GoKawiilThe fast, Pythonic way to build MCP servers, clients, and applications.
FastMCP is the standard framework for building MCP applications. The The Model Context Protocol (MCP) connects LLMs to tools and data. FastMCP gives you everything you need to go from prototype to production — build servers that expose capabilities, connect clients to any MCP service, and give your tools interactive UIs:
Copy from fastmcp import FastMCP mcp = FastMCP ( " Demo 🚀 " ) @ mcp . tool def add ( a : int , b : int ) -> int : """ Add two numbers """ return a + b if __name__ == " __main__ " : mcp . run ()
Move Fast and Make Things
The Model Context Protocol (MCP) lets you give agents access to your tools and data. But building an effective MCP application is harder than it looks.
FastMCP handles all of it. Declare a tool with a Python function, and the schema, validation, and documentation are generated automatically. Connect to a server with a URL, and transport negotiation, authentication, and protocol lifecycle are managed for you. You focus on your logic, and the MCP part just works: with FastMCP, best practices are built in.
That’s why FastMCP is the standard framework for working with MCP. FastMCP 1.0 was incorporated into the official MCP Python SDK in 2024. Today, the actively maintained standalone project is downloaded a million times a day, and some version of FastMCP powers 70% of MCP servers across all languages.
FastMCP has three pillars:
Servers Expose tools, resources, and prompts to LLMs. Apps Give your tools interactive UIs rendered directly in the conversation. Clients Connect to any MCP server — local or remote, programmatic or CLI.
Servers wrap your Python functions into MCP-compliant tools, resources, and prompts. Clients connect to any server with full protocol support. And Apps give your tools interactive UIs rendered directly in the conversation. wrap your Python functions into MCP-compliant tools, resources, and prompts.connect to any server with full protocol support. Andgive your tools interactive UIs rendered directly in the conversation.
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