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KFabric

by fbailleux2
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Python
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    Related Servers

    • Modular RAG MCP Server

      XBW-Leo
      A
      license
      Not graded
      quality
      C
      maintenance
      A modular RAG framework exposing knowledge retrieval tools via MCP, enabling AI assistants to perform hybrid search, reranking, and multimodal document queries with full observability and evaluation.
      MIT

    • knowledge-index

      gustavoali
      A
      license
      Not graded
      quality
      C
      maintenance
      A private, self-hosted RAG service over MCP that enables document ingestion, hybrid retrieval (BM25 + dense vectors fused with RRF), and notebook management through 14 tools, keeping documents on your own hardware.
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    • Modular RAG MCP Server

      zhtong-star
      A
      license
      Not graded
      quality
      C
      maintenance
      A modular RAG framework exposing MCP tools for hybrid document search, enabling AI assistants to query private knowledge bases with multimodal support and full observability.
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    • Cortex

      VBlackJack
      A
      license
      Not graded
      quality
      A
      maintenance
      A local RAG MCP server that provides semantic search over a knowledge base (documents and Confluence) in French and English, with incremental indexing and tools for syncing and freshness. It integrates with AI clients like Claude, Codex, and Gemini while keeping data local.
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      1
      Apache 2.0
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3/5.0

    Scored across 13 tools

    Disambiguation5/5

    Each tool targets a distinct step in the document processing pipeline, from query creation to corpus building. There is minimal overlap between tools like analyze_document and accept_document, which serve different stages.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (e.g., create_query, discover_documents). The style is uniform with no mixing of conventions.

    Tool Count5/5

    The 13 tools are well-scoped for a document collection and analysis pipeline. Each tool earns its place, covering discovery, collection, analysis, decision, and corpus assembly.

    Completeness4/5

    The core workflow is fully covered, but missing tools for listing queries or deleting candidates/corpora are minor gaps. Overall, the surface is nearly complete for the stated purpose.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources