MCPs tagged with vector search
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RAG Documentation MCP Server
Vector-based documentation search and context augmentation for AI assistants
RAG Documentation MCP Server provides vector-based search and retrieval tools for documentation, enabling large language models to reference relevant context in their responses. It supports managing multiple documentation sources, semantic search, and real-time context delivery. Documentation can be indexed, searched, and managed with queueing and processing features, making it highly suitable for AI-driven assistants. Integration with Claude Desktop and support for Qdrant vector databases is also available.
- ⭐ 238
- MCP
- hannesrudolph/mcp-ragdocs
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ApeRAG
Hybrid RAG platform with MCP integration for intelligent knowledge management
ApeRAG is a production-ready Retrieval-Augmented Generation (RAG) platform that integrates graph-based, vector, and full-text search capabilities. It enables the construction of knowledge graphs and supports MCP (Model Context Protocol), allowing AI assistants direct interaction with knowledge bases. Features include advanced document parsing, multimodal processing, intelligent agent workflows, and enterprise management tools. Deployment is streamlined via Docker and Kubernetes, with extensive support for customization and scalability.
- ⭐ 920
- MCP
- apecloud/ApeRAG
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Weaviate MCP Server
A server implementation for the Model Context Protocol (MCP) built on Weaviate.
Weaviate MCP Server provides a backend implementation of the Model Context Protocol, enabling interaction with Weaviate for managing, inserting, and querying context objects. The server facilitates object insertion and hybrid search retrieval, supporting context-driven workflows required for LLM orchestration and memory management. It includes tools for building and running a client application, showcasing integration with Weaviate's vector database.
- ⭐ 157
- MCP
- weaviate/mcp-server-weaviate
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mcp-server-qdrant
Official Model Context Protocol server for seamless integration with Qdrant vector search engine.
mcp-server-qdrant provides an official implementation of the Model Context Protocol for interfacing with the Qdrant vector search engine. It enables storing and retrieving contextual information, acting as a semantic memory layer for LLM-driven applications. Designed for easy integration, it supports environment-based configuration and extensibility via FastMCP. The server standardizes tool interfaces for managing and querying contextual data using Qdrant.
- ⭐ 1,054
- MCP
- qdrant/mcp-server-qdrant