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.

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.

Weaviate's MCP Server supports quick installation through Smithery and integration with Claude Desktop, providing vector search and storage functions.

An MCP server that provides any AI model with the ability to intelligently search for Claude Agent Skills through vector embeddings and semantic similarity, enabling progressive skill discovery and cross-platform skill sharing.

A document semantic search service based on the Qdrant vector database, supporting URL and local file imports and providing natural language query functions.

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

The Pinecone MCP Server is a model context protocol server designed for Claude Desktop, providing read and write interaction capabilities with Pinecone indexes.

An MCP service for semantic search of Star Wars planets based on Couchbase

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.

An MCP memory server based on the Qdrant vector database, providing knowledge graph and semantic search capabilities.

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.

A high - performance MCP server for semantic code search, written in Rust, supporting hybrid search (BM25 + vector embedding), AST intelligent chunking, and over 60 programming languages.

This project is a server based on the Model Context Protocol (MCP), which realizes seamless integration with the Milvus vector database and provides a standardized data access interface for LLM applications. It supports direct invocation of Milvus's search, query, and management functions in applications such as Claude Desktop and Cursor.

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.

A model context protocol server based on LanceDB, providing functions such as vector storage, similarity search, and metadata management

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

A system integrating the Qdrant vector database and MCP server for storing and retrieving code snippets, supporting natural language search and semantic retrieval.

The MCP server project is used to connect to the Weaviate vector database, providing search and storage functions, and supporting the configuration of the development environment through the Claude desktop application.

SQLite MCP Server is an enterprise-level SQLite database enhancement tool that provides 73 dedicated tools, supporting advanced analysis, JSON operations, text processing, vector search, geospatial operations, and intelligent workflow automation. It has AI-native JSON operations and enhanced security.

A Terraform module RAG engine that supports automatic import of multiple repositories, code analysis, vector storage, and semantic search, providing CLI, API, and MCP service interfaces.

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.

The MCP Chat Analysis Server is a service based on the Model Context Protocol (MCP), providing semantic analysis functions for chat conversations, including vector embedding search, knowledge graph construction, and conversation pattern analysis.

The MCP application is a service that combines RAG and web search tools, using OpenAI embedding vector storage, PostgreSQL as the database, and PGVector as the vector storage, supporting knowledge retrieval and document addition functions.

An MCP server that provides email processing functions, integrates MongoDB semantic search and SQLite for efficient storage, and supports Outlook email processing, vector embedding generation, and multi - email account 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.

A code search MCP server based on semantic understanding, using local embedding models and vector databases to achieve intelligent code retrieval, replacing traditional text search tools

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

The Florentine.ai MCP Server allows AI agents to query MongoDB and MySQL databases through natural language, supporting functions such as multi - tenant data isolation, automatic schema exploration, and semantic vector search.

A RAG service based on the Qdrant vector database and Ollama/OpenAI embedding, providing document semantic search and management functions.

An embedded vector search server based on the MCP protocol for querying transcribed segments and embedded vectors in the Turso database. It supports searching for relevant segments by question.

A semantic search service based on the Qdrant vector database and OpenAI embeddings

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).

NexusHub is a unified MCP server designed for the Claude AI tool, offering various functions such as file system access, database operations, vector search, and GitHub integration. It supports both HTTP and stdio protocols and includes a modern dashboard interface.

A Docker Compose solution for quickly deploying the Qdrant vector search engine and MCP protocol server locally

The RAG Document MCP Server is a vector search-based document processing tool that provides document retrieval and context enhancement functions for AI assistants.

The VikingDB MCP Server is an MCP service that provides storage and search functions for the VikingDB vector database, supporting quick access through configuration and data operations using related tools.

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

A semantic codebase search server based on the MCP protocol, using Gemini embeddings and Qdrant vector storage to provide code understanding and search functions for AI editors.

This is a demonstration project of a hybrid retrieval augmented generation (RAG) search system based on MariaDB Cloud. It integrates MariaDB vector search and Brave Search web search enhancement implemented through the FastMCP server, uses the 20 Newsgroups dataset as an example, and provides a plug-and-play hybrid search architecture.

DevContext is a powerful AI development context system that provides developers with project-centric continuous context awareness. It includes four memory types: short-term, long-term, situational, and semantic, supports code structure analysis and vector search, and significantly improves development efficiency.