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FiloHany

Video RAG MCP Server

by FiloHany

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    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables agents to analyze long videos by downloading them, extracting transcripts and storyboards, and zooming into specific moments with high-resolution frames and OCR.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to turn video links from YouTube, TikTok, Instagram, X and Vimeo into usable context: transcripts, summaries, chapters, and searchable answers with timestamped links to the exact moment. All tools are read-only, and the hosted server needs no account or API key to start.
      1
      MIT

    TDQS

    A3.5/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: ingest_data_tool loads data into an index, retrieve_data_tool queries the index for information, and show_video_tool generates video chunks from retrieved metadata. The descriptions clearly differentiate their functions, making misselection unlikely.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with snake_case naming: ingest_data_tool, retrieve_data_tool, and show_video_tool. This predictable naming convention makes the tool set easy to understand and navigate.

    Tool Count3/5

    With only 3 tools, the set feels thin for a video RAG system. While the core operations (ingest, retrieve, show) are covered, typical RAG workflows might benefit from additional tools like index management, query refinement, or batch processing. The count is borderline but functional.

    Completeness4/5

    The tools cover the essential RAG lifecycle: ingestion, retrieval, and video generation. However, there are minor gaps such as missing update/delete operations for the index, query history, or configuration tools. Agents can work around these, but the surface is not fully comprehensive.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues