Haiku RAG is an intelligent retrieval - augmented generation system built on LanceDB, Pydantic AI, and Docling. It supports hybrid search, re - ranking, Q&A agents, multi - agent research processes, and provides local - first document processing and MCP server integration.

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.

An MCP server that provides framework documentation retrieval for Claude Code, supporting real - time document access, intelligent caching, and context enhancement features for over 85 frameworks.

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.

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.

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.

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

A locally prioritized and agent-agnostic model context protocol server based on the Auggie SDK, providing codebase semantic search, file retrieval, intelligent planning, code review, and cross-session memory functions. It supports integration with multiple MCP clients.

The Internet Search MCP is a service encapsulated based on Tencent Cloud's Internet Search API, providing intelligent search capabilities with millisecond - level response and minute - level updates, supporting multiple functions such as natural result retrieval and multi - modal VR cards, helping developers quickly integrate Internet search capabilities.

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.

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

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.

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.

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.

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.

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.

A MCP server for intelligent access to React Native Godot documentation, providing features such as document retrieval, code examples, troubleshooting, and API references, and supporting workflow oriented developer tools.

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.

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.

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

An MCP server specifically designed for AI agents, connecting the Neo4j graph database with intelligent agents, providing memory storage, retrieval, and association functions based on knowledge graphs.

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

Dolphin-MCP is a semantic code search server based on the Model Context Protocol, enabling intelligent retrieval of code and documentation across repositories through the Dolphin API. It supports parallel snippet retrieval and various filtering options.

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.

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.