FastMCP

The fast, Pythonic way to build MCP servers, clients, and apps.

Free· Free and open source under Apache-2.0; no paid tier for the framework itself. Commercial hosting/scaling optionally available via Prefect Horizon.MCP Servers

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Best for

Python developers and platform teams building MCP servers, clients, or gateways for Claude, Cursor, ChatGPT connectors, or in-house LLM agents, and anyone who wants the standard Pythonic MCP toolkit.

Skip if

Teams working exclusively in TypeScript/Node, non-developers looking for a no-code MCP builder, or anyone who needs a hosted, click-to-deploy MCP platform out of the box.

FastMCP is a Python framework for building Model Context Protocol (MCP) servers, clients, and interactive applications with the minimum possible boilerplate. Originally an independent project by Jeremiah Lowin, its core was absorbed into the official MCP Python SDK in 2024, and FastMCP 2.x has since grown into a broader application framework covering the whole MCP lifecycle: exposing Python functions as tools/resources/prompts, connecting to any MCP-compliant server as a client, and shipping interactive UIs that run inside a chat conversation. Decorate a function with @mcp.tool (or @mcp.resource / @mcp.prompt) and FastMCP inspects the signature and type hints to auto-generate a spec-correct JSON schema, validation, and documentation, then negotiates transport (stdio, SSE, streamable HTTP), auth, and lifecycle for you. The client side lets you call any MCP server from Python with a typed API, and higher-level primitives cover mounting, proxying, composition, middleware, OAuth, tags, and testing. Typical workflows include wrapping an internal REST API or database query layer as a tool set for Claude Desktop, Cursor, or ChatGPT connectors; building a company-internal MCP hub that federates several backends behind one server; standing up an interactive app that renders forms and results inside an LLM chat; and writing integration tests against MCP servers without spinning up a real transport. It powers a large share of MCP servers shipped in the ecosystem and is the de facto reference for how MCP is written in Python today.

Editor's take

If you are writing MCP servers in Python, this is the default answer — not because it markets itself well, but because its core became the official SDK. FastMCP 2.x adds genuinely useful composition, proxying, and client tooling on top. The one gotcha is choosing between the bundled SDK version and the standalone 2.x package; pick 2.x if you want the newest features.

— The AI Tool Bible editorial team

Pros

✅ Decorator-based API auto-generates MCP-compliant JSON schemas from type hints, eliminating manual protocol plumbing.
✅ Covers the full stack — servers, clients, and interactive apps — instead of just one side of the protocol.
✅ Its core became the official MCP Python SDK's FastMCP module, so patterns you learn are the standard.
✅ Supports multiple transports (stdio, SSE, streamable HTTP) and handles auth/OAuth, middleware, and lifecycle automatically.
✅ First-class composition primitives — mount, proxy, and combine servers — make it easy to build MCP gateways.
✅ Apache-2.0 open source with a very active maintainer and huge install base, so bugs get triaged fast.
✅ Good testing story: an in-process client lets you exercise a server end-to-end without a real transport.

Cons

⚠️ Python-only for the flagship framework; the TypeScript port is a separate project with its own feature drift.
⚠️ FastMCP 2.x has diverged from the version bundled inside the official MCP SDK, and choosing between them can be confusing.
⚠️ MCP itself is still a moving spec, so occasional breaking changes propagate into FastMCP releases.
⚠️ Higher-level 'apps' and enterprise features are newer and less battle-tested than the core server/client APIs.
⚠️ No built-in hosting — you still have to deploy the process yourself (or pay for Prefect Horizon) to make a server reachable.

Use cases

Wrapping internal REST APIs as MCP tools for Claude DesktopBuilding MCP gateways that federate multiple backendsExposing database queries as typed MCP toolsConnecting Python agents to third-party MCP serversPrototyping ChatGPT and Cursor connectorsAdding OAuth-protected tools to an LLM chatWriting integration tests for MCP serversShipping interactive in-chat apps and forms