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maxludden

audiobook_mcp

by maxludden
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Python
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    • audio-mcp

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      Audio perception for AI agents. Measure loudness, clipping, noise and pitch; view spectrograms; split audio into events; transcribe speech; compare before/after; and clean up, edit or synthesize sound. Runs locally, no API keys.
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    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

    A4.6/5.0

    Scored across 15 tools

    Disambiguation5/5

    Each tool targets a distinct operation: job lifecycle, status, boundary inspection/correction, output verification, and metadata handling are all clearly separated. Even similar tools like continue_conversion and run_until_done are explicitly differentiated by chunked vs. looping behavior.

    Naming Consistency5/5

    All tools use the consistent audiobook_ prefix followed by verb_noun snake_case names (start_conversion, get_status, patch_chapter_boundary). This makes the intended action and target resource predictable across the entire toolset.

    Tool Count5/5

    At 15 tools, the server is at the upper end of a well-scoped set, but every tool serves a distinct and necessary purpose in the audiobook pipeline. The count feels appropriate for covering conversion, progress tracking, boundary QA, verification, and metadata enrichment without redundant clutter.

    Completeness5/5

    The toolset provides a complete workflow: inspect EPUB, start/resume/run/cancel jobs, monitor progress, verify output, handle boundary corrections, and fetch/embed cover metadata. There are no obvious dead ends or missing lifecycle operations for the stated audiobook conversion purpose.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

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