# Vector Search

> Browse 16 items tagged with Vector Search.

## Items

- [Couchbase MCP Server](https://mcpserver.ever.works/tr/items/couchbase-mcp-server) — Model Context Protocol server for Couchbase, an award-winning distributed NoSQL cloud database offering vector search for GenAI-ready applications. Delivers hyperscale vector indexing at billion-scale with 350+ times faster performance than MongoDB.
- [Weaviate MCP Server](https://mcpserver.ever.works/tr/items/weaviate-mcp-server) — Official Weaviate vector search MCP server enabling AI agents to interact with Weaviate vector database through hybrid search, semantic search, and keyword search capabilities via the Model Context Protocol.
- [MCP Qdrant Memory](https://mcpserver.ever.works/tr/items/mcp-qdrant-memory) — MCP server providing a knowledge graph implementation with semantic search capabilities powered by Qdrant vector database, enabling AI assistants with persistent, searchable memory.
- [Azure AI Search MCP Server](https://mcpserver.ever.works/tr/items/azure-ai-search-mcp-server) — Model Context Protocol Servers for Azure AI Search, providing MCP tools for searching and indexing with Azure AI Search.
- [Chroma MCP Server](https://mcpserver.ever.works/tr/items/chroma-mcp-server) — A Model Context Protocol server implementation that provides database capabilities for Chroma, enabling AI assistants to interact with Chroma vector databases for embeddings and semantic similarity.
- [Exa MCP](https://mcpserver.ever.works/tr/items/exa-mcp) — Claude can perform Web Search | Exa with MCP (Model Context Protocol).
- [Qdrant MCP Server](https://mcpserver.ever.works/tr/items/qdrant-mcp-server) — Qdrant MCP Server integrates Qdrant's vector database with MCP for semantic search and similarity matching in AI workflows. It supports vector embeddings, collections management, and hybrid search combining dense and sparse vectors. Ideal for RAG pipelines, recommendation systems, and knowledge retrieval in agents; offers an open-source, self-hosted alternative to Pinecone MCP.
- [SearXNG](https://mcpserver.ever.works/tr/items/searxng) — Model Context Protocol Server for SearXNG, enabling privacy-focused metasearch.
- [Vector Search MCP Server](https://mcpserver.ever.works/tr/items/vector-search-mcp-server) — MCP server for semantic data search enabling embedding processing, similarity matching, and data partitioning. Setup involves environment variables, embedding model, and collection creation, with integration support for various embedding models.
- [MariaDB](https://mcpserver.ever.works/tr/items/mariadb) — MCP server providing a standard interface for managing and querying MariaDB databases, supporting both standard SQL operations and advanced vector/embedding-based search.
- [MCP Memory libSQL](https://mcpserver.ever.works/tr/items/mcp-memory-libsql) — High-performance persistent memory system for MCP powered by libSQL, featuring vector search, semantic knowledge storage, and efficient relationship management for AI agents and knowledge graph applications.
- [Pinecone Assistant](https://mcpserver.ever.works/tr/items/pinecone-assistant) — MCP server that retrieves context from your Pinecone Assistant knowledge base, enabling AI agents to access vector-stored knowledge.
- [txtai Assistant MCP](https://mcpserver.ever.works/tr/items/txtai-assistant-mcp) — MCP server implementation for semantic vector search and memory management using TxtAI, providing robust API for storing, retrieving, and managing text-based memories with semantic vector database search.
- [Atlas Vector Search Docs MCP Server](https://mcpserver.ever.works/tr/items/atlas-vector-search-docs-mcp-server) — A Model Context Protocol server providing semantic search and document retrieval using MongoDB Atlas Vector Search with Voyage AI embeddings. Enables intelligent querying across markdown documentation with hierarchical chunking and contextual understanding.
- [PGVector MCP Server](https://mcpserver.ever.works/tr/items/pgvector-mcp-server) — A Model Context Protocol server providing semantic search capabilities for PostgreSQL databases using vector embeddings. Performs similarity searches, metadata filtering, and document insertion with automatic embedding generation using pgvector extension.
- [Vector MCP Server](https://mcpserver.ever.works/tr/items/vector-mcp-server) — A unified Model Context Protocol server supporting multiple vector databases including ChromaDB, Couchbase, MongoDB, Qdrant, and PGVector. Enables hybrid search (lexical/vector) for document information and retrieval augmented generation (RAG) applications.

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_Canonical page: https://mcpserver.ever.works/tr/tags/vector-search_