Builds robust Model Context Protocol (MCP) servers in Python to expose tools, resources, and prompts to LLMs.
FastMCP is a high-level Python framework designed to simplify the creation of production-grade Model Context Protocol (MCP) servers. It provides a decorator-based interface for defining tools, resources, and prompts, enabling Claude and other LLMs to interact with custom code and data sources seamlessly. With advanced features like protocol-native background tasks, session-scoped state, agentic sampling, and multiple storage backends (Redis, Disk, etc.), FastMCP manages protocol complexities and prevents common implementation errors, making it ideal for developers building sophisticated AI agents and integrations.
Key Features
01Multiple storage backends including Redis, Disk, and encrypted stores
02Decorator-based tool, resource, and prompt registration
03Protocol-native background tasks with progress tracking
04Agentic sampling allowing LLMs to call tools during reasoning
05Advanced provider architecture with hot-reloading and OpenAPI support
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Use Cases
01Building data-rich resources that provide live context to LLM conversations
02Implementing long-running agentic workflows with asynchronous progress reporting
03Exposing internal business logic and APIs to Claude as executable tools
