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  • A
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    Provides token-efficient semantic search and document retrieval by indexing PDFs, text, and markdown files into local notebooks using ChromaDB. It enables AI agents to query relevant passages from large documents through local embedding models like Hugging Face or Ollama.
    1
    MIT
  • F
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    quality
    B
    maintenance
    Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
    4
  • F
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    quality
    B
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    Enables users to search and retrieve research datasets via natural language queries, with tools for proposing and committing tag changes. Includes robust authorization, idempotent mutations, and an audit trail.
  • F
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    An MCP server that fetches real-time documentation for popular libraries like Langchain, Llama-Index, MCP, and OpenAI, allowing LLMs to access updated library information beyond their knowledge cut-off dates.
    1
    3
  • F
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    Enables querying and analyzing user, product, and order data with filtering capabilities and real-time statistics. Supports WebSocket connections to XiaoZhi AI platform with automatic reconnection.
  • F
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    quality
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    maintenance
    Automatically routes natural language questions to RAG or Text2SQL paths to answer queries about virtual construction site data, supporting semantic search and structured aggregation.
  • F
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    quality
    A
    maintenance
    Enables local data discovery by indexing metadata from SQLite, CSV, and Markdown sources, providing hybrid keyword and TF-IDF semantic search via MCP tools (listSources, indexSource, search, getSchema) and a REST API.
  • F
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    quality
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    maintenance
    Multi-datasource MCP server that connects AI assistants to 6 database types (MySQL, PostgreSQL, ClickHouse, MongoDB, SQLite, Huawei DWS) with dynamic configuration, encrypted credential storage, and schema discovery.
  • A
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    quality
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    Enables fully local retrieval over a personal document corpus via hybrid search, cross-encoder reranking, RAPTOR summaries, and knowledge graph queries, served to AI agents over MCP.
    MIT
  • A
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    quality
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    maintenance
    A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.
    MIT
  • A
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    quality
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    Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.
    MIT