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Related Servers

Alternatives to moreel

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    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to research and analyze public short-form video content on Instagram Reels, TikTok, and YouTube Shorts for trend spotting, creator research, competitive briefs, and content ideation.
      1
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Enables AI agents to extract TikTok video and soundtrack media, metadata, covers, and manifests from public URLs or IDs through verified downloads and schema access via MCP tools.
      4
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables MCP clients such as Claude and Copilot to turn YouTube videos into a persistent, compounding wiki by extracting knowledge from transcripts and scene-change vision analysis. Lets clients run agentic search over that compiled knowledge, answering questions with source citations and awareness of what the wiki does not yet cover.
      -
    • A
      license
      Not graded
      quality
      C
      maintenance
      Viral-content intelligence for AI agents — 7 read-only MCP tools for TikTok/YouTube hook scoring, virality prediction, trend analysis, and viral template search, with evidence-layer scoring.
      MIT
    • A
      license
      Not graded
      quality
      F
      maintenance
      Gives MCP clients access to YouTube video transcripts and metadata. It lets AI agents read and summarize video content from a URL.
      3 npm
      MIT

    TDQS

    A4.7/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool has a clearly distinct role: transcribe_video for audio-only transcription, understand_video for full multimodal processing, and the remaining tools operate on the returned video_id for search, evidence, timeline, or semantic mapping. Even the overlapping transcribe/understand pair is explicitly differentiated by use case, and search_video vs find_moment are separated by exhaustive vs. single-best results.

    Naming Consistency5/5

    All tool names follow a consistent snake_case verb_noun pattern (transcribe_video, understand_video, search_video, find_moment, get_video_timeline, get_video_map, get_video_entity, get_video_evidence). The pattern is uniform and predictable, making it easy for an agent to infer function from the name alone.

    Tool Count5/5

    Eight tools is well within the ideal 3–15 range and feels well-scoped for a video understanding server. Each tool has a specific place in the pipeline (ingest, search, timeline, semantic map, evidence lookup) without redundancy or bloat.

    Completeness5/5

    The tool surface covers the full workflow: ingest a video (transcribe_video/understand_video), explore it (search_video, find_moment, get_video_timeline), and dig into semantic relationships (get_video_map, get_video_entity, get_video_evidence). The design anticipates the questions agents will ask and provides no dead ends—every returned video_id can be used by downstream tools.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues