LLMling is a Python library that provides a configurable LLM task processing framework, supporting multiple context processors and LLM provider integrations.

A tool for interacting with the macOS Messages application through Python bridging, supporting reading and sending iMessage messages

click-mcp is a Python library that extends Click applications through Model Context Protocol (MCP) support, enabling AI agents to interact with CLI tools.

This project is a server - side implementation based on the Model Context Protocol (MCP) for connecting to the GraphQL API to access policy data. It is built using the Python SDK and GQL library and supports rapid deployment through the uv tool.

mcp-x is a Python library that provides specific functional support.

mcpx-py is a Python library for interacting with LLMs through the mcp.run tool. It supports multiple AI models and provides structured output functionality.

The CrewAI MCP Adapter is a Python library that extends the CrewAI adapter ecosystem by integrating the Model Context Protocol (MCP), providing comprehensive support for custom agent and tool development.

This project is a PPT production service based on the MCP protocol and the python - pptx library. It supports the dynamic creation, editing, and saving of PowerPoint presentations through chat interaction. It provides multiple functions such as adding slides, images, and tables, and can generate download links and export in Base64 format.

Documentation library for the MCP Python SDK, providing documentation for the Python development toolkit of the Model Context Protocol (MCP), including usage guides and API references for the FastMCP server framework.

The MCP Python SDK is a Python library that implements the Model Context Protocol (MCP), used to standardize the interaction between LLM applications and servers, supporting the exposure and management of resources, tools, and prompts.

OmniMind is an open - source Python library designed to simplify Model Context Protocol (MCP) integration and support the development of AI agents, workflows, and automation. It offers plug - and - play functionality, has built - in multiple tools, and provides intelligent responses based on Google Gemini, making it suitable for developers and beginners to quickly build AI applications.

The MCP Python SDK is a Python library that implements the Model Context Protocol (MCP) for standardizing the interaction between LLM applications and servers, supporting the exposure and management of resources, tools, and prompts.

MCP Server Box is a Python project that implements functions such as file search, text extraction, AI queries, and data extraction through the Box API. It integrates the box-sdk-gen library and provides a toolset for interacting with Box files and folders.

The Monarch Money MCP server is a protocol service that provides access to financial data, supporting functions such as account management, transaction operations, and budget analysis. It interacts with the Monarch Money API through a Python library wrapper.

An advanced Python library for building and managing an autonomous agent network that collaboratively solves complex tasks. It is designed based on the OpenAPI standard and provides a concise type - safe interface and team - collaboration features.

This is a Docker-based Python server template that implements the Model Context Protocol (MCP), built with the FastMCP library, and supports real-time interaction with large language models (LLMs) through Server-Sent Events (SSE).

Plex MCP Server is a Python-based service that integrates with the Plex media server API through the PlexAPI library, providing functions such as movie search and playlist management. It supports filtering movies by director/genre, finding missing movies, creating playlists, etc., and can be configured and used through the Claude application.

pymcp-sse is a lightweight Python library that implements the Model Context Protocol (MCP) based on HTTP/SSE transmission, providing functions such as modular framework, tool registration and discovery, server push, and LLM integration.

This project is a server implementation based on the Model Context Protocol (MCP) for connecting to the GraphQL API to access policy data. It is built using the Python SDK and the GQL library and supports policy queries in the TrueRAG system.

A lightweight file system service built on Python and the MCP library that securely exposes the file content and metadata in pre - configured directories and integrates with Claude AI for natural language interaction.