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 Maya MCP Server project implements the Model Context Protocol (MCP), allowing AI assistants to control Autodesk Maya through natural language. This project features a lightweight design, does not require installing plugins in Maya, communicates via Maya's default MEL script port, and supports dynamically adding new tool functions.

The Wrike MCP server is a lightweight implementation used to connect the Wrike project management platform with language learning models (LLMs), providing API interfaces for task querying, comment adding, task creation, etc.

A code analysis server based on the MCP protocol that helps AI models understand and analyze codebases through natural language conversations, providing lightweight code exploration and understanding capabilities.

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

The Model Context Protocol (MCP) is a lightweight framework for real-time management and sharing of context memory between language models.

A lightweight MCP server that retrieves relevant documentation content from popular AI libraries such as LangChain, LlamaIndex, and OpenAI by combining web search and content parsing, providing language models with the function of dynamically querying the latest documentation.

This project showcases a lightweight multi-agent AI system that combines the Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) for business analysis. The system provides statistical analysis of business data and knowledge retrieval functions for natural language queries by coordinating multiple dedicated tool servers. It features a modular design that facilitates expansion and the switching of LLM backends.

The Hyprland MCP Server is an unofficial, lightweight server that exposes the functionality of the Hyprland window manager to language models through the Model Context Protocol (MCP), supporting natural language queries and control of Hyprland's window management, layout, input, etc.