A Python framework for building MCP servers that makes it simple to expose application data and actions to AI clients like Claude Desktop, Cursor, and VS Code through the Model Context Protocol. It wraps protocol handling behind a clean, decorator-based API allowing focus on business logic.
A universal framework for creating and deploying custom Model Context Protocol (MCP) tool servers with decorator-based tool registration, supporting multiple transports and automatic JSON schema generation for AI assistants.
A lightweight framework for building and running Model Context Protocol (MCP) servers using FastMCP, providing tools for development, debugging, and server management.
A TypeScript framework for building Model Context Protocol servers with a decorator-based API, enabling custom tools, resources, and prompts for AI applications.
A Python implementation of the Model Context Protocol that allows applications to provide standardized context for LLMs, enabling creation of servers that expose data and functionality to LLM applications through resources, tools, and prompts.
A production-ready template for building Model Context Protocol servers in Python, featuring example tools, structured logging, and multiple transport protocols.
