Build and manage MCP servers and clients quickly with Python.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "fastmcp" yet — see the docs or source repo.
Using fastmcp, write a minimal runnable Python example that creates an MCP server with a tool named get_weather, which takes a city name and returns mock weather data. Include dependency installation, folder structure, and startup commands.
A runnable MCP server example with tool definitions, setup steps, and run instructions.
Using fastmcp, create a Python client example that connects to an existing MCP server, lists available tools, and calls the get_weather tool for Shanghai. Explain the key parts of the code.
An MCP client example showing connection, tool discovery, and invocation, with code explanations.
I already have a Python function that reads sales reports. Show how to wrap it as an MCP tool with fastmcp, including parameter validation, error handling, and an example response structure for AI assistant use.
An implementation approach and sample code for turning an existing function into an MCP tool for AI workflows.
Quickly bootstrap an MCP server with sample tools and Docker support.
Deploy a production-ready MCP server with demo tools and interactive testing UI.
Access versatile utility tools via FastAPI and WebSocket for development automation.
Deploy a production-ready FastMCP server with dynamically loaded tools and resources.
Build an MCP server with FastMCP and create tools for LLM workflows.
A minimal MCP server template for testing and rapid development.