A commercial MCP server offering semantic search and retrieval-augmented generation (RAG) for LLMs, with support for relevance-ranked context delivery and open-source reference implementation.
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Email MCP server supports integration with multiple email providers and enables AI-powered email operations via the Model Context Protocol.
A Pinecone MCP server providing vector search capabilities over Pinecone through the Model Context Protocol.
Provides AI models with access to PostgreSQL databases through an MCP server, automating data management and analysis.
The Qdrant MCP Server integrates with the Qdrant vector search engine, allowing AI agents to store and retrieve semantic information using MCP, ideal for advanced AI memory and retrieval tasks.
A purpose-built MCP server that enables semantic data retrieval via vector embeddings, allowing AI systems to perform meaning-based searches in large datasets. Qdrant is a leading example, offering standard protocols for vector operations.
Vectorize MCP Server enables semantic search and retrieval using natural language queries, optimized for performance on large-scale vector data sets. It supports customizable parameters like result counts and integrates Approximate Nearest Neighbors algorithms for efficient similarity matching. Highly relevant as a specialized MCP server for vector operations.
Vectara MCP Server is a commercial server that provides agentic AI applications with fast and reliable Retrieval-Augmented Generation (RAG) capabilities. It integrates with the Model Context Protocol (MCP) and leverages Vectara's semantic search and trusted RAG platform. The server supports relevance-ranked context delivery and offers an open-source reference implementation.
claude_desktop_config.json)ask_vectara and search_vectara)semantic-search, rag, ai-integration, open-source
database-messaging-mcp-servers