jlowin/fastmcp
Fastmcp
fastmcp is a Python library and framework for building servers and clients that implement the Model Context Protocol. It serves as a tool integration library designed to connect large language models to external tools and data sources.
The framework features an interactive tool user interface renderer, which allows for the display of visual interfaces for tools directly within a conversational flow. It also provides a library for automatically generating schemas and validation for tools used by language models.
The project covers server and client development, including tool and resource exposure, connection management, and AI agent resource orchestration. It handles the underlying protocol through a transport layer and message dispatch system, using schema-driven generation to map functions and resources to the protocol.
Features
- Model Context Protocol Implementations - A complete Python framework for building Model Context Protocol servers and clients for LLM tool integration.
- LLM Tooling Integrations - Wraps Python functions as tools for language models with automated schema generation and validation.
- Model Context Protocol Clients - Provides the client-side implementation for connecting AI models to external tools via the Model Context Protocol.
- Model Context Protocol Servers - Implements the server-side of the Model Context Protocol to expose tools and data sources to AI models.
- Function Schema Generators - Generates tool schemas automatically by inspecting Python function signatures and type hints at runtime.
- Tool Exposure Interfaces - Automatically generates compliant schemas and documentation to expose Python functions as tools for AI agents.
- AI Agent Servers - Provides servers that expose integration schemas and actions to AI agents via the Model Context Protocol.
- Server Connection Managers - Manages the lifecycle, transport negotiation, and authentication for multiple protocol server connections.
- Interactive AI Interfaces - Enables the rendering of functional and manipulatable UI components directly within conversational AI flows.
- Prompt Templates - Provides structured templates for integrating external resource data into formatted strings for model consumption.
- Protocol Resource Mappings - Uses Python type hints to map external data sources to protocol resources with automatic validation.
- Network Transport Layers - Implements a non-blocking asynchronous transport layer for communication between clients and servers.
- JSON-RPC Interfaces - Routes incoming JSON-RPC requests to specific handler functions based on method names.
- Protocol Negotiation Tools - Coordinates the connection handshake, capability negotiation, and session termination between protocol entities.
- Interactive Model Output Rendering - Renders raw AI model tool responses into rich, interactive visual user interface components.
- AI Agent Frameworks - Fast, Pythonic implementation of MCP servers.
- Model Context Protocol - Framework for building protocol servers using Python.
- Development and Debugging Tools - Pythonic framework for rapidly exposing tools and resources.
- Model Context Protocol - High-level Python framework for building protocol servers.
