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Sampling

Sampling allows MCP tools to request LLM completions during their execution.

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Configuration

To enable sampling, provide a sampling_callback function when initializing the MCPClient: 
from mcp_use.client import MCPClient
from mcp.client.session import ClientSession
from mcp.types import (
    CreateMessageRequestParams,
    CreateMessageResult,
    ErrorData,
    TextContent
)

async def sampling_callback(
    context: ClientSession,
    params: CreateMessageRequestParams
) -> CreateMessageResult | ErrorData:
    """
    Your sampling callback implementation.
    This function receives a prompt and returns an LLM response.
    """
    # Integrate with your LLM of choice (OpenAI, Anthropic, etc.)
    response = await your_llm.complete(params.messages[-1].content.text)

    return CreateMessageResult(
        content=TextContent(text=response, type="text"),
        model="your-model-name",
        role="assistant"
    )

# Initialize client with sampling support
client = MCPClient(
    config="config.json",
    sampling_callback=sampling_callback
)

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Creating Sampling-Enabled Tools

When building MCP servers, tools can request sampling using the context parameter: 
from fastmcp import Context, FastMCP

mcp = FastMCP(name="MyServer")

@mcp.tool
async def analyze_sentiment(text: str, ctx: Context) -> str:
    """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 through sampling
    response = await ctx.sample(prompt)

    return response.text.strip()

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Error Handling

If no sampling callback is provided but a tool requests sampling: 
# Without sampling callback
client = MCPClient(config="config.json")  # No sampling_callback

# Tool that requires sampling will return an error
result = await session.call_tool("analyze_sentiment", {"text": "Hello"})
# result.isError will be True