FastMCP

FastMCP is the standard Python framework for building MCP (Model Context Protocol) servers and clients. MCP is the open protocol that lets AI applications like Claude, Cursor, and Windsurf connect to external tools and data. FastMCP handles all the protocol complexity so you can focus on writing Python functions. It downloads over a million times a day and powers around 70% of MCP servers in production across all languages, making it the default choice for AI engineers who need to expose capabilities to LLM-based systems.

How FastMCP works

FastMCP gives LLMs a way to call your code. You write a Python function, decorate it with @mcp.tool, and FastMCP turns it into a fully compliant MCP server that any AI app can connect to.

Here is what happens under the hood:

• Schema generation: FastMCP reads your function’s type hints and docstring and builds the JSON schema the protocol requires. No manual spec-writing is needed.
• Validation: Incoming arguments from the LLM are validated against that schema before your function runs. Bad inputs get rejected cleanly before reaching your code.
• Transport: FastMCP negotiates the communication channel automatically, whether the client connects over stdio (standard I/O, used by local apps like Claude Desktop) or HTTP. You write zero transport code.
• Resources: Beyond tools (functions the LLM calls), FastMCP supports Resources, which are data sources the LLM can read at runtime, like file contents, database records, or API responses.
• Prompts: You can also define Prompts, which are templated instruction sets surfaced directly in the host application’s UI, giving users structured ways to interact with your server.
• MCP Client: FastMCP includes a full MCP Client for connecting your Python code to any other MCP server, so you can chain or compose multiple servers into one pipeline.

The framework takes obvious inspiration from FastAPI: minimal boilerplate, decorators for registration, and type hints doing the heavy lifting.

What you can build with FastMCP

• Database query tool: Expose a read-only SQL query function as an MCP tool. Claude or any MCP-compatible agent can query your database directly from chat, with schema validation ensuring only well-formed queries run.
• File processing server: Build an MCP server that reads, parses, and summarises documents. Attach it to Claude Desktop so your assistant works directly on local files without copy-pasting content into the chat window.
• API integration layer: Wrap a third-party REST API like Slack, Notion, GitHub, or Jira as a set of MCP tools. Any MCP client can then call those APIs naturally, without custom integration code in the host application.
• Internal knowledge base: Define MCP Resources that serve chunks of internal documentation or product data. The LLM fetches exactly what it needs at runtime rather than relying on a large, static system prompt.
• Multi-server agent pipeline: Use FastMCP’s composition features to proxy and combine several MCP servers into one endpoint. An agent can call tools from your database server, your Slack server, and your file server through a single connection.
• Custom developer tooling server: Expose code search, git history, CI build status, or test results as MCP tools. Attach to Cursor or Windsurf so your coding agent has live context about the codebase it is working in.