Server

PydanticAI models can also be used within MCP Servers.

Here's a simple example of a Python MCP server using PydanticAI within a tool call:

mcp_server.py

from mcp.server.fastmcp import FastMCP

from pydantic_ai import Agent

server = FastMCP('PydanticAI Server')
server_agent = Agent(
    'anthropic:claude-3-5-haiku-latest', system_prompt='always reply in rhyme'
)


@server.tool()
async def poet(theme: str) -> str:
    """Poem generator"""
    r = await server_agent.run(f'write a poem about {theme}')
    return r.data


if __name__ == '__main__':
    server.run()

This server can be queried with any MCP client. Here is an example using a direct Python client:

mcp_client.py

import asyncio
import os

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client


async def client():
    server_params = StdioServerParameters(
        command='uv', args=['run', 'mcp_server.py', 'server'], env=os.environ
    )
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            result = await session.call_tool('poet', {'theme': 'socks'})
            print(result.content[0].text)
            """
            Oh, socks, those garments soft and sweet,
            That nestle softly 'round our feet,
            From cotton, wool, or blended thread,
            They keep our toes from feeling dread.
            """


if __name__ == '__main__':
    asyncio.run(client())

Note: sampling, whereby servers may request LLM completions from the client, is not yet supported in PydanticAI.