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.
Run terminal commands, execute Python, and manage files for local automation.
Quickly bootstrap an MCP server with sample tools and Docker support.
Scrape web content and search documentation through a FastMCP-powered MCP server.
Deploy a production-ready MCP server with demo tools and interactive testing UI.
Learn and demonstrate MCP server capabilities built with FastMCP.
Run Python, install dependencies, and automate browsers through a containerized MCP server