"Vector Database" matching MCP servers:

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

Integrates RAG into AI agents via MCP Server, supporting multiple vector database technologies for collection management and search operations.

Combines Neo4j graph database with vector search using OpenAI embeddings for intelligent semantic search across knowledge graphs.

MCP server that parses 3GPP .docx specifications into structured sections with full-text search, letting LLMs browse, search, and follow cross-references without a vector database.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

Indexes local Markdown/text files into a SQLite database with vector embeddings and provides MCP tools for semantic search without cloud dependencies.

Enables any MCP client to index files, directories, and arbitrary text into a local SQLite-backed vector database and perform semantic search with language-aware chunking, filters, and context expansion, all offline without network calls.

Enables LLM agents to access a private knowledge base through MCP by automatically chunking and indexing .txt documents, with zero configuration and no Docker or vector database required.

An MCP server that integrates with LangChain and ChromaDB to provide documentation search for AI libraries and vector database management.

Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.

Model Context Protocol server for RosalindDB, enabling AI clients to create datasets, ingest vectors, run similarity queries, and check usage on a cost-optimized vector search database.

Enables AI assistants to connect to and interact with PostgreSQL, MySQL, SQLite, and MongoDB databases through natural language, supporting schema exploration, query execution, data export, and more.

MCP server that allows Claude AI to interact directly with MySQL databases, enabling query execution and table information retrieval through natural language.

A Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.

Enables access to comprehensive vehicle information including VIN decoding, license plate OCR, vehicle history checks (theft, title, salvage records), market valuations, specifications, and warranty data for vehicles across North America and Europe.

An example server that enables interaction with Alibaba Cloud's Lindorm multi-model NoSQL database, allowing applications to perform vector searches, full-text searches, and SQL operations through a unified interface.

Enables LLMs to interact with Rememberizer Vector Store for semantic search, document creation, deletion, and modification.

Enables natural language querying of MongoDB data by transforming AI agent questions into MongoDB aggregations. Supports secure data separation, semantic vector search, and advanced lookup capabilities for database interactions.

Enables AI agents to securely interact with multiple databases (MySQL, PostgreSQL) via natural language queries, with cross-database querying and enterprise-grade security.

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

Turns your Readwise library into a blazing-fast semantic search engine with a streaming MCP server for LLM clients, supporting vector search, nightly syncs, and deployment on serverless platforms like Vercel.

Provides MCP tools to interact with Milvus vector database, enabling vector search, text search, hybrid search, and collection management.

Semantic search server for code and documentation using Qdrant vector database. Supports multi-language indexing, live updates, and natural language queries.

Enables indexing and semantic search of software project files via MCP, with automatic file watching for updates.

Enables querying a curated book database using MCP tools to retrieve basic or detailed book information by ISBN or title, including batch lookups.

Automatically discovers vector indexes in a Neo4j database and exposes each as a semantic search tool with metadata pre-filtering, enabling natural language queries with dynamic filter support.

Enables searching, indexing, and recovering past AI chat sessions and agent actions from local AI coding tools, with optional vector database semantic search and direct filesystem access.

Enables AI assistants to query PDF documents by ingesting them into a vector database and generating answers grounded in the actual documents.

Connect your LLMs to SQL databases safely and intuitively using the Model Context Protocol (MCP). NLP Database acts as a secure, read-only bridge that allows AI agents to explore schemas and query data using natural language.

MCP to index techincal docs on local vector database to work with Cursor