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.

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.

Claude local semantic code search tool that uses the EmbeddingGemma model to achieve fully offline intelligent multi - language code search. Integrates with Claude Code through the MCP protocol, protects privacy, and does not require an API key.

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 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.

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

A code semantic search MCP server based on PostgreSQL and Voyage embedding model, providing code snippet search, file list, and content retrieval functions

An MCP service for semantic search of Apple Notes, supporting local embedding models, full-text search, and vector storage.

An MCP server project that provides a service for automatically selecting the OpenAI O3 or Google Gemini 2.5 Pro model based on the number of tokens, supports recursive embedding of file paths in prompts, and is suitable for code review and solving complex problems.

A semantic code search server based on the MCP protocol, supporting two embedding models, OpenAI and Ollama, capable of indexing local projects or Git repositories, and providing an enterprise - level private code search solution.

A local vector database MCP server based on FAISS, providing document embedding, semantic search, and RAG functions, supporting multiple document formats and custom embedding models.

The Insights Knowledge Base (IKB) MCP Server is a plug-and-play free knowledge base with over 10,000 high-quality insight reports built-in. It supports local secure storage and private document parsing. The project has optimized data processing efficiency, provides weekly report updates, and plans to integrate embedding models and enhance the report system in the future.

A Machine Control Protocol (MCP) server based on the Qdrant vector database, supporting text storage, semantic search, and integration of the FastEmbed embedding model.

A Node.js-based vector search project that uses the LanceDB database and Ollama embedding model to implement document similarity search functionality

An MCP server implemented for the OpenAI API, providing a standardized interface to connect Augment with OpenAI language models, supporting functions such as chat completion, model listing, and embedding generation.

Brain Server is a knowledge embedding and vector search service based on the MCP protocol, providing high-quality text vectorization, semantic search, and knowledge management functions, supporting multiple embedding models and Docker deployment.

mcp - rag - server is a Retrieval Augmented Generation (RAG) server based on the Model Context Protocol (MCP). It provides relevant context for connected LLMs by indexing project documents. It uses ChromaDB and Ollama for local storage and embedding generation, supports multiple file formats, and can be quickly deployed using Docker.

A RAG system built with open-source embedding models, vector databases, and the Gemini large language model, supporting local document processing and dynamic index update.

This is a PostgreSQL semantic search server based on the Model Context Protocol (MCP) standard. It enables AI assistants to understand the semantic structure of the database and execute natural language queries through vector embedding technology.

This is an MCP Memory Service implemented in Rust, providing memory storage and retrieval functions, supporting multiple storage backends and embedding models, and communicating with clients via the JSON-RPC protocol.

This project builds an AI system based on Nasdanika capabilities, focusing on operating on resource collections (interconnected models). It describes model elements and their relationships from multiple angles through the 'narrator' processor, and uses embeddings and vector storage to implement semantic search and RAG (Retrieval - Augmented Generation). It also supports the chat completion functions of OpenAI and Ollama.

VectorCode is a code repository indexing tool designed to optimize the prompt construction of large programming language models (LLMs) by indexing and providing code repository information. It supports multiple embedding engines, provides command - line tools and Neovim plugins to help developers more efficiently use project context to improve the quality of model output.