FastMCP

What is FastMCP?

FastMCP is an open-source Python framework for building applications on the Model Context Protocol (MCP), the interface that lets AI assistants call tools and read data. You write ordinary Python functions, and FastMCP generates the schemas, validates inputs and outputs, and handles transport, authentication and protocol details. It covers three areas: servers that expose tools, resources and prompts; clients that connect to local or remote MCP servers from Python or the command line; and apps that let tools return forms, tables and charts inside a conversation. The project, made by Prefect, says its high-level API was incorporated into the official MCP Python SDK in 2024, and the docs describe version 4 as built for the new MCP protocol. For hosting there is Prefect Horizon, which deploys a server from a GitHub repository in under a minute with OAuth, a registry, gateway controls and audit logs. FastMCP is free to use, and Horizon is free for personal projects with enterprise governance available for teams.

Use cases

• Expose Python functions as MCP tools
• Connect AI assistants to internal data
• Build MCP clients in Python
• Return interactive UIs from tools
• Deploy MCP servers from GitHub
• Proxy and compose multiple MCP servers
• Secure MCP servers with OAuth

Who it's for

• Python developers
• AI engineers
• Platform teams
• Teams building agents

Why use FastMCP

• Plain Python functions become MCP tools without hand-writing schemas or protocol code.
• One framework handles servers, clients and interactive apps, so the pieces share a single model.
• Horizon offers a push-to-deploy path from GitHub to a secured server URL, including preview deployments for pull requests.
• The project is free and open source, with a free personal tier on the hosting platform.
• Docs are written for coding agents as well as people, with Markdown pages and a docs MCP server.

FastMCP features

• Servers that turn Python functions into MCP tools, resources and prompts
• Automatic schema generation and input and output validation
• Clients for connecting to local or remote MCP servers
• Apps that return forms, tables and charts from tools
• Authentication and OAuth support
• Server composition, proxy and filesystem providers
• Tool transformations, Code Mode and Tool Search
• Middleware, sampling, elicitation and progress reporting
• CLI with login, inspect and run commands
• Documentation published as Markdown and llms.txt, plus a docs MCP server

FastMCP integrations

• Claude Desktop
• Claude Code
• Cursor
• GitHub
• Prefect Horizon
• OAuth

How to get started with FastMCP

1. Add FastMCP to your project, for example with uv add fastmcp.
2. Write a server by creating a FastMCP instance and decorating Python functions as tools.
3. Run it locally and test it with the Inspector or a FastMCP client.
4. Push the repo to GitHub and deploy it on Prefect Horizon, choosing the entrypoint file and whether to require authentication.
5. Copy the connection snippet for Claude Desktop, Cursor, Claude Code or another MCP client.

FastMCP limitations

• Requires writing Python code; TypeScript users need the separate FastMCP for TypeScript
• Horizon deployment needs a GitHub account and a repository with a FastMCP server instance
• Documentation follows the main branch and may describe features not yet in a stable release
• Pricing for Horizon enterprise features is not published

FastMCP pricing

FastMCP is open source, so you can use and self-host it for free.

FastMCP plans

• FastMCP

  Free

  open-source framework

  • Servers, clients and apps
  • Authentication and deployment tooling
  • Self-hosting

• Horizon Personal

  Free

  for personal projects

  • Managed hosting from GitHub
  • Built-in OAuth authentication
  • Preview deployments for pull requests

• Horizon Enterprise

  Price on vendor site

  • Governance for thousands of users
  • SSO and tool-level RBAC
  • Private registry and audit logs

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

FastMCP alternatives

The main FastMCP alternatives listed on Debuly are MCP Python SDK, MCP TypeScript SDK, Composio, LangChain, Pipedream and Pydantic AI. The table compares pricing and platforms; order is A–Z.

1. 

   Composio

   Developer platform with SDKs, CLI and MCP that gives AI agents 1000+ pre-authenticated app toolkits and per-user sessions.

   Developer platformsAutomation and agents
   Visit Composio (official site)
2. 

   LangChain

   Open-source framework for building LLM agents, paired with LangSmith for tracing, evals and agent deployment.

   Developer platformsAutomation and agentsCoding
   FreemiumVisit LangChain (official site)
3. 

   MCP Python SDK

   The official open-source Python library for writing Model Context Protocol servers and clients that give LLM apps access to tools and data.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit MCP Python SDK (official site)
4. 

   MCP TypeScript SDK

   Official TypeScript SDK for building Model Context Protocol servers and clients that run on Node.js, Bun and Deno.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit MCP TypeScript SDK (official site)
5. 

   Pipedream

   Integration layer for AI agents and apps, with managed auth for 3,000+ APIs, prebuilt tools, triggers and MCP servers via Pipedream Connect.

   Developer platformsAutomation and agents
   FreemiumVisit Pipedream (official site)
6. 

   Pydantic AI

   A type-safe, model-agnostic Python framework for building agents and LLM applications, from the team behind Pydantic.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit Pydantic AI (official site)

FastMCP FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from gofastmcp.com · fetched 23 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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