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FastMCP

A Python framework for developers building MCP servers and clients that connect AI applications to tools and data.

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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.

Key Highlights

  • Python decorators turn functions into MCP tools.
  • Resources and prompts expose data and reusable instructions.
  • Client library connects to local and remote MCP servers.
  • FastAPI and OpenAPI integration can generate MCP interfaces.
  • CLI runs servers locally or over HTTP.

What Makes It Different

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.

Features & Capabilities

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.

User Ratings and Testimonials

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.

Pricing & Value

  • Open-source framework: Free to use and self-host under the Apache 2.0 license. Local execution and your own infrastructure are supported.
  • Prefect Horizon: A separate enterprise offering for managed hosting and governance. Its hosted services, SSO, and tool-level RBAC are separate from the free framework.

For teams that want to write and run their own MCP tools, the framework provides the Python application layer without a software subscription.

FAQs

What is the difference between MCP and FastMCP?

MCP is the protocol for connecting AI applications to tools and data. FastMCP is a Python framework for building its servers and clients.

Who is behind FastMCP?

Prefect builds and maintains the Python framework and its official TypeScript counterpart.

What are the differences between FastAPI MCP and FastMCP?

FastAPI-MCP exposes existing FastAPI endpoints as MCP tools. FastMCP builds MCP servers and clients, with optional FastAPI integration.

How do I use FastMCP?

Create a FastMCP instance, decorate Python functions with @mcp.tool, then run the server and connect an MCP client.

What is FastMCP for?

Developers use it to expose Python functions, data, and prompts to AI applications and to connect Python clients to MCP servers.

How do I install FastMCP?

Add the Python package with uv add fastmcp or pip install fastmcp. Run fastmcp version to check the installation.

What are the current versions of FastMCP?

The latest stable Python release is 4.1.0. Run fastmcp version to check your installation; development builds may differ.

Where can I find the documentation for FastMCP?

Visit https://gofastmcp.com/ for guides and API references. The Python repository is https://github.com/PrefectHQ/fastmcp.

What is the command to run a FastMCP server?

Use fastmcp run server.py for a local stdio server. Add --transport http to serve clients over HTTP.

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Curated by Michał Śnieżyński. Website may contain affiliate links.