"Vector Database" matching MCP servers:

Turn SEC EDGAR filings into a searchable vector database, enabling natural language queries over company filings through Claude Desktop.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

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

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

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

Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.

Enables querying the Health Canada Drug Product Database for information on approximately 47,000 approved drugs, including human pharmaceuticals, biologics, veterinary drugs, radiopharmaceuticals, and disinfectants.

Enables AI assistants to interact with a Qdrant vector database by exposing collection, point, vector, payload, snapshot, search, recommendation, discovery, and observability operations as MCP tools.

Turns a tools.yaml contract into typed, tenant-guarded vector database tools that agents can use via Python imports or MCP.

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

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

Enables SQL database interaction with true server-side result paging, runtime-managed connection profiles, and SSH-bridged access for PostgreSQL.

Provides SQLite database analytics operations through MCP, enabling AI assistants to connect, query, explore schema, and export data from SQLite databases.

A Model Context Protocol server that provides real-time database schema information to AI assistants, supporting both low-code system schema queries and traditional database metadata queries.

Loads CSV files into a temporary SQLite database and provides comprehensive data analysis tools via MCP, enabling AI assistants to query, analyze, and export data using natural language.

Enables a single MCP server for interacting with multiple vector databases through pluggable backends, starting with Pinecone. It supports schema-aware index management, upserts, hybrid search, embeddings, TTL conventions, and namespace operations.

A shared, persistent MCP memory server for coding agents that enables storing and retrieving project decisions and context across different tools like Claude Code, Codex, and Cursor using semantic vector search.

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

Enables users to architect, validate, scaffold, and generate production-ready code for Milvus vector database applications. Exposes tools for schema validation, design pattern search, CRUD generation, vector and range search blueprints, and MCP client configuration.

Enables storing and retrieving semantic memories using LanceDB vector database, with tools to add memories and search by similarity.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

Enables agents to track causal relationships, reconcile concurrent state changes, and coordinate consensus across distributed networks using vector clocks, CRDTs, gossip, and quorum-based protocols.

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

Enables interaction with KDB.AI through natural language for vector database operations, similarity searches, hybrid search, and advanced data analysis.

Enables LLM clients to safely inspect database schemas and run only approved read-only queries and stored procedures, with auditing.

Enables AI assistants to interact with a Qdrant vector database, including embedding generation and vector search operations.

A Model Context Protocol server for agents to search, store, ingest, and maintain data in a Qdrant vector database.

MCP server for Milvus vector database enabling vector search, text search, and hybrid search operations.

Enables interaction with the Turbopuffer vector database, providing tools for managing namespaces, schema configuration, and performing advanced vector queries. It supports secure DAuth-style authentication and covers both v1 and v2 Turbopuffer API endpoints for comprehensive data operations.

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.