"Vector Database" matching MCP servers:

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

A production-grade MCP server for Oracle database interaction, providing 11 tools for full CRUD, transactions, execution plans, and comprehensive safety guardrails.

Read-only MySQL/MariaDB MCP server for running SELECT queries safely, with automatic read-only enforcement and query limits.

MCP server for Microsoft SQL Server that lets LLMs explore schema and execute read-only SELECT queries, with optional stored procedure execution when explicitly enabled.

Enables AI agents to format SQL, explain queries in plain English, analyze schemas, build queries from natural language, and generate migrations, all without requiring a database connection.

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

Enables secure read-only querying of multiple database types (MySQL, PostgreSQL, MSSQL, Oracle) through natural language. Automatically reads database configuration from project files and blocks any data modification operations for safety.

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

Integrates Redshift database query capabilities with vector-based knowledgebase tools for semantic search and RAG applications. It enables users to execute SQL queries, explore database schemas, and perform hybrid semantic searches on markdown files stored in S3.

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.

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.

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.

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 interaction with Cloudflare D1 databases through natural language by providing tools to list tables and execute SQL queries. Uses Cloudflare's REST API for lightweight database operations without requiring direct database connections.

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.

A Model Context Protocol server that enables LLMs to interact with databases (currently MongoDB) through natural language, supporting operations like querying, inserting, deleting documents, and running aggregation pipelines.

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.

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

MCP server providing a single query tool to execute SQL against GetDot Database, enabling schema discovery, data loading, and querying via natural language.

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

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

Enables AI agents to query and explore PostgreSQL and MySQL clusters from a single main configuration, with dynamic per-database routing, automatic database discovery and sync, table/schema inspection, and hierarchical write-rule protection that can block or allow specific SQL mutation operations.

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.

A Model Context Protocol (MCP) Server that allows AI models to securely interact with data hosted in Azure Database for PostgreSQL. It enables natural language querying, schema exploration, and data management through MCP clients like Claude Desktop and Visual Studio Code.