Pydantic AI 🤝 FastMCP - Fastmcp

Pydantic AI ships a FastMCPToolset that lets a Pydantic AI agent call tools exposed by any MCP server through the FastMCP Client. Because the toolset is built on the FastMCP Client, it works with FastMCP servers as well as any other MCP server, and supports the full range of transports: in-memory, STDIO, Streamable HTTP, and SSE.

This page shows how to point FastMCPToolset at a FastMCP server, with examples for each transport. For the toolset's full API, see the Pydantic AI documentation.

<Tip> The `FastMCPToolset` currently exposes **tools** to the agent. Other MCP features such as elicitation and sampling are not yet supported through this toolset; use Pydantic AI's standard [`MCPServer`](https://ai.pydantic.dev/mcp/client/) client if you need them. </Tip>

Install

FastMCPToolset lives in pydantic-ai-slim behind the fastmcp optional group:

bash

pip install "pydantic-ai-slim[fastmcp]"

Create a Server

Create a FastMCP server with the tools you want to expose. We'll use a single dice-rolling tool throughout this guide.

python

import random
from fastmcp import FastMCP

mcp = FastMCP(name="Dice Roller")

@mcp.tool
def roll_dice(n_dice: int) -> list[int]:
    """Roll `n_dice` 6-sided dice and return the results."""
    return [random.randint(1, 6) for _ in range(n_dice)]

if __name__ == "__main__":
    mcp.run(transport="http", port=8000)

In-Memory

If your FastMCP server lives in the same process as your agent, pass the FastMCP instance directly. The toolset reuses an in-memory transport, which avoids a network round trip and is the fastest option for tests and embedded use.

python

import asyncio
import random
from fastmcp import FastMCP
from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

mcp = FastMCP(name="Dice Roller")

@mcp.tool
def roll_dice(n_dice: int) -> list[int]:
    return [random.randint(1, 6) for _ in range(n_dice)]

toolset = FastMCPToolset(mcp)
agent = Agent("openai:gpt-4.1", toolsets=[toolset])

async def main():
    result = await agent.run("Roll 3 dice!")
    print(result.output)

if __name__ == "__main__":
    asyncio.run(main())

Streamable HTTP

For a remote FastMCP server reachable over HTTP, pass the URL as a string. The toolset infers the Streamable HTTP transport from the URL.

python

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset("https://your-server-url.com/mcp")
agent = Agent("openai:gpt-4.1", toolsets=[toolset])

For SSE, use a /sse URL instead.

STDIO

To launch a FastMCP server as a subprocess, pass a script path and the toolset will use the STDIO transport.

python

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset("server.py")
agent = Agent("openai:gpt-4.1", toolsets=[toolset])

You can also pass a StdioTransport directly when you need control over the command, args, or environment.

MCP Configuration

To wire up multiple servers at once, pass an MCP configuration dictionary. The toolset opens one client per server and exposes all of their tools to the agent.

python

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

mcp_config = {
    "mcpServers": {
        "dice": {"command": "python", "args": ["server.py"]},
        "weather": {"url": "https://weather.example.com/mcp"},
    }
}

toolset = FastMCPToolset(mcp_config)
agent = Agent("openai:gpt-4.1", toolsets=[toolset])

Authentication

Because FastMCPToolset wraps a FastMCP Client, it inherits the client's full authentication story. To pass credentials such as a bearer token to a remote server, build a Client (or StreamableHttpTransport) yourself and hand it to the toolset.

python

from fastmcp import Client
from fastmcp.client.transports import StreamableHttpTransport
from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

transport = StreamableHttpTransport(
    url="https://your-server-url.com/mcp",
    headers={"Authorization": "Bearer your-access-token"},
)

toolset = FastMCPToolset(Client(transport))
agent = Agent("openai:gpt-4.1", toolsets=[toolset])

For OAuth flows, use FastMCP's OAuth helper when constructing the Client. For server-side token verification, see Token Verification.