"Bridging DeepSeek Chat with MCP Tools for Local Hardware Development" matching MCP servers:

A conversational chat interface that uses RAG over a synthetic test corpus and a domain-knowledge corpus, exposing retrieval via MCP.

MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.

Enables read-only semantic search over a local document corpus with on-device embeddings and a local Chroma store, featuring symlink-hardened file access and structured error handling.

Facilitates semantic analysis of chat conversations through vector embeddings and knowledge graphs, offering tools for semantic search, concept extraction, and conversation pattern analysis.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

Enables AI tools to search and manage a private local knowledge base via MCP or HTTP, using local Chinese semantic retrieval without sending data externally.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

A local-first MCP server providing persistent, project-scoped and global memory for AI agents using ChromaDB and Ollama embeddings, fully offline.

Enables semantic search across Apple Mail, Messages, Calendar, and Contacts on macOS using natural language queries. All processing happens locally with privacy-first vector indexing for fast similarity search.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

Enables MCP clients to search locally indexed text files, PDFs, and images using natural-language descriptions, and to index folders and inspect index statistics.

A local-first MCP server for indexing and searching codebases using PostgreSQL and Ollama embeddings, ensuring complete privacy and zero API costs.

A fully local, self-hosted memory server for MCP clients (Claude Code, Cursor, etc.) that provides persistent memory storage with semantic search, using local embeddings and a local Qdrant vector store.

A RAG knowledge base MCP server using FastMCP and LanceDB, enabling hybrid search, document ingestion, and full MCP tool/resource/prompt support.

A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.

Enables semantic search across indexed documents using vector embeddings. Index GitHub repositories and URLs to perform natural language queries with AI-enhanced contextual results.

Enables querying hardware manuals (PDF/HTML) using local RAG with ChromaDB and embeddings, allowing Claude Code to look up pinouts, registers, and specifications.

Enables LLM clients to chat with locally indexed markdown engineering documentation through semantic search, keeping all indexing and retrieval on-device for privacy.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

A privacy-preserving local RAG system integrated with MCP, enabling natural language queries over ingested documents and a SQLite database through vector search and local database tools.

Enables semantic search over your Cursor IDE chat history by vectorizing prompts and storing them in LanceDB. Provides a Dockerized API to perform vector similarity searches against your chat history.

MCP server enabling Claude Desktop to answer questions from local Word and PDF documents by searching a vector index built from their contents.

A fully offline local RAG server that utilizes ChromaDB and Ollama to index and query PDF, text, and Markdown documents. It allows users to manage local knowledge bases and perform semantic searches with AI-generated responses.

MCP server that enables searching a TOTVS Protheus ERP knowledge base using vector similarity and reranking.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

Provides local vector database functionality using FAISS for document ingestion, semantic search, and Retrieval-Augmented Generation (RAG) applications with persistent storage and customizable embedding models.

Provides AI assistants with persistent memory through local ChromaDB vector storage, featuring automated file ingestion and batch processing for over 70 file types. It enables advanced vector search, EXIF metadata extraction for photos, and duplicate file detection across local directories.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.