context) -> str:
- """Generate a Python code example for a given concept."""
- response = await ctx.sample(
- messages=f"Write a simple Python code example demonstrating '{concept}'.",
- system_prompt="You are an expert Python programmer. Provide concise, working code examples without explanations.",
+async def generate_code(concept: str, ctx: Context) -> str:
+ """Generate a Python code example for a concept."""
+ result = await ctx.sample(
+ messages=f"Write a Python example demonstrating '{concept}'.",
+ system_prompt=(
+ "You are an expert Python programmer. "
+ "Provide concise, working code without explanations."
+ ),
temperature=0.7,
max_tokens=300
)
+ return f"```python\n{result.text}\n```"
+````
- return f"```python\n{response.text}\n```"
-```
-
+The `temperature` parameter controls randomness—higher values (up to 1.0) produce more varied outputs, while lower values make responses more deterministic. The `max_tokens` parameter limits response length.
### Model Preferences
-Specify model preferences for different use cases:
+Model preferences let you hint at which LLM the client should use for a request. You can pass a single model name or a list of preferences in priority order. These are hints rather than requirements—the actual model used depends on what the client has available.
```python
-@mcp.tool
-async def creative_writing(topic: str, ctx: Context) -> str:
- """Generate creative content using a specific model."""
- response = await ctx.sample(
- messages=f"Write a creative short story about {topic}",
- model_preferences="claude-haiku-4-5", # Prefer a specific model
- temperature=0.9, # High creativity
- max_tokens=1000
- )
-
- return response.text
-
@mcp.tool
async def technical_analysis(data: str, ctx: Context) -> str:
- """Perform technical analysis with a reasoning-focused model."""
- response = await ctx.sample(
- messages=f"Analyze this technical data and provide insights: {data}",
- model_preferences=["claude-3-opus", "gpt-4"], # Prefer reasoning models
- temperature=0.2, # Low randomness for consistency
- max_tokens=800
+ """Analyze data using a reasoning-focused model."""
+ result = await ctx.sample(
+ messages=f"Analyze this data:\n\n{data}",
+ model_preferences=["claude-opus-4-5", "gpt-5-2"],
+ temperature=0.2,
)
-
- return response.text
+ return result.text
```
-### Complex Message Structures
+Use model preferences when different tasks benefit from different model characteristics. Creative writing might prefer faster models with higher temperature, while complex analysis might benefit from larger reasoning-focused models.
+
+### Multi-Turn Conversations
-Use structured messages for more complex interactions:
+For requests that need conversational context, construct a list of `SamplingMessage` objects representing the conversation history. Each message has a `role` ("user" or "assistant") and `content`.
```python
from fastmcp.client.sampling import SamplingMessage
@mcp.tool
-async def multi_turn_analysis(user_query: str, context_data: str, ctx: Context) -> str:
- """Perform analysis using multi-turn conversation structure."""
+async def contextual_analysis(query: str, data: str, ctx: Context) -> str:
+ """Analyze data with conversational context."""
messages = [
- SamplingMessage(role="user", content=f"I have this data: {context_data}"),
- SamplingMessage(role="assistant", content="I can see your data. What would you like me to analyze?"),
- SamplingMessage(role="user", content=user_query)
+ SamplingMessage(role="user", content=f"Here's my data: {data}"),
+ SamplingMessage(
+ role="assistant",
+ content="I see the data. What would you like to know?"
+ ),
+ SamplingMessage(role="user", content=query)
]
-
- response = await ctx.sample(
- messages=messages,
- system_prompt="You are a data analyst. Provide detailed insights based on the conversation context.",
- temperature=0.3
- )
-
- return response.text
+ result = await ctx.sample(messages=messages)
+ return result.text
```
+The LLM receives the full conversation thread and responds with awareness of the preceding context.
+
## Structured Output
-
+
-Use `result_type` to get validated, typed responses from the LLM. When you specify a `result_type`, FastMCP creates a synthetic `final_response` tool that the LLM calls to provide its structured response.
+When you need validated, typed data instead of free-form text, use the `result_type` parameter with a Pydantic model. FastMCP ensures the LLM returns data matching your schema, handling validation and retries automatically.
```python
from pydantic import BaseModel
@@ -219,75 +191,41 @@ class SentimentResult(BaseModel):
@mcp.tool
async def analyze_sentiment(text: str, ctx: Context) -> SentimentResult:
- """Analyze sentiment with structured output."""
+ """Analyze text sentiment with structured output."""
result = await ctx.sample(
messages=f"Analyze the sentiment of: {text}",
result_type=SentimentResult,
)
-
- # result.result is a validated SentimentResult object
- return result.result
+ return result.result # A validated SentimentResult object
```
-### How Structured Output Works
-
-When you pass `result_type`:
+When you call this tool, the LLM returns a structured response that FastMCP validates against your Pydantic model. You access the validated object through `result.result`, while `result.text` contains the JSON representation.
-1. FastMCP creates a synthetic `final_response` tool with a schema derived from your type
-2. A hint is added to the system prompt: "Call final_response when you have completed the task."
-3. The LLM calls `final_response` with its structured response
-4. FastMCP validates the input against your type
-5. If validation fails, the error is sent back to the LLM for retry
-6. On success, `result.result` contains the validated object and `result.text` contains the JSON
+
+ When you pass `result_type`, `sample()` automatically creates a
+ `final_response` tool that the LLM calls to provide its response. If
+ validation fails, the error is sent back to the LLM for retry. This automatic
+ handling only applies to `sample()`—with `sample_step()`, you must manage
+ structured output yourself.
+
## Sampling with Tools
-
-
-Sampling with tools enables agentic workflows where the LLM can request tool calls during sampling. FastMCP automatically executes the tools and continues the conversation until the LLM provides a final response.
+
-### Creating Sampling Tools
+Sampling with tools enables agentic workflows where the LLM can call functions to gather information before responding. This implements [SEP-1577](https://github.com/modelcontextprotocol/modelcontextprotocol/issues/1577), allowing the LLM to autonomously orchestrate multi-step operations.
-Define regular Python functions and pass them directly to `ctx.sample()`:
-
-```python
-from fastmcp import FastMCP, Context
-
-def search(query: str) -> str:
- """Search the web for information."""
- return f"Search results for: {query}"
-
-def get_current_time() -> str:
- """Get the current time."""
- from datetime import datetime
- return datetime.now().strftime("%H:%M:%S")
-```
-
-
-To create a tool with a custom name or description, use `SamplingTool.from_function()`:
-
-```python
-from fastmcp.server.sampling import SamplingTool
-
-tool = SamplingTool.from_function(
- my_search_function,
- name="web_search",
- description="Search the web for information"
-)
-
-result = await ctx.sample(messages="...", tools=[tool])
-```
-
+Pass Python functions to the `tools` parameter, and FastMCP handles the execution loop automatically—calling tools, returning results to the LLM, and continuing until the LLM provides a final response.
-### Using Tools in Sampling
+### Defining Tools
-Pass tools to `ctx.sample()` to enable agentic tool use. FastMCP automatically handles the tool execution loop:
+Define regular Python functions with type hints and docstrings. FastMCP extracts the function's name, docstring, and parameter types to create tool schemas that the LLM can understand.
```python
from fastmcp import FastMCP, Context
def search(query: str) -> str:
- """Search the web."""
+ """Search the web for information."""
return f"Results for: {query}"
def get_time() -> str:
@@ -298,269 +236,205 @@ def get_time() -> str:
mcp = FastMCP()
@mcp.tool
-async def research_assistant(question: str, ctx: Context) -> str:
- """Answer questions using search and calculation tools."""
+async def research(question: str, ctx: Context) -> str:
+ """Answer questions using available tools."""
result = await ctx.sample(
messages=question,
- system_prompt="You are a research assistant. Use the available tools to answer questions.",
tools=[search, get_time],
)
-
return result.text
```
-### Tool Loop
-
-When you call `ctx.sample()` with tools, FastMCP automatically handles the execution loop:
-
-1. The LLM receives your prompt and the available tools
-2. If the LLM calls a tool, FastMCP executes it and sends the result back
-3. This continues until the LLM provides a final text response
+The LLM sees each function's signature and docstring, using this information to decide when and how to call them. Tool errors are caught and sent back to the LLM, allowing it to recover gracefully. An internal safety limit prevents infinite loops.
-Tool errors are automatically caught and sent back to the LLM, allowing it to handle failures gracefully.
-
-Use `max_iterations` to limit the number of LLM calls (default is 10). On the last iteration, FastMCP forces the LLM to return a final response rather than continuing to call tools:
+For custom names or descriptions, use `SamplingTool.from_function()`:
```python
-# Allow up to 5 tool-use cycles
-result = await ctx.sample(
- messages=prompt,
- tools=[search, get_time],
- max_iterations=5,
-)
+from fastmcp.server.sampling import SamplingTool
-# Single iteration mode - forces text on first call
-# Useful for building your own loop with result.history
-result = await ctx.sample(
- messages=prompt,
- tools=[search, get_time],
- max_iterations=1,
+tool = SamplingTool.from_function(
+ my_func,
+ name="custom_name",
+ description="Custom description"
)
-```
-
-### Tool Choice Options
-
-Control how the LLM uses tools with `tool_choice`:
-
-```python
-# Let the LLM decide (default)
-result = await ctx.sample(messages=prompt, tools=tools, tool_choice="auto")
-# Force the LLM to use a tool
-result = await ctx.sample(messages=prompt, tools=tools, tool_choice="required")
-
-# Prevent tool use (useful for final response)
-result = await ctx.sample(messages=prompt, tools=tools, tool_choice="none")
+result = await ctx.sample(messages="...", tools=[tool])
```
-### Structured Output
+### Combining with Structured Output
-Combine `result_type` with tools for agentic workflows that return structured data:
+Combine tools with `result_type` for agentic workflows that return validated, structured data. The LLM uses your tools to gather information, then returns a response matching your Pydantic schema.
```python
-from pydantic import BaseModel
-from fastmcp import FastMCP, Context
-
-class ResearchResult(BaseModel):
- summary: str
- sources: list[str]
- confidence: float
+result = await ctx.sample(
+ messages="Research Python async patterns",
+ tools=[search, fetch_url],
+ result_type=ResearchResult,
+)
+```
-def search(query: str) -> str:
- """Search for information."""
- return f"Found information about: {query}"
+## Loop Control
-def fetch_url(url: str) -> str:
- """Fetch content from a URL."""
- return f"Content from {url}"
+
-mcp = FastMCP()
+While `sample()` handles the tool execution loop automatically, some scenarios require fine-grained control over each step. The `sample_step()` method makes a single LLM call and returns a `SampleStep` containing the response and updated history.
-@mcp.tool
-async def research(topic: str, ctx: Context) -> ResearchResult:
- """Research a topic and return structured results."""
- result = await ctx.sample(
- messages=f"Research this topic thoroughly: {topic}",
- tools=[search, fetch_url],
- result_type=ResearchResult,
- )
-
- # The LLM uses search/fetch_url as needed, then calls final_response
- # result.result is the validated ResearchResult
- return result.result
-```
+Unlike `sample()`, `sample_step()` is stateless—it doesn't remember previous calls. You control the conversation by passing the full message history each time. The returned `step.history` includes all messages up through the current response, making it easy to continue the loop.
-The LLM can call your tools as many times as needed, and when it's ready to provide the final answer, it calls `final_response` with the structured data.
+Use `sample_step()` when you need to:
-### Building Custom Loops
+- Inspect tool calls before they execute
+- Implement custom termination conditions
+- Add logging, metrics, or checkpointing between steps
+- Build custom agentic loops with domain-specific logic
-The automatic tool loop handles most use cases, but sometimes you need finer control. Setting `max_iterations=1` runs a single sampling iteration and returns immediately, giving you access to `result.history` so you can continue the conversation yourself.
+### Using sample_step()
-This is useful when you need to:
-- Dynamically change which tools are available based on intermediate results
-- Inject additional context or instructions mid-conversation
-- Implement custom termination conditions beyond "LLM stopped calling tools"
-- Add logging, metrics, or checkpointing between iterations
+By default, `sample_step()` executes any tool calls and includes the results in the history. Call it in a loop, passing the updated history each time, until a stop condition is met.
```python
+from mcp.types import SamplingMessage
+
@mcp.tool
-async def custom_agent(question: str, ctx: Context) -> str:
- """Agent with custom loop logic."""
- history = []
- tools = [search, get_time]
+async def controlled_agent(question: str, ctx: Context) -> str:
+ """Agent with manual loop control."""
+ messages = [SamplingMessage(role="user", content=question)]
while True:
- result = await ctx.sample(
- messages=history or question,
- tools=tools,
- max_iterations=1,
+ step = await ctx.sample_step(
+ messages=messages,
+ tools=[search, get_time],
)
- history = result.history
- # Custom logic: check conditions, change tools, etc.
- if some_condition:
- tools = [different_tool]
+ if step.is_tool_use:
+ # Tools already executed (execute_tools=True by default)
+ # Log what was called before continuing
+ for call in step.tool_calls:
+ print(f"Called tool: {call.name}")
+
+ if step.is_text:
+ return step.text
- # When we get a text response, we're done
- if result.text:
- return result.text
+ # Continue with updated history
+ messages = step.history
```
-## Sampling Fallback Handler
+### SampleStep Properties
-Client support for sampling is optional. If the client does not support sampling, the server will report an error indicating that the client does not support sampling.
+Each `SampleStep` provides information about what the LLM returned:
-However, you can provide a `sampling_handler` to the FastMCP server, which sends sampling requests directly to an LLM provider instead of routing through the client. The `sampling_handler_behavior` parameter controls when this handler is used:
+- `step.is_tool_use` — True if the LLM requested tool calls
+- `step.is_text` — True if the LLM returned a text response
+- `step.tool_calls` — List of tool calls requested (if any)
+- `step.text` — The text content (if any)
+- `step.history` — All messages exchanged so far
-- **`"fallback"`** (default): Uses the handler only when the client doesn't support sampling. Requests go to the client first, falling back to the handler if needed.
-- **`"always"`**: Always uses the handler, bypassing the client entirely. Useful when you want full control over the LLM used for sampling.
+The contents of `step.history` depend on `execute_tools`:
+- **`execute_tools=True`** (default): Includes tool results, ready for the next iteration
+- **`execute_tools=False`**: Includes the assistant's tool request, but you add results yourself
-### OpenAI Handler
+### Manual Tool Execution
-FastMCP provides an OpenAI-compatible sampling handler that supports both basic sampling and tool use:
+Set `execute_tools=False` to handle tool execution yourself. When disabled, `step.history` contains the user message and the assistant's response with tool calls—but no tool results. You execute the tools and append the results as a user message.
```python
-import os
-from openai import OpenAI
-from fastmcp import FastMCP
-from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
-
-server = FastMCP(
- name="Sampling Server",
- sampling_handler=OpenAISamplingHandler(
- default_model="gpt-4o-mini",
- client=OpenAI(api_key=os.getenv("OPENAI_API_KEY")),
- ),
- sampling_handler_behavior="fallback",
-)
-```
-
-The OpenAI handler automatically handles tool conversion - when you pass tools to `ctx.sample()`, they're converted to OpenAI's function calling format.
+from mcp.types import SamplingMessage, ToolResultContent, TextContent
+from fastmcp import FastMCP, Context
-### Fallback Mode (Default)
+mcp = FastMCP()
-Uses the handler only when the client doesn't support sampling:
+@mcp.tool
+async def research(question: str, ctx: Context) -> str:
+ """Research with manual tool handling."""
-```python
-import asyncio
-import os
-from openai import OpenAI
+ def search(query: str) -> str:
+ return f"Results for: {query}"
-from fastmcp import FastMCP, Context
-from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+ def get_time() -> str:
+ return "12:00 PM"
+ # Map tool names to functions
+ tools = {"search": search, "get_time": get_time}
-async def async_main():
- server = FastMCP(
- name="OpenAI Sampling Fallback Example",
- sampling_handler=OpenAISamplingHandler(
- default_model="gpt-4o-mini",
- client=OpenAI(api_key=os.getenv("OPENAI_API_KEY")),
- ),
- sampling_handler_behavior="fallback",
- )
+ messages = [SamplingMessage(role="user", content=question)]
- @server.tool
- async def summarize(text: str, ctx: Context) -> str:
- # Uses client's LLM if available, falls back to OpenAI
- result = await ctx.sample(
- messages=f"Summarize: {text}",
+ while True:
+ step = await ctx.sample_step(
+ messages=messages,
+ tools=list(tools.values()),
+ execute_tools=False,
)
- return result.text
-
- await server.run_http_async()
-
-if __name__ == "__main__":
- asyncio.run(async_main())
+ if step.is_text:
+ return step.text
+
+ if step.is_tool_use:
+ tool_results = []
+ for call in step.tool_calls:
+ # Look up and execute the requested tool
+ fn = tools[call.name]
+ result = fn(**call.input)
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=call.id,
+ content=[TextContent(type="text", text=result)],
+ )
+ )
+
+ messages = list(step.history)
+ messages.append(SamplingMessage(role="user", content=tool_results))
```
-### Always Mode
+#### Error Handling
-Always uses the handler, bypassing the client:
+To report an error, set `isError=True`. The LLM will see the error and can decide how to proceed:
```python
-server = FastMCP(
- name="Server-Controlled Sampling",
- sampling_handler=OpenAISamplingHandler(
- default_model="gpt-4o-mini",
- client=OpenAI(api_key=os.getenv("API_KEY")),
- ),
- sampling_handler_behavior="always",
+tool_result = ToolResultContent(
+ type="tool_result",
+ toolUseId=call.id,
+ content=[TextContent(type="text", text="Permission denied")],
+ isError=True,
)
-
-@server.tool
-async def analyze_data(data: str, ctx: Context) -> str:
- # Always uses the server's configured LLM
- result = await ctx.sample(
- messages=f"Analyze this data: {data}",
- system_prompt="You are a data analyst.",
- )
- return result.text
```
-### Fallback with Tools
+## Fallback Handlers
+
+Client support for sampling is optional—some clients may not implement it. To ensure your tools work regardless of client capabilities, configure a `sampling_handler` that sends requests directly to an LLM provider.
-The fallback handler fully supports sampling with tools:
+### OpenAI Handler
+
+FastMCP provides an OpenAI-compatible handler that works with OpenAI's API and compatible providers. It supports the full sampling API including tools, automatically converting your Python functions to OpenAI's function calling format.
```python
-from fastmcp import FastMCP, Context
-from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
-from openai import OpenAI
import os
-
-def search(query: str) -> str:
- """Search for information."""
- return f"Results for: {query}"
+from openai import OpenAI
+from fastmcp import FastMCP
+from fastmcp.experimental.sampling.handlers.openai import (
+ OpenAISamplingHandler,
+)
server = FastMCP(
- name="Agentic Server",
+ name="My Server",
sampling_handler=OpenAISamplingHandler(
default_model="gpt-4o-mini",
client=OpenAI(api_key=os.getenv("OPENAI_API_KEY")),
),
- sampling_handler_behavior="always",
+ sampling_handler_behavior="fallback",
)
-
-@server.tool
-async def research(question: str, ctx: Context) -> str:
- """Research a topic using tools."""
- result = await ctx.sample(
- messages=question,
- tools=[search],
- )
- return result.text
```
-## Client Requirements
+### Behavior Modes
-By default, LLM sampling requires client support:
+The `sampling_handler_behavior` parameter controls when the handler is used:
-- Clients must implement sampling handlers to process requests (see [Client Sampling](/clients/sampling))
-- If the client doesn't support sampling and no fallback handler is configured, `ctx.sample()` will raise an error
-- Configure a `sampling_handler` with `sampling_handler_behavior="fallback"` to automatically handle clients that don't support sampling
-- Use `sampling_handler_behavior="always"` to completely bypass the client and control which LLM is used
+- **`"fallback"`** (default): Use the handler only when the client doesn't support sampling. This lets capable clients use their own LLM while ensuring your tools still work with clients that lack sampling support.
+- **`"always"`**: Always use the handler, bypassing the client entirely. Use this when you need guaranteed control over which LLM processes requests—for cost control, compliance requirements, or when specific model characteristics are essential.
-Sampling with tools requires a client that advertises the `sampling.tools` capability. FastMCP clients automatically advertise this capability when a `sampling_handler` is provided. For external clients that don't support tool-enabled sampling, configure a fallback handler with `sampling_handler_behavior="always"`.
+ Sampling with tools requires the client to advertise the `sampling.tools`
+ capability. FastMCP clients do this automatically. For external clients that
+ don't support tool-enabled sampling, configure a fallback handler with
+ `sampling_handler_behavior="always"`.
diff --git a/examples/advanced_sampling/tool_use.py b/examples/advanced_sampling/tool_use.py
index a3302e47a3..9de42e8590 100644
--- a/examples/advanced_sampling/tool_use.py
+++ b/examples/advanced_sampling/tool_use.py
@@ -60,7 +60,6 @@ async def research(question: str, ctx: Context) -> dict:
system_prompt="You are a research assistant. Use the available tools to gather information, then call final_response with your structured report.",
tools=[search_web, get_word_count],
result_type=ResearchReport,
- max_iterations=5,
)
return result.result.model_dump() # type: ignore[attr-defined]
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index 51854ae70c..f481907d9c 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -172,18 +172,18 @@ def _convert_to_openai_messages(
text_parts.append(item.text)
elif isinstance(item, ToolResultContent):
# Each tool result becomes a separate tool message
- content_parts: list[dict[str, str]] = []
+ content_text = ""
if item.content:
+ result_texts = []
for sub_item in item.content:
if isinstance(sub_item, TextContent):
- content_parts.append(
- {"type": "text", "text": sub_item.text}
- )
+ result_texts.append(sub_item.text)
+ content_text = "\n".join(result_texts)
openai_messages.append(
ChatCompletionToolMessageParam(
role="tool",
tool_call_id=item.toolUseId,
- content=content_parts if content_parts else "",
+ content=content_text,
)
)
@@ -237,18 +237,16 @@ def _convert_to_openai_messages(
# Handle ToolResultContent (user's tool results)
if isinstance(content, ToolResultContent):
# Extract text parts from the content list
- result_parts: list[dict[str, str]] = []
+ result_texts: list[str] = []
if content.content:
for item in content.content:
if isinstance(item, TextContent):
- result_parts.append(
- {"type": "text", "text": item.text}
- )
+ result_texts.append(item.text)
openai_messages.append(
ChatCompletionToolMessageParam(
role="tool",
tool_call_id=content.toolUseId,
- content=result_parts if result_parts else "",
+ content="\n".join(result_texts),
)
)
continue
@@ -372,9 +370,13 @@ def _chat_completion_to_result_with_tools(
# Add tool calls if present
if message.tool_calls:
for tool_call in message.tool_calls:
+ # Skip non-function tool calls
+ if not hasattr(tool_call, "function"):
+ continue
+ func = tool_call.function # type: ignore[union-attr]
# Parse the arguments JSON string
try:
- arguments = json.loads(tool_call.function.arguments)
+ arguments = json.loads(func.arguments) # type: ignore[union-attr]
except json.JSONDecodeError:
arguments = {}
@@ -382,7 +384,7 @@ def _chat_completion_to_result_with_tools(
ToolUseContent(
type="tool_use",
id=tool_call.id,
- name=tool_call.function.name,
+ name=func.name, # type: ignore[union-attr]
input=arguments,
)
)
@@ -392,7 +394,7 @@ def _chat_completion_to_result_with_tools(
raise ValueError("No content in response from completion")
return CreateMessageResultWithTools(
- content=content,
+ content=content, # type: ignore[arg-type]
role="assistant",
model=chat_completion.model,
stopReason=stop_reason,
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index ed2a9f2a71..777c0bff44 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -1,7 +1,6 @@
from __future__ import annotations
import copy
-import inspect
import json
import logging
import weakref
@@ -10,7 +9,7 @@
from contextvars import ContextVar, Token
from dataclasses import dataclass
from logging import Logger
-from typing import Any, Generic, Literal, cast, overload
+from typing import Any, Literal, cast, overload
import anyio
from mcp import LoggingLevel, ServerSession
@@ -18,24 +17,19 @@
from mcp.server.lowlevel.server import request_ctx
from mcp.shared.context import RequestContext
from mcp.types import (
- ClientCapabilities,
CreateMessageResult,
CreateMessageResultWithTools,
GetPromptResult,
- IncludeContext,
ModelHint,
ModelPreferences,
Root,
- SamplingCapability,
SamplingMessage,
SamplingMessageContentBlock,
- SamplingToolsCapability,
TextContent,
ToolChoice,
ToolResultContent,
ToolUseContent,
)
-from mcp.types import CreateMessageRequestParams as SamplingParams
from mcp.types import Prompt as SDKPrompt
from mcp.types import Resource as SDKResource
from mcp.types import Tool as SDKTool
@@ -50,7 +44,15 @@
handle_elicit_accept,
parse_elicit_response_type,
)
-from fastmcp.server.sampling import SamplingTool
+from fastmcp.server.sampling import (
+ SampleStep,
+ SamplingResult,
+ SamplingTool,
+ call_client,
+ call_sampling_handler,
+ determine_handler_mode,
+)
+from fastmcp.server.sampling import execute_tools as run_sampling_tools
from fastmcp.server.server import FastMCP
from fastmcp.utilities.json_schema import compress_schema
from fastmcp.utilities.logging import _clamp_logger, get_logger
@@ -74,28 +76,6 @@
_current_context: ContextVar[Context | None] = ContextVar("context", default=None) # type: ignore[assignment]
-@dataclass
-class SamplingResult(Generic[ResultT]):
- """Result from ctx.sample() containing the response and history.
-
- This is a generic class where ResultT defaults to str for text responses.
- When a result_type is specified, ResultT is that type.
-
- Attributes:
- text: The text representation of the result. For text responses, this is
- the raw text. For structured responses, this is the JSON representation.
- None if the response was a tool use (for manual loop building).
- result: The typed result. When ResultT is str, this equals text. When ResultT
- is a custom type, this is the validated/parsed object.
- history: List of all messages exchanged during sampling, including tool calls
- and results. Useful for continuing conversations or debugging.
- """
-
- text: str | None
- result: ResultT
- history: list[SamplingMessage]
-
-
_flush_lock = anyio.Lock()
@@ -561,19 +541,152 @@ async def long_running_task(ctx: Context) -> str:
return
await self.request_context.close_sse_stream()
+ async def sample_step(
+ self,
+ messages: str | Sequence[str | SamplingMessage],
+ *,
+ system_prompt: str | None = None,
+ temperature: float | None = None,
+ max_tokens: int | None = None,
+ model_preferences: ModelPreferences | str | list[str] | None = None,
+ tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
+ tool_choice: ToolChoiceOption | str | None = None,
+ execute_tools: bool = True,
+ ) -> SampleStep:
+ """
+ Make a single LLM sampling call.
+
+ This is a stateless function that makes exactly one LLM call and optionally
+ executes any requested tools. Use this for fine-grained control over the
+ sampling loop.
+
+ Args:
+ messages: The message(s) to send. Can be a string, list of strings,
+ or list of SamplingMessage objects.
+ system_prompt: Optional system prompt for the LLM.
+ temperature: Optional sampling temperature.
+ max_tokens: Maximum tokens to generate. Defaults to 512.
+ model_preferences: Optional model preferences.
+ tools: Optional list of tools the LLM can use.
+ tool_choice: Tool choice mode ("auto", "required", "none", or tool name).
+ execute_tools: If True (default), execute tool calls and append results
+ to history. If False, return immediately with tool_calls available
+ in the step for manual execution.
+
+ Returns:
+ SampleStep containing:
+ - .response: The raw LLM response
+ - .history: Messages including input, assistant response, and tool results
+ - .is_tool_use / .is_text: Check what kind of response
+ - .tool_calls: List of tool calls (if any)
+ - .text: The text content (if any)
+
+ Example:
+ messages = [SamplingMessage(role="user", content="Research X")]
+
+ while True:
+ step = await ctx.sample_step(messages, tools=[search])
+
+ if step.is_text:
+ print(step.text)
+ break
+
+ # Continue with tool results
+ messages = step.history
+ """
+ # Convert messages to SamplingMessage objects
+ current_messages = _prepare_messages(messages)
+
+ # Convert tools to SamplingTools
+ sampling_tools = _prepare_tools(tools)
+ sdk_tools: list[SDKTool] | None = (
+ [t._to_sdk_tool() for t in sampling_tools] if sampling_tools else None
+ )
+ tool_map: dict[str, SamplingTool] = (
+ {t.name: t for t in sampling_tools} if sampling_tools else {}
+ )
+
+ # Determine whether to use fallback handler or client
+ use_fallback = determine_handler_mode(self, bool(sampling_tools))
+
+ # Build tool choice
+ effective_tool_choice: ToolChoice | None = None
+ if tool_choice is not None:
+ effective_tool_choice = ToolChoice(
+ mode=cast(Literal["auto", "required", "none"], tool_choice)
+ )
+
+ # Effective max_tokens
+ effective_max_tokens = max_tokens if max_tokens is not None else 512
+
+ # Make the LLM call
+ if use_fallback:
+ response = await call_sampling_handler(
+ self,
+ current_messages,
+ system_prompt=system_prompt,
+ temperature=temperature,
+ max_tokens=effective_max_tokens,
+ model_preferences=model_preferences,
+ sdk_tools=sdk_tools,
+ tool_choice=effective_tool_choice,
+ )
+ else:
+ response = await call_client(
+ self,
+ current_messages,
+ system_prompt=system_prompt,
+ temperature=temperature,
+ max_tokens=effective_max_tokens,
+ model_preferences=model_preferences,
+ sdk_tools=sdk_tools,
+ tool_choice=effective_tool_choice,
+ )
+
+ # Check if this is a tool use response
+ is_tool_use_response = (
+ isinstance(response, CreateMessageResultWithTools)
+ and response.stopReason == "toolUse"
+ )
+
+ # If not a tool use, return immediately
+ if not is_tool_use_response:
+ return SampleStep(response=response, history=current_messages)
+
+ # Add assistant's response to messages
+ current_messages.append(
+ SamplingMessage(role="assistant", content=response.content)
+ )
+
+ # If not executing tools, return with assistant message but no tool results
+ if not execute_tools:
+ return SampleStep(response=response, history=current_messages)
+
+ # Execute tools and add results to history
+ step_tool_calls = _extract_tool_calls(response)
+ if step_tool_calls:
+ tool_results = await run_sampling_tools(step_tool_calls, tool_map)
+
+ if tool_results:
+ current_messages.append(
+ SamplingMessage(
+ role="user",
+ content=tool_results, # type: ignore[arg-type]
+ )
+ )
+
+ return SampleStep(response=response, history=current_messages)
+
@overload
async def sample(
self,
messages: str | Sequence[str | SamplingMessage],
*,
system_prompt: str | None = None,
- include_context: IncludeContext | None = None,
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
- tool_choice: ToolChoiceOption | None = None,
- max_iterations: int = 10,
result_type: type[ResultT],
) -> SamplingResult[ResultT]:
"""Overload: With result_type, returns SamplingResult[ResultT]."""
@@ -584,13 +697,10 @@ async def sample(
messages: str | Sequence[str | SamplingMessage],
*,
system_prompt: str | None = None,
- include_context: IncludeContext | None = None,
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
- tool_choice: ToolChoiceOption | None = None,
- max_iterations: int = 10,
result_type: None = None,
) -> SamplingResult[str]:
"""Overload: Without result_type, returns SamplingResult[str]."""
@@ -600,48 +710,35 @@ async def sample(
messages: str | Sequence[str | SamplingMessage],
*,
system_prompt: str | None = None,
- include_context: IncludeContext | None = None,
temperature: float | None = None,
max_tokens: int | None = None,
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
- tool_choice: ToolChoiceOption | None = None,
- max_iterations: int = 10,
result_type: type[ResultT] | None = None,
) -> SamplingResult[ResultT] | SamplingResult[str]:
"""
Send a sampling request to the client and await the response.
- Call this method at any time to have the server request an LLM
- completion from the client. The client must be appropriately configured,
- or the request will error.
-
- When tools are provided, the method automatically executes a tool loop:
- if the LLM returns a tool use request, the tools are executed and the
- results are sent back to the LLM. This continues until the LLM provides
- a final response or max_iterations is reached.
+ This method runs to completion automatically. When tools are provided,
+ it executes a tool loop: if the LLM returns a tool use request, the tools
+ are executed and the results are sent back to the LLM. This continues
+ until the LLM provides a final text response.
- When result_type is specified (not str), a synthetic `final_response` tool
- is created. The LLM calls this tool to provide the structured response,
+ When result_type is specified, a synthetic `final_response` tool is
+ created. The LLM calls this tool to provide the structured response,
which is validated against the result_type and returned as `.result`.
+ For fine-grained control over the sampling loop, use sample_step() instead.
+
Args:
messages: The message(s) to send. Can be a string, list of strings,
or list of SamplingMessage objects.
system_prompt: Optional system prompt for the LLM.
- include_context: Optional context inclusion setting.
temperature: Optional sampling temperature.
max_tokens: Maximum tokens to generate. Defaults to 512.
model_preferences: Optional model preferences.
tools: Optional list of tools the LLM can use. Accepts plain
- functions or SamplingTools. When provided, the method automatically
- handles tool execution and returns the final response after all
- tool calls complete.
- tool_choice: Optional control over tool usage behavior. Only valid
- when tools are provided.
- max_iterations: Maximum number of LLM calls before returning.
- Defaults to 10. Set to 1 for single-iteration mode where you
- can build your own loop using the history.
+ functions or SamplingTools.
result_type: Optional type for structured output. When specified,
a synthetic `final_response` tool is created and the LLM's
response is validated against this type.
@@ -652,467 +749,95 @@ async def sample(
- .result: The typed result (str for text, parsed object for structured)
- .history: All messages exchanged during sampling
"""
+ # Safety limit to prevent infinite loops
+ max_iterations = 50
- if max_tokens is None:
- max_tokens = 512
+ # Convert tools to SamplingTools
+ sampling_tools = _prepare_tools(tools)
+ has_user_tools = bool(sampling_tools)
- if isinstance(messages, str):
- sampling_messages = [
- SamplingMessage(
- content=TextContent(text=messages, type="text"), role="user"
- )
- ]
- elif isinstance(messages, Sequence):
- sampling_messages = [
- SamplingMessage(content=TextContent(text=m, type="text"), role="user")
- if isinstance(m, str)
- else m
- for m in messages
- ]
-
- # Convert tools to SamplingTools, then to SDK tools
- sampling_tools: list[SamplingTool] = []
- if tools is not None:
- for t in tools:
- if isinstance(t, SamplingTool):
- sampling_tools.append(t)
- elif callable(t):
- sampling_tools.append(SamplingTool.from_function(t))
- else:
- raise TypeError(f"Expected SamplingTool or callable, got {type(t)}")
-
- # Create synthetic final_response tool for structured output
- final_response_tool: SamplingTool | None = None
+ # Handle structured output with result_type
+ tool_choice: str | None = None
if result_type is not None and result_type is not str:
final_response_tool = _create_final_response_tool(result_type)
+ sampling_tools = list(sampling_tools) if sampling_tools else []
sampling_tools.append(final_response_tool)
- # Add hint to system prompt
- hint = "Call final_response when you have completed the task."
- system_prompt = f"{system_prompt}\n\n{hint}" if system_prompt else hint
-
- sdk_tools: list[SDKTool] | None = None
- if sampling_tools:
- sdk_tools = [t._to_sdk_tool() for t in sampling_tools]
-
- # Build a tool lookup for execution
- tool_map: dict[str, SamplingTool] = {}
- if sampling_tools:
- tool_map = {t.name: t for t in sampling_tools}
-
- should_fallback = (
- self.fastmcp.sampling_handler_behavior == "fallback"
- and not self.session.check_client_capability(
- capability=ClientCapabilities(sampling=SamplingCapability())
- )
- )
+ # If no user tools, force the LLM to call a tool (which is final_response)
+ if not has_user_tools:
+ tool_choice = "required"
- if self.fastmcp.sampling_handler_behavior == "always" or should_fallback:
- if self.fastmcp.sampling_handler is None:
- raise ValueError("Client does not support sampling")
+ # Convert messages for the loop
+ current_messages: str | Sequence[str | SamplingMessage] = messages
- return await self._sample_with_fallback_handler(
- sampling_messages=sampling_messages,
- system_prompt=system_prompt,
- temperature=temperature,
- max_tokens=max_tokens,
- model_preferences=model_preferences,
- sdk_tools=sdk_tools,
- tool_choice=tool_choice,
- tool_map=tool_map,
- max_iterations=max_iterations,
- result_type=result_type,
- final_response_tool=final_response_tool,
- )
-
- # When tools are provided, we need the client to support sampling.tools
- if sampling_tools:
- has_tools_capability = self.session.check_client_capability(
- capability=ClientCapabilities(
- sampling=SamplingCapability(tools=SamplingToolsCapability())
- )
- )
- if not has_tools_capability:
- raise ValueError(
- "Client does not support sampling with tools. "
- "The client must advertise the sampling.tools capability."
- )
-
- return await self._sample_with_client(
- sampling_messages=sampling_messages,
+ for _iteration in range(max_iterations):
+ step = await self.sample_step(
+ messages=current_messages,
system_prompt=system_prompt,
- include_context=include_context,
temperature=temperature,
max_tokens=max_tokens,
model_preferences=model_preferences,
- sdk_tools=sdk_tools,
+ tools=sampling_tools,
tool_choice=tool_choice,
- tool_map=tool_map,
- max_iterations=max_iterations,
- result_type=result_type,
- final_response_tool=final_response_tool,
- )
-
- # Simple case: no tools, no structured output
- result = await self.session.create_message(
- messages=sampling_messages,
- system_prompt=system_prompt,
- include_context=include_context,
- temperature=temperature,
- max_tokens=max_tokens,
- model_preferences=_parse_model_preferences(model_preferences),
- related_request_id=self.request_id,
- )
-
- # Extract text from content
- text = _extract_text_from_content(result.content)
-
- return SamplingResult(
- text=text,
- result=cast(ResultT, text),
- history=list(sampling_messages),
- )
-
- async def _sample_with_fallback_handler(
- self,
- sampling_messages: list[SamplingMessage],
- system_prompt: str | None,
- temperature: float | None,
- max_tokens: int,
- model_preferences: ModelPreferences | str | list[str] | None,
- sdk_tools: list[SDKTool] | None,
- tool_choice: ToolChoiceOption | None,
- tool_map: dict[str, SamplingTool],
- max_iterations: int,
- result_type: type[ResultT] | None,
- final_response_tool: SamplingTool | None,
- ) -> SamplingResult[ResultT] | SamplingResult[str]:
- """Execute sampling with fallback handler, including automatic tool loop."""
- assert self.fastmcp.sampling_handler is not None
-
- current_messages = list(sampling_messages)
- iteration = 0
- has_tools = bool(sdk_tools)
- has_result_type = result_type is not None and result_type is not str
-
- while True:
- # On last iteration, force completion
- effective_tool_choice: ToolChoice | None = (
- ToolChoice(mode=tool_choice) if tool_choice else None
- )
- if iteration == max_iterations - 1:
- if has_result_type and final_response_tool is not None:
- # Force final_response tool on last iteration
- effective_tool_choice = ToolChoice(
- mode="required", name="final_response"
- )
- elif has_tools:
- # Force text response (no tools) on last iteration
- effective_tool_choice = ToolChoice(mode="none")
-
- create_message_result = self.fastmcp.sampling_handler(
- current_messages,
- SamplingParams(
- systemPrompt=system_prompt,
- messages=current_messages,
- temperature=temperature,
- maxTokens=max_tokens,
- modelPreferences=_parse_model_preferences(model_preferences),
- tools=sdk_tools,
- toolChoice=effective_tool_choice,
- ),
- self.request_context,
)
- if inspect.isawaitable(create_message_result):
- create_message_result = await create_message_result
-
- iteration += 1
-
- # Handle simple string/content responses
- if isinstance(create_message_result, str):
- if has_tools:
- raise ValueError(
- "Sampling handler returned a string, but tools were provided. "
- "Handler must return CreateMessageResultWithTools when tools are used."
- )
- return SamplingResult(
- text=create_message_result,
- result=cast(ResultT, create_message_result),
- history=current_messages,
- )
-
- if isinstance(create_message_result, CreateMessageResult):
- if has_tools:
- raise ValueError(
- "Sampling handler returned CreateMessageResult, but tools were provided. "
- "Handler must return CreateMessageResultWithTools when tools are used."
- )
- text = _extract_text_from_content(create_message_result.content)
- return SamplingResult(
- text=text,
- result=cast(ResultT, text),
- history=current_messages,
- )
-
- if isinstance(create_message_result, CreateMessageResultWithTools):
- # If not a tool use, this is the final response
- if create_message_result.stopReason != "toolUse" or not tool_map:
- text = _extract_text_from_content(create_message_result.content)
- return SamplingResult(
- text=text,
- result=cast(ResultT, text),
- history=current_messages,
- )
-
- # Check if we've hit max iterations
- if iteration >= max_iterations:
- # Return what we have with None text (tool use without completion)
- return SamplingResult(
- text=None,
- result=cast(ResultT, None),
- history=current_messages,
- )
-
- # Execute tool calls
- result = await self._execute_tool_calls(
- result=create_message_result,
- current_messages=current_messages,
- tool_map=tool_map,
- result_type=result_type,
- final_response_tool=final_response_tool,
- )
-
- if isinstance(result, SamplingResult):
- # final_response was called, return the structured result
- return result
-
- # result is a tuple of (messages, should_continue)
- current_messages = cast(list[SamplingMessage], result[0])
- should_continue = result[1]
- if not should_continue:
- text = _extract_text_from_content(create_message_result.content)
- return SamplingResult(
- text=text,
- result=cast(ResultT, text),
- history=current_messages,
- )
- continue
-
- raise ValueError(
- f"Unexpected sampling handler result: {create_message_result}"
- )
-
- async def _sample_with_client(
- self,
- sampling_messages: list[SamplingMessage],
- system_prompt: str | None,
- include_context: IncludeContext | None,
- temperature: float | None,
- max_tokens: int,
- model_preferences: ModelPreferences | str | list[str] | None,
- sdk_tools: list[SDKTool] | None,
- tool_choice: ToolChoiceOption | None,
- tool_map: dict[str, SamplingTool],
- max_iterations: int,
- result_type: type[ResultT] | None,
- final_response_tool: SamplingTool | None,
- ) -> SamplingResult[ResultT] | SamplingResult[str]:
- """Execute sampling with client, including automatic tool loop."""
- current_messages = list(sampling_messages)
- iteration = 0
- has_tools = bool(sdk_tools)
- has_result_type = result_type is not None and result_type is not str
-
- while True:
- # On last iteration, force completion
- effective_tool_choice: ToolChoice | None = (
- ToolChoice(mode=tool_choice) if tool_choice else None
- )
- if iteration == max_iterations - 1:
- if has_result_type and final_response_tool is not None:
- # Force final_response tool on last iteration
- effective_tool_choice = ToolChoice(
- mode="required", name="final_response"
- )
- elif has_tools:
- # Force text response (no tools) on last iteration
- effective_tool_choice = ToolChoice(mode="none")
-
- result = await self.session.create_message(
- messages=current_messages,
- system_prompt=system_prompt,
- include_context=include_context,
- temperature=temperature,
- max_tokens=max_tokens,
- model_preferences=_parse_model_preferences(model_preferences),
- tools=sdk_tools,
- tool_choice=effective_tool_choice,
- related_request_id=self.request_id,
- )
- iteration += 1
-
- # If not a tool use or no tools to execute, return text result
- if result.stopReason != "toolUse" or not tool_map:
- text = _extract_text_from_content(result.content)
- return SamplingResult(
- text=text,
- result=cast(ResultT, text),
- history=current_messages,
- )
-
- # Check if we've hit max iterations
- if iteration >= max_iterations:
- # Return what we have with None text (tool use without completion)
- return SamplingResult(
- text=None,
- result=cast(ResultT, None),
- history=current_messages,
- )
-
- # Execute tool calls
- tool_result = await self._execute_tool_calls(
- result=result,
- current_messages=current_messages,
- tool_map=tool_map,
- result_type=result_type,
- final_response_tool=final_response_tool,
- )
-
- if isinstance(tool_result, SamplingResult):
- # final_response was called, return the structured result
- return tool_result
+ # Check for final_response tool call for structured output
+ if result_type is not None and result_type is not str and step.is_tool_use:
+ for tool_call in step.tool_calls:
+ if tool_call.name == "final_response":
+ # Validate and return the structured result
+ type_adapter = get_cached_typeadapter(result_type)
+ try:
+ validated_result = type_adapter.validate_python(
+ tool_call.input
+ )
+ text = json.dumps(
+ type_adapter.dump_python(validated_result, mode="json")
+ )
+ return SamplingResult(
+ text=text,
+ result=validated_result,
+ history=step.history,
+ )
+ except Exception as e:
+ # Validation failed - add error as tool result
+ step.history.append(
+ SamplingMessage(
+ role="user",
+ content=[
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_call.id,
+ content=[
+ TextContent(
+ type="text",
+ text=(
+ f"Validation error: {e}. "
+ "Please try again with valid data."
+ ),
+ )
+ ],
+ isError=True,
+ )
+ ], # type: ignore[arg-type]
+ )
+ )
- # tool_result is a tuple of (messages, should_continue)
- current_messages = cast(list[SamplingMessage], tool_result[0])
- should_continue = tool_result[1]
- if not should_continue:
- text = _extract_text_from_content(result.content)
+ # If text response, we're done
+ if step.is_text:
return SamplingResult(
- text=text,
- result=cast(ResultT, text),
- history=current_messages,
+ text=step.text,
+ result=cast(ResultT, step.text if step.text else ""),
+ history=step.history,
)
- async def _execute_tool_calls(
- self,
- result: CreateMessageResultWithTools,
- current_messages: list[SamplingMessage],
- tool_map: dict[str, SamplingTool],
- result_type: type[ResultT] | None,
- final_response_tool: SamplingTool | None,
- ) -> (
- tuple[list[SamplingMessage], bool]
- | SamplingResult[ResultT]
- | SamplingResult[str]
- ):
- """Execute tool calls from an LLM response and return updated messages.
-
- Returns:
- - Tuple of (updated_messages, should_continue) for normal tool calls
- - SamplingResult when final_response tool is called with valid input
- """
- # Find all tool use content blocks
- content_list = (
- result.content if isinstance(result.content, list) else [result.content]
- )
- tool_use_blocks = [c for c in content_list if isinstance(c, ToolUseContent)]
-
- if not tool_use_blocks:
- return current_messages, False
-
- # Add the assistant's response to messages
- current_messages.append(
- SamplingMessage(role="assistant", content=result.content)
- )
-
- # Execute each tool and collect results into a single list
- tool_results: list[ToolResultContent] = []
-
- for tool_use in tool_use_blocks:
- # Check if this is the final_response tool
- if (
- tool_use.name == "final_response"
- and final_response_tool is not None
- and result_type is not None
- and result_type is not str
- ):
- # Validate and parse the input as the result type
- try:
- type_adapter = get_cached_typeadapter(result_type)
- validated_result = type_adapter.validate_python(tool_use.input)
- # Convert to JSON for .text
- text = json.dumps(
- type_adapter.dump_python(validated_result, mode="json")
- )
- return SamplingResult(
- text=text,
- result=validated_result,
- history=current_messages,
- )
- except Exception as e:
- # Validation failed - add error and continue the loop
- tool_results.append(
- ToolResultContent(
- type="tool_result",
- toolUseId=tool_use.id,
- content=[
- TextContent(
- type="text",
- text=f"Validation error: {e}. Please try again.",
- )
- ],
- isError=True,
- )
- )
- continue
-
- tool = tool_map.get(tool_use.name)
- if tool is None:
- # Tool not found - add error result
- tool_results.append(
- ToolResultContent(
- type="tool_result",
- toolUseId=tool_use.id,
- content=[
- TextContent(
- type="text",
- text=f"Error: Unknown tool '{tool_use.name}'",
- )
- ],
- isError=True,
- )
- )
- else:
- try:
- result_value = await tool.run(tool_use.input)
- tool_results.append(
- ToolResultContent(
- type="tool_result",
- toolUseId=tool_use.id,
- content=[TextContent(type="text", text=str(result_value))],
- )
- )
- except Exception as e:
- tool_results.append(
- ToolResultContent(
- type="tool_result",
- toolUseId=tool_use.id,
- content=[TextContent(type="text", text=f"Error: {e}")],
- isError=True,
- )
- )
+ # Continue with the updated history
+ current_messages = step.history
- # Add all tool results as a single user message with list content
- if tool_results:
- current_messages.append(
- SamplingMessage(
- role="user",
- content=tool_results, # type: ignore[arg-type]
- )
- )
+ # After first iteration, reset tool_choice to auto
+ tool_choice = None
- return current_messages, True
+ raise RuntimeError(f"Sampling exceeded maximum iterations ({max_iterations})")
@overload
async def elicit(
@@ -1342,8 +1067,58 @@ def _extract_text_from_content(
if isinstance(content, list):
for block in content:
if hasattr(block, "text"):
- return block.text
+ return block.text # type: ignore[return-value]
return None
elif hasattr(content, "text"):
- return content.text
+ return content.text # type: ignore[return-value]
return None
+
+
+def _prepare_messages(
+ messages: str | Sequence[str | SamplingMessage],
+) -> list[SamplingMessage]:
+ """Convert various message formats to a list of SamplingMessage objects."""
+ if isinstance(messages, str):
+ return [
+ SamplingMessage(
+ content=TextContent(text=messages, type="text"), role="user"
+ )
+ ]
+ else:
+ return [
+ SamplingMessage(content=TextContent(text=m, type="text"), role="user")
+ if isinstance(m, str)
+ else m
+ for m in messages
+ ]
+
+
+def _prepare_tools(
+ tools: Sequence[SamplingTool | Callable[..., Any]] | None,
+) -> list[SamplingTool] | None:
+ """Convert tools to SamplingTool objects."""
+ if tools is None:
+ return None
+
+ sampling_tools: list[SamplingTool] = []
+ for t in tools:
+ if isinstance(t, SamplingTool):
+ sampling_tools.append(t)
+ elif callable(t):
+ sampling_tools.append(SamplingTool.from_function(t))
+ else:
+ raise TypeError(f"Expected SamplingTool or callable, got {type(t)}")
+
+ return sampling_tools if sampling_tools else None
+
+
+def _extract_tool_calls(
+ response: CreateMessageResult | CreateMessageResultWithTools,
+) -> list[ToolUseContent]:
+ """Extract tool calls from a response."""
+ content = response.content
+ if isinstance(content, list):
+ return [c for c in content if isinstance(c, ToolUseContent)]
+ elif isinstance(content, ToolUseContent):
+ return [content]
+ return []
diff --git a/src/fastmcp/server/sampling/__init__.py b/src/fastmcp/server/sampling/__init__.py
index 6485e658ec..e6e05a29f6 100644
--- a/src/fastmcp/server/sampling/__init__.py
+++ b/src/fastmcp/server/sampling/__init__.py
@@ -1,9 +1,23 @@
"""Sampling module for FastMCP servers."""
from fastmcp.server.sampling.handler import ServerSamplingHandler
+from fastmcp.server.sampling.run import (
+ SampleStep,
+ SamplingResult,
+ call_client,
+ call_sampling_handler,
+ determine_handler_mode,
+ execute_tools,
+)
from fastmcp.server.sampling.sampling_tool import SamplingTool
__all__ = [
+ "SampleStep",
+ "SamplingResult",
"SamplingTool",
"ServerSamplingHandler",
+ "call_client",
+ "call_sampling_handler",
+ "determine_handler_mode",
+ "execute_tools",
]
diff --git a/src/fastmcp/server/sampling/run.py b/src/fastmcp/server/sampling/run.py
new file mode 100644
index 0000000000..a2b206777c
--- /dev/null
+++ b/src/fastmcp/server/sampling/run.py
@@ -0,0 +1,290 @@
+"""Sampling types and helper functions for FastMCP servers."""
+
+from __future__ import annotations
+
+import inspect
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Generic
+
+from mcp.types import (
+ ClientCapabilities,
+ CreateMessageResult,
+ CreateMessageResultWithTools,
+ ModelHint,
+ ModelPreferences,
+ SamplingCapability,
+ SamplingMessage,
+ SamplingToolsCapability,
+ TextContent,
+ ToolChoice,
+ ToolResultContent,
+ ToolUseContent,
+)
+from mcp.types import CreateMessageRequestParams as SamplingParams
+from mcp.types import Tool as SDKTool
+from typing_extensions import TypeVar
+
+from fastmcp.server.sampling.sampling_tool import SamplingTool
+
+if TYPE_CHECKING:
+ from fastmcp.server.context import Context
+
+ResultT = TypeVar("ResultT", default=str)
+
+
+@dataclass
+class SamplingResult(Generic[ResultT]):
+ """Result of a sampling operation.
+
+ Attributes:
+ text: The text representation of the result (raw text or JSON for structured).
+ result: The typed result (str for text, parsed object for structured output).
+ history: All messages exchanged during sampling.
+ """
+
+ text: str | None
+ result: ResultT
+ history: list[SamplingMessage]
+
+
+@dataclass
+class SampleStep:
+ """Result of a single sampling call.
+
+ Represents what the LLM returned in this step plus the message history.
+ """
+
+ response: CreateMessageResult | CreateMessageResultWithTools
+ history: list[SamplingMessage]
+
+ @property
+ def is_tool_use(self) -> bool:
+ """True if the LLM is requesting tool execution."""
+ if isinstance(self.response, CreateMessageResultWithTools):
+ return self.response.stopReason == "toolUse"
+ return False
+
+ @property
+ def is_text(self) -> bool:
+ """True if the LLM returned a text response (not tool use)."""
+ return not self.is_tool_use
+
+ @property
+ def text(self) -> str | None:
+ """Extract text from the response, if available."""
+ content = self.response.content
+ if isinstance(content, list):
+ for block in content:
+ if hasattr(block, "text"):
+ return block.text # type: ignore[return-value]
+ return None
+ elif hasattr(content, "text"):
+ return content.text # type: ignore[return-value]
+ return None
+
+ @property
+ def tool_calls(self) -> list[ToolUseContent]:
+ """Get the list of tool calls from the response."""
+ content = self.response.content
+ if isinstance(content, list):
+ return [c for c in content if isinstance(c, ToolUseContent)]
+ elif isinstance(content, ToolUseContent):
+ return [content]
+ return []
+
+
+def _parse_model_preferences(
+ model_preferences: ModelPreferences | str | list[str] | None,
+) -> ModelPreferences | None:
+ """Convert model preferences to ModelPreferences object."""
+ if model_preferences is None:
+ return None
+ elif isinstance(model_preferences, ModelPreferences):
+ return model_preferences
+ elif isinstance(model_preferences, str):
+ return ModelPreferences(hints=[ModelHint(name=model_preferences)])
+ elif isinstance(model_preferences, list):
+ if not all(isinstance(h, str) for h in model_preferences):
+ raise ValueError("All elements of model_preferences list must be strings.")
+ return ModelPreferences(hints=[ModelHint(name=h) for h in model_preferences])
+ else:
+ raise ValueError(
+ "model_preferences must be one of: ModelPreferences, str, list[str], or None."
+ )
+
+
+# --- Standalone functions for sample_step() ---
+
+
+def determine_handler_mode(context: Context, needs_tools: bool) -> bool:
+ """Determine whether to use fallback handler or client for sampling.
+
+ Args:
+ context: The MCP context.
+ needs_tools: Whether the sampling request requires tool support.
+
+ Returns:
+ True if fallback handler should be used, False to use client.
+
+ Raises:
+ ValueError: If client lacks required capability and no fallback configured.
+ """
+ fastmcp = context.fastmcp
+ session = context.session
+
+ # Check what capabilities the client has
+ has_sampling = session.check_client_capability(
+ capability=ClientCapabilities(sampling=SamplingCapability())
+ )
+ has_tools_capability = session.check_client_capability(
+ capability=ClientCapabilities(
+ sampling=SamplingCapability(tools=SamplingToolsCapability())
+ )
+ )
+
+ if fastmcp.sampling_handler_behavior == "always":
+ if fastmcp.sampling_handler is None:
+ raise ValueError(
+ "sampling_handler_behavior is 'always' but no handler configured"
+ )
+ return True
+ elif fastmcp.sampling_handler_behavior == "fallback":
+ client_sufficient = has_sampling and (not needs_tools or has_tools_capability)
+ if not client_sufficient:
+ if fastmcp.sampling_handler is None:
+ if needs_tools and has_sampling and not has_tools_capability:
+ raise ValueError(
+ "Client does not support sampling with tools. "
+ "The client must advertise the sampling.tools capability."
+ )
+ raise ValueError("Client does not support sampling")
+ return True
+ elif needs_tools and not has_tools_capability:
+ raise ValueError(
+ "Client does not support sampling with tools. "
+ "The client must advertise the sampling.tools capability."
+ )
+
+ return False
+
+
+async def call_sampling_handler(
+ context: Context,
+ messages: list[SamplingMessage],
+ *,
+ system_prompt: str | None,
+ temperature: float | None,
+ max_tokens: int,
+ model_preferences: ModelPreferences | str | list[str] | None,
+ sdk_tools: list[SDKTool] | None,
+ tool_choice: ToolChoice | None,
+) -> CreateMessageResult | CreateMessageResultWithTools:
+ """Make LLM call using the fallback handler."""
+ assert context.fastmcp.sampling_handler is not None
+ assert context.request_context is not None
+
+ result = context.fastmcp.sampling_handler(
+ messages,
+ SamplingParams(
+ systemPrompt=system_prompt,
+ messages=messages,
+ temperature=temperature,
+ maxTokens=max_tokens,
+ modelPreferences=_parse_model_preferences(model_preferences),
+ tools=sdk_tools,
+ toolChoice=tool_choice,
+ ),
+ context.request_context,
+ )
+
+ if inspect.isawaitable(result):
+ result = await result
+
+ # Convert string to CreateMessageResult
+ if isinstance(result, str):
+ return CreateMessageResult(
+ role="assistant",
+ content=TextContent(type="text", text=result),
+ model="unknown",
+ stopReason="endTurn",
+ )
+
+ return result
+
+
+async def call_client(
+ context: Context,
+ messages: list[SamplingMessage],
+ *,
+ system_prompt: str | None,
+ temperature: float | None,
+ max_tokens: int,
+ model_preferences: ModelPreferences | str | list[str] | None,
+ sdk_tools: list[SDKTool] | None,
+ tool_choice: ToolChoice | None,
+) -> CreateMessageResult | CreateMessageResultWithTools:
+ """Make LLM call using the client."""
+ return await context.session.create_message(
+ messages=messages,
+ system_prompt=system_prompt,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ model_preferences=_parse_model_preferences(model_preferences),
+ tools=sdk_tools,
+ tool_choice=tool_choice,
+ related_request_id=context.request_id,
+ )
+
+
+async def execute_tools(
+ tool_calls: list[ToolUseContent],
+ tool_map: dict[str, SamplingTool],
+) -> list[ToolResultContent]:
+ """Execute tool calls and return results.
+
+ Args:
+ tool_calls: List of tool use requests from the LLM.
+ tool_map: Mapping from tool name to SamplingTool.
+
+ Returns:
+ List of tool result content blocks.
+ """
+ tool_results: list[ToolResultContent] = []
+
+ for tool_use in tool_calls:
+ tool = tool_map.get(tool_use.name)
+ if tool is None:
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[
+ TextContent(
+ type="text",
+ text=f"Error: Unknown tool '{tool_use.name}'",
+ )
+ ],
+ isError=True,
+ )
+ )
+ else:
+ try:
+ result_value = await tool.run(tool_use.input)
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[TextContent(type="text", text=str(result_value))],
+ )
+ )
+ except Exception as e:
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[TextContent(type="text", text=f"Error: {e}")],
+ isError=True,
+ )
+ )
+
+ return tool_results
diff --git a/tests/client/test_sampling.py b/tests/client/test_sampling.py
index 65e2b834f4..23c9afa43b 100644
--- a/tests/client/test_sampling.py
+++ b/tests/client/test_sampling.py
@@ -204,14 +204,15 @@ def sampling_handler(
with pytest.raises(ToolError, match="sampling.tools capability"):
await client.call_tool("sample_with_tool", {})
- async def test_sampling_with_tools_fallback_handler_must_return_correct_type(self):
- """Test that fallback handler must return CreateMessageResultWithTools when tools provided."""
- from fastmcp.exceptions import ToolError
+ async def test_sampling_with_tools_fallback_handler_can_return_string(self):
+ """Test that fallback handler can return a string even when tools are provided.
- # This handler returns a string, which is invalid when tools are provided
- invalid_handler = AsyncMock(return_value="Fallback response")
+ The LLM might choose not to use any tools and just return a text response.
+ """
+ # This handler returns a string - valid even when tools are provided
+ simple_handler = AsyncMock(return_value="Direct response without tools")
- mcp = FastMCP(sampling_handler=invalid_handler)
+ mcp = FastMCP(sampling_handler=simple_handler)
def search(query: str) -> str:
"""Search the web."""
@@ -223,15 +224,14 @@ async def sample_with_tool(context: Context) -> str:
messages="Search for Python tutorials",
tools=[search],
)
- return str(result)
+ return result.text or "no text"
# Client without sampling handler - will use server's fallback
async with Client(mcp) as client:
- # Handler returns string but tools were provided - should error
- with pytest.raises(
- ToolError, match="must return CreateMessageResultWithTools"
- ):
- await client.call_tool("sample_with_tool", {})
+ result = await client.call_tool("sample_with_tool", {})
+
+ # Handler returned string directly, which is treated as final text response
+ assert result.data == "Direct response without tools"
def test_sampling_tool_schema(self):
"""Test that SamplingTool generates correct schema."""
@@ -406,69 +406,6 @@ async def multi_tool(context: Context) -> str:
assert executed_tools == ["tool_a(5)", "tool_b(3)"]
assert result.data == "Done!"
- async def test_automatic_tool_loop_max_iterations(self):
- """Test that max_iterations prevents infinite loops.
-
- On the last iteration, tool_choice is set to 'none' to force text response.
- """
- from mcp.types import CreateMessageResultWithTools, ToolUseContent
-
- call_count = 0
- received_tool_choices: list = []
-
- def looping_tool() -> str:
- """A tool that always gets called again."""
- return "keep going"
-
- def sampling_handler(
- messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
- ) -> CreateMessageResultWithTools:
- nonlocal call_count
- call_count += 1
- received_tool_choices.append(params.toolChoice)
-
- # On last iteration (when tool_choice=none), return text
- if params.toolChoice and params.toolChoice.mode == "none":
- return CreateMessageResultWithTools(
- role="assistant",
- content=[TextContent(type="text", text="Forced to stop")],
- model="test-model",
- stopReason="endTurn",
- )
-
- # Otherwise keep returning tool use
- return CreateMessageResultWithTools(
- role="assistant",
- content=[
- ToolUseContent(
- type="tool_use",
- id=f"call_{call_count}",
- name="looping_tool",
- input={},
- )
- ],
- model="test-model",
- stopReason="toolUse",
- )
-
- mcp = FastMCP(sampling_handler=sampling_handler)
-
- @mcp.tool
- async def infinite_loop(context: Context) -> str:
- result = await context.sample(
- messages="Start", tools=[looping_tool], max_iterations=3
- )
- return result.text or "no text"
-
- async with Client(mcp) as client:
- result = await client.call_tool("infinite_loop", {})
-
- # Should complete after 3 iterations with forced text response
- assert call_count == 3
- assert result.data == "Forced to stop"
- # Last call should have tool_choice=none
- assert received_tool_choices[-1].mode == "none"
-
async def test_automatic_tool_loop_handles_unknown_tool(self):
"""Test that unknown tool names result in error being passed to LLM."""
from mcp.types import (
@@ -616,64 +553,6 @@ async def test_exception(context: Context) -> str:
assert "Tool failed intentionally" in error_text
assert result.data == "Handled error"
- async def test_max_iterations_one_for_manual_loop(self):
- """Test that max_iterations=1 forces text on first call for manual loop building.
-
- With max_iterations=1, tool_choice is set to 'none' on the first (and only) call,
- forcing a text response so users can build their own loop using history.
- """
- from mcp.types import CreateMessageResultWithTools, ToolUseContent
-
- received_tool_choices: list = []
-
- def my_tool() -> str:
- """A tool."""
- return "result"
-
- def sampling_handler(
- messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
- ) -> CreateMessageResultWithTools:
- received_tool_choices.append(params.toolChoice)
-
- # With tool_choice=none, return text response
- if params.toolChoice and params.toolChoice.mode == "none":
- return CreateMessageResultWithTools(
- role="assistant",
- content=[TextContent(type="text", text="Forced text response")],
- model="test-model",
- stopReason="endTurn",
- )
-
- # Otherwise return tool use (shouldn't happen with max_iterations=1)
- return CreateMessageResultWithTools(
- role="assistant",
- content=[
- ToolUseContent(
- type="tool_use", id="call_1", name="my_tool", input={}
- )
- ],
- model="test-model",
- stopReason="toolUse",
- )
-
- mcp = FastMCP(sampling_handler=sampling_handler)
-
- @mcp.tool
- async def single_iteration(context: Context) -> str:
- result = await context.sample(
- messages="Test", tools=[my_tool], max_iterations=1
- )
- return result.text or "no text"
-
- async with Client(mcp) as client:
- result = await client.call_tool("single_iteration", {})
-
- # Should get forced text response
- assert result.data == "Forced text response"
- # First and only call should have tool_choice=none
- assert len(received_tool_choices) == 1
- assert received_tool_choices[0].mode == "none"
-
class TestSamplingResultType:
"""Tests for result_type parameter (structured output)."""
@@ -921,3 +800,190 @@ async def check_result(context: Context) -> str:
result = await client.call_tool("check_result", {})
assert result.data == "ok"
+
+
+class TestSampleStep:
+ """Tests for ctx.sample_step() - single LLM call with manual control."""
+
+ async def test_sample_step_basic(self):
+ """Test basic sample_step returns text response."""
+ from mcp.types import CreateMessageResultWithTools
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="Hello from step")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def test_step(context: Context) -> str:
+ step = await context.sample_step(messages="Hi")
+ assert step.is_text
+ assert not step.is_tool_use
+ assert step.text == "Hello from step"
+ return step.text or ""
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("test_step", {})
+
+ assert result.data == "Hello from step"
+
+ async def test_sample_step_with_tool_execution(self):
+ """Test sample_step executes tools by default."""
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+
+ call_count = 0
+
+ def my_tool(x: int) -> str:
+ """A test tool."""
+ return f"result:{x}"
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ nonlocal call_count
+ call_count += 1
+
+ if call_count == 1:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="my_tool",
+ input={"x": 42},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+ else:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[TextContent(type="text", text="Done")],
+ model="test-model",
+ stopReason="endTurn",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def test_step(context: Context) -> str:
+ messages: str | list[SamplingMessage] = "Run tool"
+
+ while True:
+ step = await context.sample_step(messages=messages, tools=[my_tool])
+
+ if step.is_text:
+ return step.text or ""
+
+ # History should include tool results when execute_tools=True
+ messages = step.history
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("test_step", {})
+
+ assert result.data == "Done"
+ assert call_count == 2
+
+ async def test_sample_step_execute_tools_false(self):
+ """Test sample_step with execute_tools=False doesn't execute tools."""
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+
+ tool_executed = False
+
+ def my_tool() -> str:
+ """A test tool."""
+ nonlocal tool_executed
+ tool_executed = True
+ return "executed"
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="my_tool",
+ input={},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ @mcp.tool
+ async def test_step(context: Context) -> str:
+ step = await context.sample_step(
+ messages="Run tool",
+ tools=[my_tool],
+ execute_tools=False,
+ )
+ assert step.is_tool_use
+ assert len(step.tool_calls) == 1
+ assert step.tool_calls[0].name == "my_tool"
+ # History should include assistant message but no tool results
+ assert len(step.history) == 2 # user + assistant
+ return "ok"
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("test_step", {})
+
+ assert result.data == "ok"
+ assert not tool_executed # Tool should not have been executed
+
+ async def test_sample_step_history_includes_assistant_message(self):
+ """Test that history includes assistant message when execute_tools=False."""
+ from mcp.types import CreateMessageResultWithTools, ToolUseContent
+
+ def sampling_handler(
+ messages: list[SamplingMessage], params: SamplingParams, ctx: RequestContext
+ ) -> CreateMessageResultWithTools:
+ return CreateMessageResultWithTools(
+ role="assistant",
+ content=[
+ ToolUseContent(
+ type="tool_use",
+ id="call_1",
+ name="my_tool",
+ input={"query": "test"},
+ )
+ ],
+ model="test-model",
+ stopReason="toolUse",
+ )
+
+ mcp = FastMCP(sampling_handler=sampling_handler)
+
+ def my_tool(query: str) -> str:
+ return f"result for {query}"
+
+ @mcp.tool
+ async def test_step(context: Context) -> str:
+ step = await context.sample_step(
+ messages="Search",
+ tools=[my_tool],
+ execute_tools=False,
+ )
+ # History should have: user message + assistant message
+ assert len(step.history) == 2
+ assert step.history[0].role == "user"
+ assert step.history[1].role == "assistant"
+ return "ok"
+
+ async with Client(mcp) as client:
+ result = await client.call_tool("test_step", {})
+
+ assert result.data == "ok"
diff --git a/tests/server/proxy/test_proxy_client.py b/tests/server/proxy/test_proxy_client.py
index 4f68f11577..c15374a4aa 100644
--- a/tests/server/proxy/test_proxy_client.py
+++ b/tests/server/proxy/test_proxy_client.py
@@ -1,5 +1,4 @@
from dataclasses import dataclass
-from typing import cast
import pytest
from anyio import create_task_group
@@ -40,12 +39,11 @@ async def sampling(
result = await context.sample(
"Hello, world!",
system_prompt="You love FastMCP",
- include_context="thisServer",
temperature=0.5,
max_tokens=100,
model_preferences="gpt-4o",
)
- return cast(TextContent, result).text
+ return result.text or ""
@dataclass
class Person:
diff --git a/uv.lock b/uv.lock
index f0a955630c..342cb8a5fb 100644
--- a/uv.lock
+++ b/uv.lock
@@ -723,7 +723,7 @@ requires-dist = [
{ name = "exceptiongroup", specifier = ">=1.2.2" },
{ name = "httpx", specifier = ">=0.28.1" },
{ name = "jsonschema-path", specifier = ">=0.3.4" },
- { name = "mcp", specifier = ">=1.23.3" },
+ { name = "mcp", specifier = ">=1.24.0" },
{ name = "openai", marker = "extra == 'openai'", specifier = ">=1.102.0" },
{ name = "openapi-pydantic", specifier = ">=0.5.1" },
{ name = "platformdirs", specifier = ">=4.0.0" },
@@ -1237,7 +1237,7 @@ wheels = [
[[package]]
name = "mcp"
-version = "1.23.3"
+version = "1.24.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "anyio" },
@@ -1255,9 +1255,9 @@ dependencies = [
{ name = "typing-inspection" },
{ name = "uvicorn", marker = "sys_platform != 'emscripten'" },
]
-sdist = { url = "https://files.pythonhosted.org/packages/a7/a4/d06a303f45997e266f2c228081abe299bbcba216cb806128e2e49095d25f/mcp-1.23.3.tar.gz", hash = "sha256:b3b0da2cc949950ce1259c7bfc1b081905a51916fcd7c8182125b85e70825201", size = 600697, upload-time = "2025-12-09T16:04:37.351Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/db9ae5ab1fcdd9cd2bcc7ca3b7361b712e30590b64d5151a31563af8f82d/mcp-1.24.0.tar.gz", hash = "sha256:aeaad134664ce56f2721d1abf300666a1e8348563f4d3baff361c3b652448efc", size = 604375, upload-time = "2025-12-12T14:19:38.205Z" }
wheels = [
- { url = "https://files.pythonhosted.org/packages/32/c6/13c1a26b47b3f3a3b480783001ada4268917c9f42d78a079c336da2e75e5/mcp-1.23.3-py3-none-any.whl", hash = "sha256:32768af4b46a1b4f7df34e2bfdf5c6011e7b63d7f1b0e321d0fdef4cd6082031", size = 231570, upload-time = "2025-12-09T16:04:35.56Z" },
+ { url = "https://files.pythonhosted.org/packages/61/0d/5cf14e177c8ae655a2fd9324a6ef657ca4cafd3fc2201c87716055e29641/mcp-1.24.0-py3-none-any.whl", hash = "sha256:db130e103cc50ddc3dffc928382f33ba3eaef0b711f7a87c05e7ded65b1ca062", size = 232896, upload-time = "2025-12-12T14:19:36.14Z" },
]
[[package]]
From 5e6c790f893a3fef6ba25b3ddd391d58ea1375e1 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sat, 13 Dec 2025 22:28:38 -0500
Subject: [PATCH 08/15] Address CodeRabbit nitpicks
---
docs/servers/sampling.mdx | 2 +-
examples/advanced_sampling/tool_use.py | 6 +++---
src/fastmcp/server/sampling/run.py | 1 +
3 files changed, 5 insertions(+), 4 deletions(-)
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index 5bee7ee341..e9d6db6209 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -153,7 +153,7 @@ Use model preferences when different tasks benefit from different model characte
For requests that need conversational context, construct a list of `SamplingMessage` objects representing the conversation history. Each message has a `role` ("user" or "assistant") and `content`.
```python
-from fastmcp.client.sampling import SamplingMessage
+from mcp.types import SamplingMessage
@mcp.tool
async def contextual_analysis(query: str, data: str, ctx: Context) -> str:
diff --git a/examples/advanced_sampling/tool_use.py b/examples/advanced_sampling/tool_use.py
index 9de42e8590..bb38246988 100644
--- a/examples/advanced_sampling/tool_use.py
+++ b/examples/advanced_sampling/tool_use.py
@@ -15,7 +15,7 @@
import asyncio
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
from fastmcp import Client, Context, FastMCP
from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
@@ -45,7 +45,7 @@ def get_word_count(text: str) -> str:
class ResearchReport(BaseModel):
summary: str
sources_used: list[str]
- confidence: float
+ confidence: float = Field(ge=0.0, le=1.0)
# Create the MCP server
@@ -77,7 +77,7 @@ async def main():
print("Research Report:")
print(f" Summary: {result.data['summary']}")
print(f" Sources: {', '.join(result.data['sources_used'])}")
- print(f" Confidence: {result.data['confidence']}%")
+ print(f" Confidence: {result.data['confidence'] * 100:.0f}%")
if __name__ == "__main__":
diff --git a/src/fastmcp/server/sampling/run.py b/src/fastmcp/server/sampling/run.py
index a2b206777c..5066042e5e 100644
--- a/src/fastmcp/server/sampling/run.py
+++ b/src/fastmcp/server/sampling/run.py
@@ -278,6 +278,7 @@ async def execute_tools(
)
)
except Exception as e:
+ # Catch all exceptions to report errors to the LLM for recovery
tool_results.append(
ToolResultContent(
type="tool_result",
From 1ef5319f24eb8764de95c10cfca0efdcf7cebaa4 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sun, 14 Dec 2025 09:16:34 -0500
Subject: [PATCH 09/15] Address CodeRabbit review feedback for sampling tools
- Fix temperature=0.0 being dropped due to falsy evaluation
- Add ToolChoice.name support for forcing specific tools
- Replace assert statements with explicit RuntimeError checks
- Add mask_error_details parameter to sample()/sample_step() with ToolError escape hatch
- Fix hasattr patterns with proper isinstance checks
- Document mask_error_details and add OpenAI prerequisites to docs
---
docs/servers/sampling.mdx | 46 +++++++++++++++++
.../experimental/sampling/handlers/openai.py | 13 +++--
src/fastmcp/server/context.py | 29 +++++++++--
src/fastmcp/server/sampling/run.py | 50 +++++++++++++++----
4 files changed, 121 insertions(+), 17 deletions(-)
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index e9d6db6209..d16a1cff31 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -49,6 +49,10 @@ By default, sampling requests are routed to the client's LLM. You can also confi
A Pydantic model for validated structured output.
+
+ If True, mask detailed error messages from tool execution. When None (default), uses the global `settings.mask_error_details` value. Tools can raise `ToolError` to bypass masking and provide specific error messages to the LLM.
+
+
@@ -76,6 +80,10 @@ By default, sampling requests are routed to the client's LLM. You can also confi
If True, execute tool calls and append results to history. If False, return immediately with tool calls available for manual execution.
+
+ If True, mask detailed error messages from tool execution. When None (default), uses the global `settings.mask_error_details` value. Tools can raise `ToolError` to bypass masking.
+
+
@@ -247,6 +255,34 @@ async def research(question: str, ctx: Context) -> str:
The LLM sees each function's signature and docstring, using this information to decide when and how to call them. Tool errors are caught and sent back to the LLM, allowing it to recover gracefully. An internal safety limit prevents infinite loops.
+### Tool Error Handling
+
+By default, when a sampling tool raises an exception, the error message (including details) is sent back to the LLM so it can attempt recovery. To prevent sensitive information from leaking to the LLM, use the `mask_error_details` parameter:
+
+```python
+result = await ctx.sample(
+ messages=question,
+ tools=[search],
+ mask_error_details=True, # Generic error messages only
+)
+```
+
+When `mask_error_details=True`, tool errors become generic messages like `"Error executing tool 'search'"` instead of exposing stack traces or internal details.
+
+To intentionally provide specific error messages to the LLM regardless of masking, raise `ToolError`:
+
+```python
+from fastmcp.exceptions import ToolError
+
+def search(query: str) -> str:
+ """Search for information."""
+ if not query.strip():
+ raise ToolError("Search query cannot be empty")
+ return f"Results for: {query}"
+```
+
+`ToolError` messages always pass through to the LLM, making it the escape hatch for errors you want the LLM to see and handle.
+
For custom names or descriptions, use `SamplingTool.from_function()`:
```python
@@ -407,6 +443,16 @@ Client support for sampling is optional—some clients may not implement it. To
FastMCP provides an OpenAI-compatible handler that works with OpenAI's API and compatible providers. It supports the full sampling API including tools, automatically converting your Python functions to OpenAI's function calling format.
+
+ The OpenAI handler requires the `openai` package. Install it with:
+ ```bash
+ pip install fastmcp[openai]
+ # or
+ pip install openai
+ ```
+ You'll also need to set the `OPENAI_API_KEY` environment variable or pass it directly to the client.
+
+
```python
import os
from openai import OpenAI
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index f481907d9c..23acde3f5f 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -78,9 +78,11 @@ async def __call__(
response = self.client.chat.completions.create(
model=model,
messages=openai_messages,
- temperature=params.temperature or NOT_GIVEN,
+ temperature=(
+ params.temperature if params.temperature is not None else NOT_GIVEN
+ ),
max_tokens=params.maxTokens,
- stop=params.stopSequences or NOT_GIVEN,
+ stop=params.stopSequences if params.stopSequences else NOT_GIVEN,
tools=openai_tools,
tool_choice=openai_tool_choice,
)
@@ -328,6 +330,12 @@ def _convert_tool_choice_to_openai(
tool_choice: ToolChoice,
) -> ChatCompletionToolChoiceOptionParam:
"""Convert MCP tool_choice to OpenAI format."""
+ # If a specific tool is requested via name (may be in extra_data)
+ tool_name = getattr(tool_choice, "name", None)
+ if tool_name:
+ return {"type": "function", "function": {"name": tool_name}} # type: ignore[return-value]
+
+ # Mode-based selection
if tool_choice.mode == "auto":
return "auto"
elif tool_choice.mode == "required":
@@ -335,7 +343,6 @@ def _convert_tool_choice_to_openai(
elif tool_choice.mode == "none":
return "none"
else:
- # Unknown mode, default to auto
return "auto"
@staticmethod
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index 777c0bff44..76e70681ef 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -37,6 +37,7 @@
from starlette.requests import Request
from typing_extensions import TypeVar
+from fastmcp import settings
from fastmcp.server.elicitation import (
AcceptedElicitation,
CancelledElicitation,
@@ -552,6 +553,7 @@ async def sample_step(
tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
tool_choice: ToolChoiceOption | str | None = None,
execute_tools: bool = True,
+ mask_error_details: bool | None = None,
) -> SampleStep:
"""
Make a single LLM sampling call.
@@ -572,6 +574,9 @@ async def sample_step(
execute_tools: If True (default), execute tool calls and append results
to history. If False, return immediately with tool_calls available
in the step for manual execution.
+ mask_error_details: If True, mask detailed error messages from tool
+ execution. When None (default), uses the global settings value.
+ Tools can raise ToolError to bypass masking.
Returns:
SampleStep containing:
@@ -665,7 +670,14 @@ async def sample_step(
# Execute tools and add results to history
step_tool_calls = _extract_tool_calls(response)
if step_tool_calls:
- tool_results = await run_sampling_tools(step_tool_calls, tool_map)
+ effective_mask = (
+ mask_error_details
+ if mask_error_details is not None
+ else settings.mask_error_details
+ )
+ tool_results = await run_sampling_tools(
+ step_tool_calls, tool_map, mask_error_details=effective_mask
+ )
if tool_results:
current_messages.append(
@@ -688,6 +700,7 @@ async def sample(
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
result_type: type[ResultT],
+ mask_error_details: bool | None = None,
) -> SamplingResult[ResultT]:
"""Overload: With result_type, returns SamplingResult[ResultT]."""
@@ -702,6 +715,7 @@ async def sample(
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
result_type: None = None,
+ mask_error_details: bool | None = None,
) -> SamplingResult[str]:
"""Overload: Without result_type, returns SamplingResult[str]."""
@@ -715,6 +729,7 @@ async def sample(
model_preferences: ModelPreferences | str | list[str] | None = None,
tools: Sequence[SamplingTool | Callable[..., Any]] | None = None,
result_type: type[ResultT] | None = None,
+ mask_error_details: bool | None = None,
) -> SamplingResult[ResultT] | SamplingResult[str]:
"""
Send a sampling request to the client and await the response.
@@ -742,6 +757,9 @@ async def sample(
result_type: Optional type for structured output. When specified,
a synthetic `final_response` tool is created and the LLM's
response is validated against this type.
+ mask_error_details: If True, mask detailed error messages from tool
+ execution. When None (default), uses the global settings value.
+ Tools can raise ToolError to bypass masking.
Returns:
SamplingResult[T] containing:
@@ -779,6 +797,7 @@ async def sample(
model_preferences=model_preferences,
tools=sampling_tools,
tool_choice=tool_choice,
+ mask_error_details=mask_error_details,
)
# Check for final_response tool call for structured output
@@ -1066,11 +1085,11 @@ def _extract_text_from_content(
"""
if isinstance(content, list):
for block in content:
- if hasattr(block, "text"):
- return block.text # type: ignore[return-value]
+ if isinstance(block, TextContent):
+ return block.text
return None
- elif hasattr(content, "text"):
- return content.text # type: ignore[return-value]
+ elif isinstance(content, TextContent):
+ return content.text
return None
diff --git a/src/fastmcp/server/sampling/run.py b/src/fastmcp/server/sampling/run.py
index 5066042e5e..d3ee8aca18 100644
--- a/src/fastmcp/server/sampling/run.py
+++ b/src/fastmcp/server/sampling/run.py
@@ -24,7 +24,11 @@
from mcp.types import Tool as SDKTool
from typing_extensions import TypeVar
+from fastmcp.exceptions import ToolError
from fastmcp.server.sampling.sampling_tool import SamplingTool
+from fastmcp.utilities.logging import get_logger
+
+logger = get_logger(__name__)
if TYPE_CHECKING:
from fastmcp.server.context import Context
@@ -75,11 +79,11 @@ def text(self) -> str | None:
content = self.response.content
if isinstance(content, list):
for block in content:
- if hasattr(block, "text"):
- return block.text # type: ignore[return-value]
+ if isinstance(block, TextContent):
+ return block.text
return None
- elif hasattr(content, "text"):
- return content.text # type: ignore[return-value]
+ elif isinstance(content, TextContent):
+ return content.text
return None
@property
@@ -179,9 +183,16 @@ async def call_sampling_handler(
sdk_tools: list[SDKTool] | None,
tool_choice: ToolChoice | None,
) -> CreateMessageResult | CreateMessageResultWithTools:
- """Make LLM call using the fallback handler."""
- assert context.fastmcp.sampling_handler is not None
- assert context.request_context is not None
+ """Make LLM call using the fallback handler.
+
+ Note: This function expects the caller (sample_step) to have validated that
+ sampling_handler is set via determine_handler_mode(). The checks below are
+ safeguards against internal misuse.
+ """
+ if context.fastmcp.sampling_handler is None:
+ raise RuntimeError("sampling_handler is None")
+ if context.request_context is None:
+ raise RuntimeError("request_context is None")
result = context.fastmcp.sampling_handler(
messages,
@@ -239,12 +250,17 @@ async def call_client(
async def execute_tools(
tool_calls: list[ToolUseContent],
tool_map: dict[str, SamplingTool],
+ mask_error_details: bool = False,
) -> list[ToolResultContent]:
"""Execute tool calls and return results.
Args:
tool_calls: List of tool use requests from the LLM.
tool_map: Mapping from tool name to SamplingTool.
+ mask_error_details: If True, mask detailed error messages from tool execution.
+ When masked, only generic error messages are returned to the LLM.
+ Tools can explicitly raise ToolError to bypass masking when they want
+ to provide specific error messages to the LLM.
Returns:
List of tool result content blocks.
@@ -277,13 +293,29 @@ async def execute_tools(
content=[TextContent(type="text", text=str(result_value))],
)
)
+ except ToolError as e:
+ # ToolError is the escape hatch - always pass message through
+ logger.exception(f"Error calling sampling tool '{tool_use.name}'")
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=tool_use.id,
+ content=[TextContent(type="text", text=str(e))],
+ isError=True,
+ )
+ )
except Exception as e:
- # Catch all exceptions to report errors to the LLM for recovery
+ # Generic exceptions - mask based on setting
+ logger.exception(f"Error calling sampling tool '{tool_use.name}'")
+ if mask_error_details:
+ error_text = f"Error executing tool '{tool_use.name}'"
+ else:
+ error_text = f"Error executing tool '{tool_use.name}': {e}"
tool_results.append(
ToolResultContent(
type="tool_result",
toolUseId=tool_use.id,
- content=[TextContent(type="text", text=f"Error: {e}")],
+ content=[TextContent(type="text", text=error_text)],
isError=True,
)
)
From b079fe58119b378949d00e6147a6a0ef961bf71b Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sun, 14 Dec 2025 09:52:00 -0500
Subject: [PATCH 10/15] Address additional CodeRabbit review feedback
- Catch ValidationError specifically instead of bare Exception
- Update result_type docs to mention dataclasses and basic types
- Raise ValueError for unknown tool_choice modes
- Validate sampling_handler_behavior to catch typos
- Remove ToolChoice.name handling (not part of MCP spec)
- Validate tool_choice string in sample_step()
---
docs/servers/sampling.mdx | 6 +++---
src/fastmcp/experimental/sampling/handlers/openai.py | 8 +-------
src/fastmcp/server/context.py | 8 +++++++-
src/fastmcp/server/sampling/run.py | 5 +++++
4 files changed, 16 insertions(+), 11 deletions(-)
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index d16a1cff31..38c274cd01 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -46,7 +46,7 @@ By default, sampling requests are routed to the client's LLM. You can also confi
- A Pydantic model for validated structured output.
+ A type for validated structured output. Supports Pydantic models, dataclasses, and basic types like `int`, `list[str]`, or `dict[str, int]`.
@@ -184,7 +184,7 @@ The LLM receives the full conversation thread and responds with awareness of the
-When you need validated, typed data instead of free-form text, use the `result_type` parameter with a Pydantic model. FastMCP ensures the LLM returns data matching your schema, handling validation and retries automatically.
+When you need validated, typed data instead of free-form text, use the `result_type` parameter. FastMCP ensures the LLM returns data matching your type, handling validation and retries automatically. The `result_type` parameter accepts Pydantic models, dataclasses, and basic types like `int`, `list[str]`, or `dict[str, int]`.
```python
from pydantic import BaseModel
@@ -299,7 +299,7 @@ result = await ctx.sample(messages="...", tools=[tool])
### Combining with Structured Output
-Combine tools with `result_type` for agentic workflows that return validated, structured data. The LLM uses your tools to gather information, then returns a response matching your Pydantic schema.
+Combine tools with `result_type` for agentic workflows that return validated, structured data. The LLM uses your tools to gather information, then returns a response matching your type.
```python
result = await ctx.sample(
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index 23acde3f5f..e9f75593be 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -330,12 +330,6 @@ def _convert_tool_choice_to_openai(
tool_choice: ToolChoice,
) -> ChatCompletionToolChoiceOptionParam:
"""Convert MCP tool_choice to OpenAI format."""
- # If a specific tool is requested via name (may be in extra_data)
- tool_name = getattr(tool_choice, "name", None)
- if tool_name:
- return {"type": "function", "function": {"name": tool_name}} # type: ignore[return-value]
-
- # Mode-based selection
if tool_choice.mode == "auto":
return "auto"
elif tool_choice.mode == "required":
@@ -343,7 +337,7 @@ def _convert_tool_choice_to_openai(
elif tool_choice.mode == "none":
return "none"
else:
- return "auto"
+ raise ValueError(f"Unsupported tool_choice mode: {tool_choice.mode!r}")
@staticmethod
def _chat_completion_to_result_with_tools(
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index 76e70681ef..f6ea5fd8d5 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -33,6 +33,7 @@
from mcp.types import Prompt as SDKPrompt
from mcp.types import Resource as SDKResource
from mcp.types import Tool as SDKTool
+from pydantic import ValidationError
from pydantic.networks import AnyUrl
from starlette.requests import Request
from typing_extensions import TypeVar
@@ -617,6 +618,11 @@ async def sample_step(
# Build tool choice
effective_tool_choice: ToolChoice | None = None
if tool_choice is not None:
+ if tool_choice not in ("auto", "required", "none"):
+ raise ValueError(
+ f"Invalid tool_choice: {tool_choice!r}. "
+ "Must be 'auto', 'required', or 'none'."
+ )
effective_tool_choice = ToolChoice(
mode=cast(Literal["auto", "required", "none"], tool_choice)
)
@@ -818,7 +824,7 @@ async def sample(
result=validated_result,
history=step.history,
)
- except Exception as e:
+ except ValidationError as e:
# Validation failed - add error as tool result
step.history.append(
SamplingMessage(
diff --git a/src/fastmcp/server/sampling/run.py b/src/fastmcp/server/sampling/run.py
index d3ee8aca18..c937d2c39f 100644
--- a/src/fastmcp/server/sampling/run.py
+++ b/src/fastmcp/server/sampling/run.py
@@ -163,6 +163,11 @@ def determine_handler_mode(context: Context, needs_tools: bool) -> bool:
)
raise ValueError("Client does not support sampling")
return True
+ elif fastmcp.sampling_handler_behavior is not None:
+ raise ValueError(
+ f"Invalid sampling_handler_behavior: {fastmcp.sampling_handler_behavior!r}. "
+ "Must be 'always', 'fallback', or None."
+ )
elif needs_tools and not has_tools_capability:
raise ValueError(
"Client does not support sampling with tools. "
From 07df7c6b8bc62a9ff1f73e2bf588aecf5c63b4bb Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sun, 14 Dec 2025 12:58:07 -0500
Subject: [PATCH 11/15] Review fixes for sampling tools PR
- Remove internal functions from sampling __init__.py exports
- Remove fragile is_text property, use not is_tool_use instead
- Inline call_client into context.py, remove from run.py
- Fix SamplingMessage docs to use TextContent
- Handle result.text being None in doc examples
- Simplify client sampling docs to recommend OpenAISamplingHandler
- Add sampling_capabilities override documentation
- Raise iteration limit from 50 to 100
- Remove _parse_model_preferences duplication
- Use AsyncOpenAI in OpenAISamplingHandler
- Fix tool_choice docstring
---
docs/clients/sampling.mdx | 118 +++++-------------
docs/servers/sampling.mdx | 66 +++++-----
.../experimental/sampling/handlers/openai.py | 24 +++-
src/fastmcp/server/context.py | 78 +++---------
src/fastmcp/server/sampling/__init__.py | 13 +-
src/fastmcp/server/sampling/run.py | 29 -----
tests/client/test_sampling.py | 3 +-
7 files changed, 106 insertions(+), 225 deletions(-)
diff --git a/docs/clients/sampling.mdx b/docs/clients/sampling.mdx
index 11a9a1bd23..1f8d29a83b 100644
--- a/docs/clients/sampling.mdx
+++ b/docs/clients/sampling.mdx
@@ -111,7 +111,7 @@ The sampling handler receives three parameters:
- Optional list of tools the LLM can use during sampling. See [Handling Tool Requests](#handling-tool-requests).
+ Optional list of tools the LLM can use during sampling. See [Using the OpenAI Handler](#using-the-openai-handler).
@@ -163,110 +163,54 @@ client = Client(
If the client doesn't provide a sampling handler, servers can optionally configure a fallback handler. See [Server Sampling](/servers/sampling#sampling-fallback-handler) for details.
-
-When you provide a `sampling_handler`, FastMCP automatically advertises full sampling capabilities including tool support to the server.
-
-
-## Handling Tool Requests
-
-
-
-Servers may request sampling with tools, allowing the LLM to make tool calls during generation. When tools are provided in `params.tools`, your handler should return a `CreateMessageResultWithTools` object instead of a simple string.
+## Sampling Capabilities
-### Checking for Tools
+When you provide a `sampling_handler`, FastMCP automatically advertises full sampling capabilities to the server, including tool support. To disable tool support (for simpler handlers that don't support tools), pass `sampling_capabilities` explicitly:
```python
-from fastmcp import Client
-from fastmcp.client.sampling import SamplingMessage, SamplingParams, RequestContext
-from mcp.types import (
- CreateMessageResultWithTools,
- TextContent,
- ToolUseContent,
- ToolChoice,
-)
-
-async def sampling_handler_with_tools(
- messages: list[SamplingMessage],
- params: SamplingParams,
- context: RequestContext
-) -> str | CreateMessageResultWithTools:
- # Check if tools were provided
- if params.tools:
- # Call your LLM with tool support
- # Return CreateMessageResultWithTools with appropriate content
- return CreateMessageResultWithTools(
- role="assistant",
- content=[TextContent(type="text", text="I'll help you with that.")],
- model="gpt-4",
- stopReason="endTurn",
- )
-
- # Standard text response when no tools
- return "Generated response"
+from mcp.types import SamplingCapability
client = Client(
"my_mcp_server.py",
- sampling_handler=sampling_handler_with_tools,
+ sampling_handler=basic_handler,
+ sampling_capabilities=SamplingCapability(), # No tool support
)
```
-### Returning Tool Use
+## Using the OpenAI Handler
+
+
-When the LLM wants to call a tool, return a `CreateMessageResultWithTools` with `stopReason="toolUse"` and `ToolUseContent` in the content:
+For full-featured sampling with tool support, use the built-in OpenAI handler. It handles message conversion, tool calls, and response formatting automatically:
```python
-from mcp.types import CreateMessageResultWithTools, ToolUseContent
+from fastmcp import Client
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
-async def sampling_handler_with_tools(
- messages: list[SamplingMessage],
- params: SamplingParams,
- context: RequestContext
-) -> str | CreateMessageResultWithTools:
- if params.tools:
- # Your LLM decided to call a tool
- return CreateMessageResultWithTools(
- role="assistant",
- content=[
- ToolUseContent(
- type="toolUse",
- id="call_123",
- name="search",
- input={"query": "Python tutorials"},
- )
- ],
- model="gpt-4",
- stopReason="toolUse", # Indicates tool use
- )
-
- return "Response without tools"
+client = Client(
+ "my_mcp_server.py",
+ sampling_handler=OpenAISamplingHandler(default_model="gpt-4o"),
+)
```
-### Tool Choice
-
-The `params.toolChoice` field indicates how the server wants tools to be used:
-
-- **`auto`**: The LLM decides whether to use tools
-- **`required`**: The LLM must use at least one tool
-- **`none`**: The LLM should not use any tools
+The handler works with any OpenAI-compatible API by passing a custom client:
```python
-async def sampling_handler_with_tools(
- messages: list[SamplingMessage],
- params: SamplingParams,
- context: RequestContext
-) -> str | CreateMessageResultWithTools:
- if params.tools and params.toolChoice:
- if params.toolChoice.mode == "required":
- # Must return a tool call
- pass
- elif params.toolChoice.mode == "none":
- # Should not return tool calls even though tools are available
- pass
- # "auto" - let the LLM decide
-
- return "Response"
+from openai import AsyncOpenAI
+
+client = Client(
+ "my_mcp_server.py",
+ sampling_handler=OpenAISamplingHandler(
+ default_model="llama-3.1-70b",
+ client=AsyncOpenAI(base_url="http://localhost:8000/v1"),
+ ),
+)
```
Tool execution happens on the server side. The client's role is to pass tools to the LLM and return the LLM's response (which may include tool use requests). The server then executes the tools and may send follow-up sampling requests with tool results.
-
\ No newline at end of file
+
+
+
+To implement a custom sampling handler, see the [OpenAISamplingHandler source code](https://github.com/jlowin/fastmcp/blob/main/src/fastmcp/experimental/sampling/handlers/openai.py) as a reference.
+
\ No newline at end of file
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index 38c274cd01..345bcf07b7 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -90,7 +90,7 @@ By default, sampling requests are routed to the client's LLM. You can also confi
- `.response`: The raw LLM response
- `.history`: Messages including input, assistant response, and tool results
- - `.is_tool_use` / `.is_text`: Check what kind of response
+ - `.is_tool_use`: True if the LLM requested tool execution
- `.tool_calls`: List of tool calls (if any)
- `.text`: The text content (if any)
@@ -111,7 +111,7 @@ mcp = FastMCP()
async def summarize(content: str, ctx: Context) -> str:
"""Generate a summary of the provided content."""
result = await ctx.sample(f"Please summarize this:\n\n{content}")
- return result.text
+ return result.text or ""
```
The `SamplingResult` also provides `.result` (identical to `.text` for plain text responses) and `.history` containing the full message exchange—useful if you need to continue the conversation or debug the interaction.
@@ -151,31 +151,37 @@ async def technical_analysis(data: str, ctx: Context) -> str:
model_preferences=["claude-opus-4-5", "gpt-5-2"],
temperature=0.2,
)
- return result.text
+ return result.text or ""
```
Use model preferences when different tasks benefit from different model characteristics. Creative writing might prefer faster models with higher temperature, while complex analysis might benefit from larger reasoning-focused models.
### Multi-Turn Conversations
-For requests that need conversational context, construct a list of `SamplingMessage` objects representing the conversation history. Each message has a `role` ("user" or "assistant") and `content`.
+For requests that need conversational context, construct a list of `SamplingMessage` objects representing the conversation history. Each message has a `role` ("user" or "assistant") and `content` (a `TextContent` object).
```python
-from mcp.types import SamplingMessage
+from mcp.types import SamplingMessage, TextContent
@mcp.tool
async def contextual_analysis(query: str, data: str, ctx: Context) -> str:
"""Analyze data with conversational context."""
messages = [
- SamplingMessage(role="user", content=f"Here's my data: {data}"),
+ SamplingMessage(
+ role="user",
+ content=TextContent(type="text", text=f"Here's my data: {data}"),
+ ),
SamplingMessage(
role="assistant",
- content="I see the data. What would you like to know?"
+ content=TextContent(type="text", text="I see the data. What would you like to know?"),
+ ),
+ SamplingMessage(
+ role="user",
+ content=TextContent(type="text", text=query),
),
- SamplingMessage(role="user", content=query)
]
result = await ctx.sample(messages=messages)
- return result.text
+ return result.text or ""
```
The LLM receives the full conversation thread and responds with awareness of the preceding context.
@@ -250,7 +256,7 @@ async def research(question: str, ctx: Context) -> str:
messages=question,
tools=[search, get_time],
)
- return result.text
+ return result.text or ""
```
The LLM sees each function's signature and docstring, using this information to decide when and how to call them. Tool errors are caught and sent back to the LLM, allowing it to recover gracefully. An internal safety limit prevents infinite loops.
@@ -348,8 +354,8 @@ async def controlled_agent(question: str, ctx: Context) -> str:
for call in step.tool_calls:
print(f"Called tool: {call.name}")
- if step.is_text:
- return step.text
+ if not step.is_tool_use:
+ return step.text or ""
# Continue with updated history
messages = step.history
@@ -360,7 +366,6 @@ async def controlled_agent(question: str, ctx: Context) -> str:
Each `SampleStep` provides information about what the LLM returned:
- `step.is_tool_use` — True if the LLM requested tool calls
-- `step.is_text` — True if the LLM returned a text response
- `step.tool_calls` — List of tool calls requested (if any)
- `step.text` — The text content (if any)
- `step.history` — All messages exchanged so far
@@ -392,7 +397,7 @@ async def research(question: str, ctx: Context) -> str:
# Map tool names to functions
tools = {"search": search, "get_time": get_time}
- messages = [SamplingMessage(role="user", content=question)]
+ messages: list[SamplingMessage] = [question] # strings are converted automatically
while True:
step = await ctx.sample_step(
@@ -401,25 +406,24 @@ async def research(question: str, ctx: Context) -> str:
execute_tools=False,
)
- if step.is_text:
- return step.text
-
- if step.is_tool_use:
- tool_results = []
- for call in step.tool_calls:
- # Look up and execute the requested tool
- fn = tools[call.name]
- result = fn(**call.input)
- tool_results.append(
- ToolResultContent(
- type="tool_result",
- toolUseId=call.id,
- content=[TextContent(type="text", text=result)],
- )
+ if not step.is_tool_use:
+ return step.text or ""
+
+ # Execute tools and collect results
+ tool_results = []
+ for call in step.tool_calls:
+ fn = tools[call.name]
+ result = fn(**call.input)
+ tool_results.append(
+ ToolResultContent(
+ type="tool_result",
+ toolUseId=call.id,
+ content=[TextContent(type="text", text=result)],
)
+ )
- messages = list(step.history)
- messages.append(SamplingMessage(role="user", content=tool_results))
+ messages = list(step.history)
+ messages.append(SamplingMessage(role="user", content=tool_results))
```
#### Error Handling
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index e9f75593be..d4f74fb734 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -19,7 +19,7 @@
)
try:
- from openai import NOT_GIVEN, NotGiven, OpenAI
+ from openai import NOT_GIVEN, AsyncOpenAI, NotGiven, OpenAI
from openai.types.chat import (
ChatCompletion,
ChatCompletionAssistantMessageParam,
@@ -44,8 +44,24 @@
class OpenAISamplingHandler(BaseLLMSamplingHandler):
- def __init__(self, default_model: ChatModel, client: OpenAI | None = None):
- self.client: OpenAI = client or OpenAI()
+ def __init__(
+ self,
+ default_model: ChatModel,
+ client: AsyncOpenAI | OpenAI | None = None,
+ ):
+ # Accept either sync or async client, convert sync to async if needed
+ if client is None:
+ self.client: AsyncOpenAI = AsyncOpenAI()
+ elif isinstance(client, AsyncOpenAI):
+ self.client = client
+ else:
+ # Wrap sync client by creating async client with same config
+ self.client = AsyncOpenAI(
+ api_key=client.api_key,
+ base_url=str(client.base_url) if client.base_url else None,
+ organization=client.organization,
+ timeout=client.timeout,
+ )
self.default_model: ChatModel = default_model
@override
@@ -75,7 +91,7 @@ async def __call__(
if params.toolChoice:
openai_tool_choice = self._convert_tool_choice_to_openai(params.toolChoice)
- response = self.client.chat.completions.create(
+ response = await self.client.chat.completions.create(
model=model,
messages=openai_messages,
temperature=(
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index f6ea5fd8d5..cebedbff32 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -20,7 +20,6 @@
CreateMessageResult,
CreateMessageResultWithTools,
GetPromptResult,
- ModelHint,
ModelPreferences,
Root,
SamplingMessage,
@@ -46,15 +45,15 @@
handle_elicit_accept,
parse_elicit_response_type,
)
-from fastmcp.server.sampling import (
- SampleStep,
- SamplingResult,
- SamplingTool,
- call_client,
+from fastmcp.server.sampling import SampleStep, SamplingResult, SamplingTool
+from fastmcp.server.sampling.run import (
+ _parse_model_preferences,
call_sampling_handler,
determine_handler_mode,
)
-from fastmcp.server.sampling import execute_tools as run_sampling_tools
+from fastmcp.server.sampling.run import (
+ execute_tools as run_sampling_tools,
+)
from fastmcp.server.server import FastMCP
from fastmcp.utilities.json_schema import compress_schema
from fastmcp.utilities.logging import _clamp_logger, get_logger
@@ -571,7 +570,7 @@ async def sample_step(
max_tokens: Maximum tokens to generate. Defaults to 512.
model_preferences: Optional model preferences.
tools: Optional list of tools the LLM can use.
- tool_choice: Tool choice mode ("auto", "required", "none", or tool name).
+ tool_choice: Tool choice mode ("auto", "required", or "none").
execute_tools: If True (default), execute tool calls and append results
to history. If False, return immediately with tool_calls available
in the step for manual execution.
@@ -583,17 +582,17 @@ async def sample_step(
SampleStep containing:
- .response: The raw LLM response
- .history: Messages including input, assistant response, and tool results
- - .is_tool_use / .is_text: Check what kind of response
+ - .is_tool_use: True if the LLM requested tool execution
- .tool_calls: List of tool calls (if any)
- .text: The text content (if any)
Example:
- messages = [SamplingMessage(role="user", content="Research X")]
+ messages = "Research X"
while True:
step = await ctx.sample_step(messages, tools=[search])
- if step.is_text:
+ if not step.is_tool_use:
print(step.text)
break
@@ -643,15 +642,15 @@ async def sample_step(
tool_choice=effective_tool_choice,
)
else:
- response = await call_client(
- self,
- current_messages,
+ response = await self.session.create_message(
+ messages=current_messages,
system_prompt=system_prompt,
temperature=temperature,
max_tokens=effective_max_tokens,
- model_preferences=model_preferences,
- sdk_tools=sdk_tools,
+ model_preferences=_parse_model_preferences(model_preferences),
+ tools=sdk_tools,
tool_choice=effective_tool_choice,
+ related_request_id=self.request_id,
)
# Check if this is a tool use response
@@ -774,7 +773,7 @@ async def sample(
- .history: All messages exchanged during sampling
"""
# Safety limit to prevent infinite loops
- max_iterations = 50
+ max_iterations = 100
# Convert tools to SamplingTools
sampling_tools = _prepare_tools(tools)
@@ -848,8 +847,8 @@ async def sample(
)
)
- # If text response, we're done
- if step.is_text:
+ # If not a tool use response, we're done
+ if not step.is_tool_use:
return SamplingResult(
text=step.text,
result=cast(ResultT, step.text if step.text else ""),
@@ -987,47 +986,6 @@ async def _flush_notifications(self) -> None:
pass
-def _parse_model_preferences(
- model_preferences: ModelPreferences | str | list[str] | None,
-) -> ModelPreferences | None:
- """
- Validates and converts user input for model_preferences into a ModelPreferences object.
-
- Args:
- model_preferences (ModelPreferences | str | list[str] | None):
- The model preferences to use. Accepts:
- - ModelPreferences (returns as-is)
- - str (single model hint)
- - list[str] (multiple model hints)
- - None (no preferences)
-
- Returns:
- ModelPreferences | None: The parsed ModelPreferences object, or None if not provided.
-
- Raises:
- ValueError: If the input is not a supported type or contains invalid values.
- """
- if model_preferences is None:
- return None
- elif isinstance(model_preferences, ModelPreferences):
- return model_preferences
- elif isinstance(model_preferences, str):
- # Single model hint
- return ModelPreferences(hints=[ModelHint(name=model_preferences)])
- elif isinstance(model_preferences, list):
- # List of model hints (strings)
- if not all(isinstance(h, str) for h in model_preferences):
- raise ValueError(
- "All elements of model_preferences list must be"
- " strings (model name hints)."
- )
- return ModelPreferences(hints=[ModelHint(name=h) for h in model_preferences])
- else:
- raise ValueError(
- "model_preferences must be one of: ModelPreferences, str, list[str], or None."
- )
-
-
async def _log_to_server_and_client(
data: LogData,
session: ServerSession,
diff --git a/src/fastmcp/server/sampling/__init__.py b/src/fastmcp/server/sampling/__init__.py
index e6e05a29f6..afb903a9ed 100644
--- a/src/fastmcp/server/sampling/__init__.py
+++ b/src/fastmcp/server/sampling/__init__.py
@@ -1,14 +1,7 @@
"""Sampling module for FastMCP servers."""
from fastmcp.server.sampling.handler import ServerSamplingHandler
-from fastmcp.server.sampling.run import (
- SampleStep,
- SamplingResult,
- call_client,
- call_sampling_handler,
- determine_handler_mode,
- execute_tools,
-)
+from fastmcp.server.sampling.run import SampleStep, SamplingResult
from fastmcp.server.sampling.sampling_tool import SamplingTool
__all__ = [
@@ -16,8 +9,4 @@
"SamplingResult",
"SamplingTool",
"ServerSamplingHandler",
- "call_client",
- "call_sampling_handler",
- "determine_handler_mode",
- "execute_tools",
]
diff --git a/src/fastmcp/server/sampling/run.py b/src/fastmcp/server/sampling/run.py
index c937d2c39f..37fa8dbdb4 100644
--- a/src/fastmcp/server/sampling/run.py
+++ b/src/fastmcp/server/sampling/run.py
@@ -68,11 +68,6 @@ def is_tool_use(self) -> bool:
return self.response.stopReason == "toolUse"
return False
- @property
- def is_text(self) -> bool:
- """True if the LLM returned a text response (not tool use)."""
- return not self.is_tool_use
-
@property
def text(self) -> str | None:
"""Extract text from the response, if available."""
@@ -228,30 +223,6 @@ async def call_sampling_handler(
return result
-async def call_client(
- context: Context,
- messages: list[SamplingMessage],
- *,
- system_prompt: str | None,
- temperature: float | None,
- max_tokens: int,
- model_preferences: ModelPreferences | str | list[str] | None,
- sdk_tools: list[SDKTool] | None,
- tool_choice: ToolChoice | None,
-) -> CreateMessageResult | CreateMessageResultWithTools:
- """Make LLM call using the client."""
- return await context.session.create_message(
- messages=messages,
- system_prompt=system_prompt,
- temperature=temperature,
- max_tokens=max_tokens,
- model_preferences=_parse_model_preferences(model_preferences),
- tools=sdk_tools,
- tool_choice=tool_choice,
- related_request_id=context.request_id,
- )
-
-
async def execute_tools(
tool_calls: list[ToolUseContent],
tool_map: dict[str, SamplingTool],
diff --git a/tests/client/test_sampling.py b/tests/client/test_sampling.py
index 23c9afa43b..c6cd42fad9 100644
--- a/tests/client/test_sampling.py
+++ b/tests/client/test_sampling.py
@@ -824,7 +824,6 @@ def sampling_handler(
@mcp.tool
async def test_step(context: Context) -> str:
step = await context.sample_step(messages="Hi")
- assert step.is_text
assert not step.is_tool_use
assert step.text == "Hello from step"
return step.text or ""
@@ -881,7 +880,7 @@ async def test_step(context: Context) -> str:
while True:
step = await context.sample_step(messages=messages, tools=[my_tool])
- if step.is_text:
+ if not step.is_tool_use:
return step.text or ""
# History should include tool results when execute_tools=True
From 1bba6e004668da4538dd1078460d2fb756f79a11 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sun, 14 Dec 2025 13:03:01 -0500
Subject: [PATCH 12/15] Fix OpenAI handler tests to use AsyncOpenAI
---
.../experimental/sampling/handlers/openai.py | 18 +++---------------
.../sampling/test_openai_handler.py | 6 +++---
2 files changed, 6 insertions(+), 18 deletions(-)
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index d4f74fb734..a55ed2169d 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -19,7 +19,7 @@
)
try:
- from openai import NOT_GIVEN, AsyncOpenAI, NotGiven, OpenAI
+ from openai import NOT_GIVEN, AsyncOpenAI, NotGiven
from openai.types.chat import (
ChatCompletion,
ChatCompletionAssistantMessageParam,
@@ -47,21 +47,9 @@ class OpenAISamplingHandler(BaseLLMSamplingHandler):
def __init__(
self,
default_model: ChatModel,
- client: AsyncOpenAI | OpenAI | None = None,
+ client: AsyncOpenAI | None = None,
):
- # Accept either sync or async client, convert sync to async if needed
- if client is None:
- self.client: AsyncOpenAI = AsyncOpenAI()
- elif isinstance(client, AsyncOpenAI):
- self.client = client
- else:
- # Wrap sync client by creating async client with same config
- self.client = AsyncOpenAI(
- api_key=client.api_key,
- base_url=str(client.base_url) if client.base_url else None,
- organization=client.organization,
- timeout=client.timeout,
- )
+ self.client: AsyncOpenAI = client or AsyncOpenAI()
self.default_model: ChatModel = default_model
@override
diff --git a/tests/experimental/sampling/test_openai_handler.py b/tests/experimental/sampling/test_openai_handler.py
index 99f7003c35..0c5025cde8 100644
--- a/tests/experimental/sampling/test_openai_handler.py
+++ b/tests/experimental/sampling/test_openai_handler.py
@@ -8,7 +8,7 @@
SamplingMessage,
TextContent,
)
-from openai import OpenAI
+from openai import AsyncOpenAI
from openai.types.chat import (
ChatCompletion,
ChatCompletionAssistantMessageParam,
@@ -67,13 +67,13 @@ def test_convert_to_openai_messages_raises_on_non_text():
],
)
def test_select_model_from_preferences(prefs, expected):
- mock_client = MagicMock(spec=OpenAI)
+ mock_client = MagicMock(spec=AsyncOpenAI)
handler = OpenAISamplingHandler(default_model="fallback-model", client=mock_client) # type: ignore[arg-type]
assert handler._select_model_from_preferences(prefs) == expected
async def test_chat_completion_to_create_message_result():
- mock_client = MagicMock(spec=OpenAI)
+ mock_client = MagicMock(spec=AsyncOpenAI)
handler = OpenAISamplingHandler(default_model="fallback-model", client=mock_client) # type: ignore[arg-type]
mock_client.chat.completions.create.return_value = ChatCompletion(
id="123",
From 5629f2415273807497a4d06f814854c97a895118 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sun, 14 Dec 2025 13:22:31 -0500
Subject: [PATCH 13/15] Address remaining CodeRabbit review comments
- Fix message ordering in OpenAI handler: tool results now correctly
follow assistant message with tool_calls
- sample_step() now always includes assistant message in history
- Raise ValueError on JSON parse errors instead of silent {}
- Add has_sampling capability check when behavior is None
- Raise RuntimeError when structured output receives text response
- Wrap primitive result_type schemas in object wrapper
- Fix docs example using invalid SamplingMessage construction
- Add comprehensive client_sampling_test.py example
---
docs/servers/sampling.mdx | 2 +-
examples/advanced_sampling/README.md | 12 ++
.../advanced_sampling/client_sampling_test.py | 159 ++++++++++++++++++
.../experimental/sampling/handlers/openai.py | 19 ++-
src/fastmcp/server/context.py | 42 ++++-
src/fastmcp/server/sampling/run.py | 2 +
6 files changed, 223 insertions(+), 13 deletions(-)
create mode 100644 examples/advanced_sampling/client_sampling_test.py
diff --git a/docs/servers/sampling.mdx b/docs/servers/sampling.mdx
index 345bcf07b7..ae31b3aa61 100644
--- a/docs/servers/sampling.mdx
+++ b/docs/servers/sampling.mdx
@@ -340,7 +340,7 @@ from mcp.types import SamplingMessage
@mcp.tool
async def controlled_agent(question: str, ctx: Context) -> str:
"""Agent with manual loop control."""
- messages = [SamplingMessage(role="user", content=question)]
+ messages: list[str | SamplingMessage] = [question] # strings auto-convert
while True:
step = await ctx.sample_step(
diff --git a/examples/advanced_sampling/README.md b/examples/advanced_sampling/README.md
index 4ffa247c7f..12b01ae195 100644
--- a/examples/advanced_sampling/README.md
+++ b/examples/advanced_sampling/README.md
@@ -26,3 +26,15 @@ Gives the LLM tools to use during sampling, with automatic tool execution:
```bash
python examples/advanced_sampling/tool_use.py
```
+
+### Client Sampling Test (`client_sampling_test.py`)
+
+Comprehensive test of advanced sampling features:
+- Primitive `result_type` (`int`, `list[str]`) with automatic schema wrapping
+- `sample_step()` for fine-grained loop control
+- History tracking (verifies assistant messages are included)
+- Multi-step reasoning with tools
+
+```bash
+python examples/advanced_sampling/client_sampling_test.py
+```
diff --git a/examples/advanced_sampling/client_sampling_test.py b/examples/advanced_sampling/client_sampling_test.py
new file mode 100644
index 0000000000..102c5ba95e
--- /dev/null
+++ b/examples/advanced_sampling/client_sampling_test.py
@@ -0,0 +1,159 @@
+"""
+Client Sampling Test
+
+This example demonstrates advanced sampling features using a Client with an
+OpenAI handler. It tests:
+- Primitive result_type (int, list[str]) with automatic schema wrapping
+- The sample_step() method for fine-grained loop control
+- History tracking including assistant messages
+- Tool execution with manual control
+
+Prerequisites:
+ pip install fastmcp[openai]
+ export OPENAI_API_KEY=your-key
+
+Run:
+ python examples/advanced_sampling/client_sampling_test.py
+"""
+
+import asyncio
+
+from pydantic import BaseModel
+
+from fastmcp import Client, Context, FastMCP
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+
+# Create the MCP server
+mcp = FastMCP("Sampling Test Server")
+
+
+# --- Test 1: Primitive result_type (int) ---
+@mcp.tool
+async def count_vowels(text: str, ctx: Context) -> int:
+ """Count the number of vowels in the given text using LLM sampling."""
+ result = await ctx.sample(
+ messages=f"Count the number of vowels (a, e, i, o, u) in this text and return just the count as an integer:\n\n{text}",
+ system_prompt="You are a precise counting assistant. Return only the numeric count.",
+ result_type=int,
+ )
+ return result.result
+
+
+# --- Test 2: Primitive result_type (list[str]) ---
+@mcp.tool
+async def extract_keywords(text: str, ctx: Context) -> list[str]:
+ """Extract keywords from the given text."""
+ result = await ctx.sample(
+ messages=f"Extract the 3 most important keywords from this text:\n\n{text}",
+ system_prompt="You are a keyword extraction expert. Return a list of keywords.",
+ result_type=list[str],
+ )
+ return result.result
+
+
+# --- Test 3: sample_step() with history tracking ---
+@mcp.tool
+async def multi_step_reasoning(question: str, ctx: Context) -> str:
+ """Demonstrate multi-step reasoning with sample_step()."""
+
+ def think(thought: str) -> str:
+ """Record a thought in the reasoning chain."""
+ return f"Thought recorded: {thought}"
+
+ # First step: initial reasoning
+ messages: list[str] = [question]
+ step_count = 0
+ max_steps = 5
+
+ while step_count < max_steps:
+ step = await ctx.sample_step(
+ messages=messages,
+ tools=[think],
+ system_prompt="Think step by step. Use the think() tool to record your reasoning, then provide your final answer.",
+ )
+
+ step_count += 1
+
+ # History should always include the assistant's response
+ assert len(step.history) > len(messages), (
+ "History should grow with assistant message"
+ )
+
+ if not step.is_tool_use:
+ return f"Answer (after {step_count} steps): {step.text or ''}"
+
+ # Continue with updated history
+ messages = step.history
+
+ return "Max steps reached without final answer"
+
+
+# --- Test 4: Structured output with Pydantic model ---
+class AnalysisResult(BaseModel):
+ main_topic: str
+ sentiment: str
+ word_count: int
+
+
+@mcp.tool
+async def analyze_text(text: str, ctx: Context) -> dict:
+ """Analyze text and return structured results."""
+ result = await ctx.sample(
+ messages=f"Analyze this text:\n\n{text}",
+ system_prompt="Analyze the text and provide structured results.",
+ result_type=AnalysisResult,
+ )
+ return result.result.model_dump()
+
+
+async def main():
+ sampling_handler = OpenAISamplingHandler(default_model="gpt-4o-mini")
+
+ async with Client(mcp, sampling_handler=sampling_handler) as client:
+ print("=" * 60)
+ print("Test 1: Primitive result_type (int)")
+ print("=" * 60)
+ result = await client.call_tool(
+ "count_vowels",
+ {"text": "Hello, world!"},
+ )
+ print(f" Vowel count: {result.data}")
+ print()
+
+ print("=" * 60)
+ print("Test 2: Primitive result_type (list[str])")
+ print("=" * 60)
+ result = await client.call_tool(
+ "extract_keywords",
+ {
+ "text": "FastMCP is a Python framework for building Model Context Protocol servers and clients."
+ },
+ )
+ print(f" Keywords: {result.data}")
+ print()
+
+ print("=" * 60)
+ print("Test 3: sample_step() with history tracking")
+ print("=" * 60)
+ result = await client.call_tool(
+ "multi_step_reasoning",
+ {"question": "What is 15 + 27? Think step by step."},
+ )
+ print(f" Result: {result.data}")
+ print()
+
+ print("=" * 60)
+ print("Test 4: Structured output (Pydantic model)")
+ print("=" * 60)
+ result = await client.call_tool(
+ "analyze_text",
+ {"text": "I really enjoyed learning about FastMCP today!"},
+ )
+ print(f" Analysis: {result.data}")
+ print()
+
+ print("All tests completed successfully!")
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index a55ed2169d..9e8fc7a2c9 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -161,6 +161,8 @@ def _convert_to_openai_messages(
# Collect tool calls and text from the list
tool_calls: list[ChatCompletionMessageToolCallParam] = []
text_parts: list[str] = []
+ # Collect tool results separately to maintain correct ordering
+ tool_messages: list[ChatCompletionToolMessageParam] = []
for item in content:
if isinstance(item, ToolUseContent):
@@ -177,7 +179,7 @@ def _convert_to_openai_messages(
elif isinstance(item, TextContent):
text_parts.append(item.text)
elif isinstance(item, ToolResultContent):
- # Each tool result becomes a separate tool message
+ # Collect tool results (added after assistant message)
content_text = ""
if item.content:
result_texts = []
@@ -185,7 +187,7 @@ def _convert_to_openai_messages(
if isinstance(sub_item, TextContent):
result_texts.append(sub_item.text)
content_text = "\n".join(result_texts)
- openai_messages.append(
+ tool_messages.append(
ChatCompletionToolMessageParam(
role="tool",
tool_call_id=item.toolUseId,
@@ -194,6 +196,7 @@ def _convert_to_openai_messages(
)
# Add assistant message with tool calls if present
+ # OpenAI requires: assistant (with tool_calls) -> tool messages
if tool_calls or text_parts:
msg_content = "\n".join(text_parts) if text_parts else None
if tool_calls:
@@ -204,6 +207,8 @@ def _convert_to_openai_messages(
tool_calls=tool_calls,
)
)
+ # Add tool messages AFTER assistant message
+ openai_messages.extend(tool_messages)
elif msg_content:
if message.role == "user":
openai_messages.append(
@@ -219,6 +224,9 @@ def _convert_to_openai_messages(
content=msg_content,
)
)
+ elif tool_messages:
+ # Tool results only (assistant message was in previous message)
+ openai_messages.extend(tool_messages)
continue
# Handle ToolUseContent (assistant's tool calls)
@@ -382,8 +390,11 @@ def _chat_completion_to_result_with_tools(
# Parse the arguments JSON string
try:
arguments = json.loads(func.arguments) # type: ignore[union-attr]
- except json.JSONDecodeError:
- arguments = {}
+ except json.JSONDecodeError as e:
+ raise ValueError(
+ f"Invalid JSON in tool arguments for "
+ f"'{func.name}': {func.arguments}" # type: ignore[union-attr]
+ ) from e
content.append(
ToolUseContent(
diff --git a/src/fastmcp/server/context.py b/src/fastmcp/server/context.py
index cebedbff32..2e5cea6a4e 100644
--- a/src/fastmcp/server/context.py
+++ b/src/fastmcp/server/context.py
@@ -659,15 +659,15 @@ async def sample_step(
and response.stopReason == "toolUse"
)
- # If not a tool use, return immediately
- if not is_tool_use_response:
- return SampleStep(response=response, history=current_messages)
-
- # Add assistant's response to messages
+ # Always include the assistant response in history
current_messages.append(
SamplingMessage(role="assistant", content=response.content)
)
+ # If not a tool use, return immediately
+ if not is_tool_use_response:
+ return SampleStep(response=response, history=current_messages)
+
# If not executing tools, return with assistant message but no tool results
if not execute_tools:
return SampleStep(response=response, history=current_messages)
@@ -811,10 +811,21 @@ async def sample(
if tool_call.name == "final_response":
# Validate and return the structured result
type_adapter = get_cached_typeadapter(result_type)
+
+ # Unwrap if we wrapped primitives (non-object schemas)
+ input_data = tool_call.input
+ original_schema = compress_schema(
+ type_adapter.json_schema(), prune_titles=True
+ )
+ if (
+ original_schema.get("type") != "object"
+ and isinstance(input_data, dict)
+ and "value" in input_data
+ ):
+ input_data = input_data["value"]
+
try:
- validated_result = type_adapter.validate_python(
- tool_call.input
- )
+ validated_result = type_adapter.validate_python(input_data)
text = json.dumps(
type_adapter.dump_python(validated_result, mode="json")
)
@@ -849,6 +860,13 @@ async def sample(
# If not a tool use response, we're done
if not step.is_tool_use:
+ # For structured output, the LLM must use the final_response tool
+ if result_type is not None and result_type is not str:
+ raise RuntimeError(
+ f"Expected structured output of type {result_type.__name__}, "
+ "but the LLM returned a text response instead of calling "
+ "the final_response tool."
+ )
return SamplingResult(
text=step.text,
result=cast(ResultT, step.text if step.text else ""),
@@ -1024,6 +1042,14 @@ def _create_final_response_tool(result_type: type) -> SamplingTool:
schema = type_adapter.json_schema()
schema = compress_schema(schema, prune_titles=True)
+ # Tool parameters must be object-shaped. Wrap primitives in {"value": }
+ if schema.get("type") != "object":
+ schema = {
+ "type": "object",
+ "properties": {"value": schema},
+ "required": ["value"],
+ }
+
# The fn just returns the input as-is (validation happens in the loop)
def final_response(**kwargs: Any) -> dict[str, Any]:
return kwargs
diff --git a/src/fastmcp/server/sampling/run.py b/src/fastmcp/server/sampling/run.py
index 37fa8dbdb4..70eccd47a9 100644
--- a/src/fastmcp/server/sampling/run.py
+++ b/src/fastmcp/server/sampling/run.py
@@ -163,6 +163,8 @@ def determine_handler_mode(context: Context, needs_tools: bool) -> bool:
f"Invalid sampling_handler_behavior: {fastmcp.sampling_handler_behavior!r}. "
"Must be 'always', 'fallback', or None."
)
+ elif not has_sampling:
+ raise ValueError("Client does not support sampling")
elif needs_tools and not has_tools_capability:
raise ValueError(
"Client does not support sampling with tools. "
From 0d9272e6981b8f4f8e166db7b3f8619fa84df7fb Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sun, 14 Dec 2025 13:39:44 -0500
Subject: [PATCH 14/15] Add return type annotation to
OpenAISamplingHandler.__init__
---
src/fastmcp/experimental/sampling/handlers/openai.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/fastmcp/experimental/sampling/handlers/openai.py b/src/fastmcp/experimental/sampling/handlers/openai.py
index 9e8fc7a2c9..3d327682a7 100644
--- a/src/fastmcp/experimental/sampling/handlers/openai.py
+++ b/src/fastmcp/experimental/sampling/handlers/openai.py
@@ -48,7 +48,7 @@ def __init__(
self,
default_model: ChatModel,
client: AsyncOpenAI | None = None,
- ):
+ ) -> None:
self.client: AsyncOpenAI = client or AsyncOpenAI()
self.default_model: ChatModel = default_model
From 2c63948b16eb31703103066a5e6e4525e4de9c03 Mon Sep 17 00:00:00 2001
From: Jeremiah Lowin <153965+jlowin@users.noreply.github.com>
Date: Sun, 14 Dec 2025 13:40:32 -0500
Subject: [PATCH 15/15] Use explicit 'is not None' check for
sampling_capabilities defaulting
---
src/fastmcp/client/client.py | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/src/fastmcp/client/client.py b/src/fastmcp/client/client.py
index 1b9daa5efa..2a68a5b3af 100644
--- a/src/fastmcp/client/client.py
+++ b/src/fastmcp/client/client.py
@@ -310,7 +310,8 @@ def __init__(
# Default to tools-enabled capabilities unless explicitly overridden
self._session_kwargs["sampling_capabilities"] = (
sampling_capabilities
- or mcp.types.SamplingCapability(
+ if sampling_capabilities is not None
+ else mcp.types.SamplingCapability(
tools=mcp.types.SamplingToolsCapability()
)
)
@@ -377,7 +378,8 @@ def set_sampling_callback(
# Default to tools-enabled capabilities unless explicitly overridden
self._session_kwargs["sampling_capabilities"] = (
sampling_capabilities
- or mcp.types.SamplingCapability(tools=mcp.types.SamplingToolsCapability())
+ if sampling_capabilities is not None
+ else mcp.types.SamplingCapability(tools=mcp.types.SamplingToolsCapability())
)
def set_elicitation_callback(