
FastMCP is Prefect's open-source Python framework for building Model Context Protocol (MCP) servers and clients. It helps developers expose functions, data, and reusable prompts to AI applications. Your code defines the behavior; the framework handles schemas, validation, and protocol communication.
FastMCP builds tool schemas from Python functions, so developers can work with familiar code instead of assembling protocol messages. It combines server authoring and client connections in one framework. The official TypeScript counterpart is built and maintained by the same team at Prefect.
Install with uv add fastmcp or pip install fastmcp, create a FastMCP instance, and register functions with @mcp.tool. Start a saved server with fastmcp run server.py. The default stdio transport suits local client connections; Streamable HTTP serves clients over the network.
For existing APIs, generate MCP components from an OpenAPI definition or FastAPI app, or mount an MCP server inside FastAPI. Treat automatic API conversion as a starting point for prototyping: choose tools around agent tasks rather than exposing every endpoint. FastAPI must be installed separately.
Evaluate FastMCP by connecting your intended client and checking tool schemas, results, and error handling. A concrete maintenance tradeoff is version compatibility: minor releases can include breaking changes. Pin exact dependencies and test upgrades before deploying them.
For teams that want to write and run their own MCP tools, the framework provides the Python application layer without a software subscription.
MCP is the protocol for connecting AI applications to tools and data. FastMCP is a Python framework for building its servers and clients.
Prefect builds and maintains the Python framework and its official TypeScript counterpart.
FastAPI-MCP exposes existing FastAPI endpoints as MCP tools. FastMCP builds MCP servers and clients, with optional FastAPI integration.
Create a FastMCP instance, decorate Python functions with @mcp.tool, then run the server and connect an MCP client.
Developers use it to expose Python functions, data, and prompts to AI applications and to connect Python clients to MCP servers.
Add the Python package with uv add fastmcp or pip install fastmcp. Run fastmcp version to check the installation.
The latest stable Python release is 4.1.0. Run fastmcp version to check your installation; development builds may differ.
Visit https://gofastmcp.com/ for guides and API references. The Python repository is https://github.com/PrefectHQ/fastmcp.
Use fastmcp run server.py for a local stdio server. Add --transport http to serve clients over HTTP.
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