Claude Context is an MCP plugin that provides in - depth context of the entire codebase for AI programming assistants through semantic code search. It supports multiple embedding models and vector databases to achieve efficient code retrieval.

Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.

The Crawl4AI RAG MCP Server is an AI agent service integrating web crawler and RAG functions, supporting smart URL detection, recursive crawling, parallel processing, and vector search. It aims to provide powerful knowledge acquisition and retrieval capabilities for AI coding assistants.

Awesome MCP Servers is a comprehensive collection of Model Context Protocol (MCP) servers, covering 7,158 MCP servers divided into 33 categories, including AI integration, art and media, browser automation, cloud services, databases, developer tools, file systems, finance, gaming, hardware, healthcare, infrastructure, knowledge management, location maps, marketing, monitoring, multimedia processing, operating systems, project management, scientific research, security, social media, travel and transportation, utilities, and version control, etc.

The GPT Researcher MCP Server is an AI research server based on the MCP protocol. It can provide high-quality and optimized research results for LLM applications through in-depth web search and verification.

RagRabbit is a self - hosted AI site search and LLM.txt generation tool that supports one - click deployment on Vercel.

An MCP service for document retrieval based on vector search, providing relevant document context for AI assistants to enhance their answering ability

This project creates an AI agent based on the n8n platform that can discover and use Model Context Protocol (MCP) server tools through the Nostr network. The agent searches for available tools by querying Data Vending Machines (DVM) in the network, sends requests, and obtains responses, thereby expanding the functionality of the LLM to use MCP tools that are not installed.

An EigenLayer MCP service based on Next.js that provides documentation support for AI assistants and can run locally or be deployed on Vercel

An offline documentation MCP server for Garmin Connect IQ SDK, providing complete access to version 8.2.3 documentation for AI assistants, including API documentation, programming guides, device references, and troubleshooting resources.

An IaC component memory enhancement server that provides persistent storage for Claude AI, focusing on version tracking and relationship mapping of Terraform and Ansible resources

This project is a Python server based on the MCP protocol, providing functions for querying and calculating soil and water conservation technical specifications, including engineering calculations such as Manning's coefficient, earth pressure coefficient, drainage ditch flow velocity, and slope stability, as well as the conversion between latitude and longitude and UTM/TWD97 coordinates in Taiwan region. It is suitable for AI application integration.

The project aims to connect RAG applications to open-webui through the MCP protocol to achieve model context interaction, including basic PoC verification, knowledge base integration, and client docking.

GoPluto MCP is a tool that provides real-time service knowledge, routing rules, and code snippets for AI programming assistants, helping developers quickly obtain the latest verified service provider information and code examples.

The Recraft AI MCP Server is a server that integrates MCP clients with Recraft AI's image processing capabilities, supporting functions such as generating and editing raster/vector images, creating custom styles, image vectorization, background processing, and image upscaling.

A remote ChromaDB server based on the Streamable HTTP MCP protocol, providing AI assistants such as Claude with the ability to remotely access the vector database, supporting cross - platform shared memory and semantic search.

The Hex MCP Server is a service that provides real-time Hex package version information for AI tools (such as Cursor), ensuring the use of correct and up-to-date package versions when adding dependencies in Elixir projects.

rag - mcp is an over - designed retrieval - augmented generation system that provides multiple text search modes (semantic search, question - answer search, style search) through a Python server. It uses PostgreSQL and pgvector to store text embedding vectors, supports interaction with AI agents, and has a complex but scalable architecture.

The React Native MCP server is a platform that provides comprehensive development tools, aiming to optimize the React Native development experience. It includes functions such as project initialization, version management, automated upgrades, and Expo integration. It simplifies the development process through AI assistance.

Archon is an intelligent system for autonomously constructing and optimizing AI agents. It demonstrates key principles of modern AI through iterative development, including agent reasoning, domain knowledge integration, and scalable architecture. The current version V4 provides a comprehensive Streamlit interface, supports Docker deployment, and integrates with MCP services for seamless docking of the AI IDE.

Mobi MCP Server is a service based on the Model Context Protocol, used to enable the interaction between AI models and the Mobi knowledge management platform, providing functions such as search, data retrieval, content creation, and version control.

MCP Email Verify is a lightweight server based on the Model Context Protocol (MCP) for real-time verification of email address validity. It checks email format, domain name validity, and deliverability through AbstractAPI and is suitable for integration into AI applications such as Claude Desktop. The project offers simple deployment and MCP compatibility, supporting Python environments.

This is a project that converts the Python version of the MySQL MCP server to a Java implementation, providing a secure MySQL database interaction interface that allows AI assistants to access database resources in a controlled manner.

A memory system that enhances AI programming capabilities through multi-type database integration, including vector databases, SQLite, and knowledge graphs, for storing and retrieving code patterns, algorithms, and relationships between programming concepts.

An HTTP server based on the MCP protocol, providing AGENTS.md knowledge management, structured task tracking, version history recording, and scratchpad functions, supporting multi - user collaboration and AI sub - agent calls.

A document retrieval system based on MongoDB Atlas vector search and Voyage AI embedding technology, supporting semantic search and text matching, including document chunking, embedding generation, and storage functions.

This project is a document processing server based on the Model Context Protocol (MCP) standard. By building a vector database and an MCP interface, it enables AI assistants to access external document resources and break through the knowledge limitations of large language models. The project includes two major components: a document processing pipeline and an MCP server. It supports multiple embedding models and file formats and can be applied to scenarios such as querying the latest technical documents and understanding private code libraries.

The Chroma MCP Server is a model context protocol server designed for the Chroma vector database, aiming to provide persistent working memory functions for AI-assisted development, supporting cross-session context storage and semantic retrieval.

An MCP server project that supports interaction between AI assistants and the macOS version of the ChatGPT desktop application, enabling cross - application command sending and response retrieval.

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.

Context Lens is a local semantic search tool that can convert any content into a searchable knowledge base, enabling AI assistants to understand the meaning rather than just match keywords. It uses a built - in LanceDB vector database, supports local files, GitHub repositories, and URL content, does not require an API key or cloud service, and processes data completely locally.

VkRunner is a Vulkan shader testing tool based on Piglit's shader_runner, aiming to support test scripts as similar as possible to Piglit's shader_test format. It supports writing shaders in GLSL or SPIR-V and provides a rich set of test commands to verify shader behavior, including drawing, computing, and probe detection functions.

The MCP Toolkit is a modular server implementation for building high-precision vertical AI agents, providing support for 123 tools, significantly reducing the amount of code, and simplifying complex workflows.

The You.com DX Toolkit is an open-source AI development toolkit that includes npm packages, plugins, and skills, and can work in conjunction with more than 20 AI programming assistants such as Claude Code and Cursor. It provides tools such as the MCP server, Vercel AI SDK plugin, and Teams.ai integration, supporting real-time web search, AI agents, and content extraction functions.

The MCP Document Indexer is a Python - based local document indexing and search server that uses the LanceDB vector database and a local LLM (through Ollama) to implement real - time monitoring, multi - format document processing, and semantic search, and provides tools for AI assistants such as Claude through the Model Context Protocol (MCP).

The Athena Protocol is an intelligent MCP server acting as a technical leader for AI programming agents, providing expert verification, impact analysis, and strategic guidance. It helps AI agents detect problems, validate assumptions, and optimize solutions before code changes, thereby improving code quality, reducing regressions, and making more informed architectural decisions.

This project is an MCP server based on Google Cloud Vertex AI Gemini model, providing a rich toolset for code assistance and general Q&A, supporting web search-enhanced answers, document explanation generation, file system operations, and other functions.

The Product Requirement Prompt (PRP) is a structured prompt that combines the goal and rationale parts of a traditional Product Requirement Document (PRD) and adds AI key layers (context, implementation details and strategies, verification gates), providing everything needed for an AI coding agent to build a working software vertical slice.

An MCP server for document retrieval and processing based on vector search, providing document enhancement functions for AI assistants

An MCP server that provides relevant Ethereum Improvement Proposals (EIP) content for AI agents through semantic search, supporting Markdown document processing and vectorized retrieval.