K8M is an AI-driven lightweight Kubernetes console tool that integrates large model capabilities, supports multi-cluster management, and provides MCP services.

This project is a Model Context Protocol (MCP) adapter used to connect large language models (LLMs) with the Lisp development environment, supporting interaction through the lightweight Lisply protocol. The main functions include Lisp code evaluation, HTTP requests, and debugging support, suitable for scenarios such as AI-assisted symbolic programming and CAD design automation.

Memgraph MCP Server is a lightweight implementation of the Model Context Protocol (MCP) server, aiming to connect the Memgraph graph database with large language models (LLMs) and provide Cypher query execution and schema information acquisition functions.

The MCP GitHub Reader is a lightweight MCP server that integrates the content of GitHub repositories into the context of large language models, providing repository analysis, file access, intelligent filtering, and search functions.

OtterBridge is a lightweight MCP server used to connect applications with multiple large language model providers, supporting models such as Ollama, with a simple and flexible design.

The MCP Crew AI Server is a lightweight Python - based server for running and managing CrewAI workflows, supporting communication with large language models through the MCP protocol.

A lightweight MCP server that allows large language models to interact with SQLite databases autonomously