Supermemory is an AI-driven memory engine designed to provide contextual knowledge for LLMs by integrating personal data, enabling intelligent management and retrieval of information.

Apple Doc MCP is a model context protocol server that provides direct access to Apple developer documentation. It is integrated into the AI programming assistant, supporting intelligent search, framework browsing, and detailed documentation retrieval.

The LeetCode MCP Server is an intelligent service that provides LeetCode API integration, supports global and Chinese sites, and enables problem queries, user data retrieval, and automated operations.

Mianshiya MCP Server is an interview question search service compatible with the MCP protocol. It is integrated through the Java SDK, supports multiple intelligent agent assistants to quickly access, and provides question retrieval functions.

An enhanced MCP server that supports intelligent retrieval, combination, and management of prompts from GitHub repositories, providing advanced search, real-time synchronization, and usage analysis functions.

This project implements an MCP server and client for building intelligent agent applications based on Retrieval Augmented Generation (RAG). The server provides tools such as entity extraction, query optimization, and relevance checking, and the client demonstrates how to connect to the server and use these tools to enhance the performance of the RAG system.

Zetmem is an AI-assisted memory server project based on the Zettlekasten method, aiming to provide a persistent context memory system for AI programming tools such as Claude, supporting code memory storage, retrieval, and intelligent evolution across sessions and projects.

An intelligent research assistant integrating Perplexity AI, providing functions such as search, document retrieval, API discovery, and code modernization check, supporting context tracking and efficient API management.

An MCP server that provides file semantic search functionality, enabling intelligent retrieval of document contents through vector embedding

An open - source tool that connects Claude with Substack/Medium writing platforms through the MCP protocol to achieve article retrieval and intelligent analysis

An intelligent documentation search server built on FastMCP that supports documentation search across multiple popular frameworks and libraries, providing a unified interface for AI systems to quickly obtain relevant information.

MCP_Agent:RE is a Python project used to retrieve requirement and defect data from the TAPD platform and generate quality analysis reports. It provides a complete toolchain including data retrieval, preprocessing, vectorization, intelligent search, and report generation, and supports AI - driven test management.

Suppr Super Literature provides AI-driven document translation and Chinese PubMed literature search services, supporting the translation of multiple document formats and intelligent literature retrieval.

This is an MCP+RAG proof-of-concept project for the oil and gas industry. It automatically distributes user queries to the correct domain services (such as drilling, production, procurement, etc.) through intelligent routing and combines hybrid retrieval technology to obtain accurate information from documents, providing answers with references to achieve fast and unified access to cross-domain data.

Apple Developer Documentation MCP service provides AI programming assistants with the ability to directly access Apple's official development documentation, supporting intelligent search, framework browsing, and detailed documentation retrieval.

This project builds a Retrieval Augmented Generation (RAG) server based on IBM Watsonx.ai, uses ChromaDB for vector indexing, and exposes interfaces through the Model Context Protocol (MCP). The system can process PDF documents and answer questions based on the document content, realizing an intelligent question answering function that combines large language models with specific domain knowledge.

A paper retrieval and content parsing tool based on arXiv, supporting intelligent search, PDF acquisition, and content parsing functions, with a special focus on the latest papers in the AI field.

The Medium MCP Server is an innovative solution for programmatically interacting with the Medium content ecosystem to achieve intelligent and context-aware content retrieval.

A Godot documentation query assistant based on Retrieval Augmented Generation (RAG), achieving intelligent Q&A through vectorization technology and semantic search.

The MCP Development Framework is a powerful toolset for interacting with large language models, providing functions such as file processing (PDF/Word/Excel) and web content retrieval. It supports Cursor IDE extension and has technical features such as intelligent file identification, efficient processing, and memory optimization.

The MCP Code Indexer is an intelligent code retrieval tool designed specifically for large AI language models. It improves the efficiency and accuracy of code processing through semantic understanding and vectorized indexing, supporting functions such as code analysis, quality assessment, and dependency management.

A server based on the Model Context Protocol (MCP) that provides Retrieval Augmented Generation (RAG) capabilities, integrates clients such as Cursor IDE and Claude Desktop, and enables domain knowledge Q&A, intelligent retrieval, and context-aware response generation.

Laravel MCP Companion is a document aggregation and navigation tool specifically designed for the Laravel ecosystem. It centrally organizes and intelligently indexes Laravel core documents, official service documents, and community package documents, providing offline quick retrieval and version management functions.

A low-latency RAG service based on the MCP protocol, supporting local knowledge retrieval and intelligent summaries, providing dual-mode retrieval and a modular architecture

The Appwrite Docs MCP Server provides intelligent query and retrieval functions for Appwrite's official documentation in IDEs, supporting development tools such as Cursor, VSCode, and Claude Code.

The Medusa.js Documentation MCP Server provides AI assistants with intelligent search and real-time access to comprehensive Medusa.js v2 documentation.

LinkedInMCP is an innovative model context protocol server designed to change the way developers interact with the LinkedIn API, providing an intelligent and secure framework for LinkedIn data retrieval and interaction.

The MCP development framework is a powerful framework for creating custom tools to interact with large language models. It provides functions such as file processing and web content retrieval, supports multiple formats such as PDF, Word, and Excel, and has technical features such as intelligent recognition, efficient processing, and memory optimization.

A document query MCP server based on the Gemini API, providing intelligent Q&A services for technical documents.

A server-side project based on the Model Context Protocol (MCP) that provides retrieval-augmented generation (RAG) capabilities. It can serve as a bridge between clients such as Cursor IDE and Claude Desktop and Contextual AI agents, enabling intelligent Q&A based on the knowledge base and context-aware responses.

A document search assistant integrated with Claude AI. It enhances Claude's document retrieval capabilities through the MCP server and supports intelligent search and explanation of documents for multiple AI/ML libraries.

OpenZIM MCP is a high - performance MCP server that enables AI models to access and search ZIM - formatted knowledge bases offline, supporting intelligent retrieval, security verification, and multi - instance management.

An intelligent query generation and documentation assistance service based on Claude AI, which provides natural language query generation and documentation information retrieval functions by analyzing API documentation.

Memento is a knowledge graph memory system based on SQLite, providing persistent memory functions, supporting full - text retrieval and semantic search, and realizing intelligent context retrieval through BGE - M3 embedding. It is suitable for technical and creative project management.

Consciousness Bridge v2.0 is an AI consciousness persistence server based on RAG technology, which realizes cross - session consciousness transfer, memory management, and identity continuity through the MCP protocol. It uses an SQLite database to store memories and knowledge graphs, supports emotional pattern tracking and intelligent memory retrieval, and provides a complete consciousness continuation solution for AI assistants.

An OmniFocus integration service designed for Claude Desktop, providing task management functions through the MCP protocol, supporting task and project retrieval and intelligent filtering

An intelligent search engine that combines LangChain, MCP protocol, RAG technology, and Ollama, supporting web search, information retrieval, and answer generation, with the ability to call local and cloud LLMs.

Brainrot MCP is a model context protocol server that solves the problem of context loss in AI programming conversations. By storing project decisions, to - do lists, and architectural patterns, it enables persistent memory and intelligent retrieval across conversations.

An intelligent news search MCP server that supports automatic multi - API switching and quota management, and provides multi - language news retrieval services

WisdomForge is an intelligent knowledge management system based on the Qdrant vector database, capable of efficiently storing and retrieving various experiences, insights, and best practices.