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  • grafeo-mcp by GrafeoDB

    grafeo-mcpofficial

    GrafeoDB
    A
    license
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    quality
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    maintenance
    Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.
    Last updated
    23
    4
    Apache 2.0

  • Agentic RAG MCP

    enached134-ctrl
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    quality
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    maintenance
    A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
    Last updated
    3
    1
    MIT
  • Tribal by tribal-memory

    Tribalofficial

    tribal-memory
    F
    license
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    quality
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    maintenance
    Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
    Last updated
    10
    8
  • ArcadeDB Multi-Model DBMS by ArcadeData

    ArcadeDB Multi-Model DBMSofficial

    ArcadeData
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    license
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    quality
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    ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.
    Last updated
    10
    1,040
    Apache 2.0

  • mcp-ragchat

    gogabrielordonez
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    quality
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    maintenance
    An MCP server that enables RAG-powered AI chat integration for websites by crawling content, building local vector stores, and generating embeddable chat widgets. It simplifies the setup of local chat servers with support for various LLM and embedding providers.
    Last updated
    5
    3
    2
    MIT

  • velesdb-memory

    cyberlife-coder
    F
    license
    A
    quality
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    maintenance
    Local-first agent-memory MCP server with a why() tool: recall a fact together with its connected subgraph (multi-hop), so linked memories surface even when they share no words with the query. remember/recall/relate/forget/why over one fused vector + graph + columnar engine a single offline Rust binary.
    Last updated
    8
    76

  • food-recipe-mcp

    AIDataNordic
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    license
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    quality
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    maintenance
    A production-grade semantic search server for food recipes — built for AI agents using the Model Context Protocol (MCP). Search across 50,000+ recipes with hybrid dense + sparse retrieval and cross-encoder reranking.
    Last updated
    2
    1
    MIT

  • Typesense MCP Server

    avarant
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    license
    A
    quality
    B
    maintenance
    A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.
    Last updated
    14
    9
    MIT
  • Baidu Vector Database MCP Server by baidu

    Baidu Vector Database MCPofficial

    baidu
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    license
    B
    quality
    D
    maintenance
    A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.
    Last updated
    14
    3
    Apache 2.0

  • MindsDB MySQL MCP Server

    nikhgupta
    F
    license
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    quality
    D
    maintenance
    Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.
    Last updated
    11

  • MCP Tooling Lab

    kaypon
    F
    license
    B
    quality
    D
    maintenance
    A Node.js-based MCP server that enables AI agents to generate embeddings, index documents, and perform semantic vector searches using OpenAI and Chroma. It facilitates the creation of retrieval-augmented generation (RAG) pipelines for internal knowledge assistants and document-based workflows.
    Last updated
    3