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AIMusicVideoGen

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    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

    A3.9/5.0

    Scored across 5 tools

    Disambiguation4/5

    Each tool maps to a distinct stage: analysis (analyze_song), planning (plan_music_video), spec lookup (video_specs), and two creation paths (make_spotify_canvas, create_ai_music_video). The only mild friction is between the two creation tools, since one actually renders a file while the other only returns instructions and a link, but the descriptions make the distinction clear.

    Naming Consistency4/5

    Four tools follow a clean verb_noun pattern (analyze_song, plan_music_video, make_spotify_canvas, create_ai_music_video). The lone outlier is video_specs, a bare noun, which is a minor deviation from an otherwise predictable convention.

    Tool Count5/5

    Five tools is well-scoped for a focused music-video pipeline, with each tool earning its place across analysis, planning, specs, and generation. Nothing feels redundant or padded.

    Completeness4/5

    The surface covers a reasonable end-to-end flow: analyze a song, plan cuts, look up platform specs, and produce a Spotify Canvas or kick off full AI video generation. The main gap is that full video rendering is delegated to an external web app rather than produced locally, leaving a partial dead end.

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