FastMCP

FastMCP is the standard Python framework for building MCP servers, clients, and applications. MCP, or Model Context Protocol, is an open standard that gives AI models a consistent way to connect with external tools, data sources, and services. FastMCP handles all the protocol complexity under the hood, so you write a Python function, decorate it, and your tool is immediately available to any MCP-compatible AI client. It powers over 70% of all MCP servers in production today and is downloaded more than a million times a day.

How FastMCP works

When an AI model needs to use a tool, it speaks MCP: a standardized communication format that tells a server what to do and receives structured results back. Building that server from scratch involves a lot of boilerplate protocol code. FastMCP removes all of it.

Here is how the framework is structured:

• FastMCP class: The entry point for every server. You instantiate it once and attach all your tools, resources, and prompts to it using decorators.
• Tools: Functions the AI can call to take action, like querying a database, sending a message, or running a calculation. You register them with @mcp.tool.
• Resources: Read-only data the AI can pull into its context, like config files, user profiles, or live data from an API. You register them with @mcp.resource.
• Prompts: Reusable message templates that guide how the AI interacts with your tools in consistent ways. You register them with @mcp.prompt.
• Automatic schema generation: FastMCP reads your Python type hints and docstrings to generate the full MCP schema for each tool automatically. No manual configuration needed.
• Transport layer: FastMCP handles how messages move between client and server, supporting stdio for local tools and HTTP for remote deployments, without you changing your application code.

A minimal working server looks like this: create a FastMCP instance, write a Python function, and add @mcp.tool. That is the entire setup.

What you can build with FastMCP

• Internal admin tool server: Expose your company’s internal APIs as MCP tools so an AI assistant can query your database, update records, or trigger workflows directly from a conversation.
• Document processing server: Build a server that reads PDFs, DOCX files, and other formats, then surfaces them to any connected AI client for analysis, summarisation, or extraction.
• Custom AI agent backend: Wire up tools for web search, email sending, calendar access, and file management, then connect the server to Claude, Cursor, or any MCP-compatible agent.
• Multi-server composition pipeline: Combine multiple FastMCP servers into one application using server mounting, so a single endpoint exposes tools from several specialised services.
• Interactive dashboard tool: Use FastMCP’s Apps feature to return live charts, tables, and forms directly in the conversation instead of plain text, so users see visual outputs from tool calls.

FastAPI-to-MCP bridge: Wrap an existing FastAPI application with FastMCP so your current REST endpoints become agent-callable tools without duplicating any logic or schemas.