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    Related Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to watch YouTube videos by extracting frames at scene changes and visual references, pairing each frame with the exact words spoken at that timestamp. Provides dense frame-transcript interleaving for any model.
      19 npm
      5
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables users to turn GIFs, local videos, and Bilibili videos into timestamped, queryable, and verifiable frame evidence and event timelines, while passing extracted frames to the current session's model instead of calling an external vision API.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI assistants to generate, edit and render finished, ready-to-post videos from a plain-language prompt, including script, visuals, voiceover, captions and music for TikTok, Reels and YouTube. Also supports remaking reference videos, importing your own media, and reviewing or adjusting existing videos before rendering.
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables users to turn YouTube videos into a compounding knowledge base by extracting transcripts, detecting scene changes, generating vision-based frame descriptions, and building a searchable wiki. It supports agentic natural-language querying over the accumulated knowledge for MCP clients like Claude and Copilot.
      -

    TDQS

    A4.3/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool targets a distinct stage of the pipeline: watch_video ingests, get_transcript and get_frames retrieve derived artifacts, search_memory queries across the whole cache, and list_watched inventories what exists. The descriptions explicitly cross-reference each other and state when NOT to use a tool (e.g. search_memory over watch_video for already-seen content). The transcript-vs-frames split is clearly signposted by modality and context cost.

    Naming Consistency5/5

    All five tools use a consistent snake_case verb_noun pattern (get_transcript, watch_video, get_frames, search_memory, list_watched). Retrieval verbs are used consistently: get_ for fetching an artifact from a known source, list_/search_ for exploration, watch_ for the domain's primary action.

    Tool Count5/5

    Five tools is tightly scoped for a video-ingestion-and-recall server, with each tool earning its place as a distinct step (ingest, frames, transcript, search, inventory). No obvious redundancy or bloat.

    Completeness4/5

    The core lifecycle (watch, page frames, get text, search across videos, list cache) is fully covered with no dead ends for the main workflow. Minor gaps: search_memory accepts kind="note" but no tool can write notes, and there is no way to delete/forget or re-index a stale video other than re-watching it.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues