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  • A
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    Self-hosted knowledge backend for AI agents. Provides 11 MCP tools for hybrid vector + keyword search, container-isolated knowledge bases, and 4 storage connectors (S3, Azure Blob, MinIO, filesystem). Built with .NET, runs via Docker.
    11
    15
    MIT
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    Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.
    2
    2
    MIT
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    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.
    7
    MIT
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    An MCP server for OpenServerless that exposes action tools for creating, invoking, and managing API endpoints with integrated services like S3, PostgreSQL, Redis, and Milvus.
    8
    1
    Apache 2.0
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    quality
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    Enables Claude to interact with core AWS services like S3, EC2, RDS, and CloudWatch, along with a generic SDK wrapper for any AWS operation. It also supports cost monitoring and optional vector store capabilities for document ingestion and search.
    10
    3
    The Unlicense
  • A
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    An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.
    12
    7
    2
    MIT
  • A
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    Enables storing and retrieving information using semantic search with Qdrant vector database. Acts as a memory layer for LLMs to persistently store and semantically search through information and metadata.
    Apache 2.0
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    MCP server for a self-hosted RAG system that enables AI tools to search and retrieve grounded answers from locally ingested documents via MCP tools, with local embeddings and no API key required.
    MIT
  • A
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    Enables AI agents to interact with TigerGraph databases through the Model Context Protocol, supporting graph operations, schema queries, and GSQL execution via natural language.
    Apache 2.0
  • A
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    A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.
    1
    MIT
  • F
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    A FastAPI-based application that enables document embedding and semantic retrieval using Qdrant vector database, allowing users to convert documents into embeddings and retrieve relevant content through natural language queries.