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OP3 MCP

by thenavidm
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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

    A4.1/5.0

    Scored across 22 tools

    Disambiguation5/5

    Each tool targets a distinct metric or level of the OP3 data model—headline downloads, trends, raw rows, audience cohorts, geography, app/device mix, benchmarks—and the descriptions repeatedly instruct which tool to prefer over which. Even the four download-related tools are cleanly separated by aggregation level and intent.

    Naming Consistency4/5

    All names share the op3_ prefix and snake_case, with many following a verb_noun or resource_metric pattern like op3_list_episodes and op3_query_downloads. A few are noun-only or phrase-like, such as op3_geography and op3_new_vs_returning, so the pattern is consistent but not perfectly uniform.

    Tool Count3/5

    At 22 tools this is on the heavy side and will create meaningful selection overhead for an agent. The count is not bloated—each tool maps to a genuinely different OP3 query—but it is above the range where a tool set feels lean and immediately navigable.

    Completeness5/5

    The surface covers the full OP3 analytics workflow: resolving and verifying show identity, show and episode metadata, headline and raw downloads, unique audience, returning listeners, retention, episode overlap, geography, app and device mix, global benchmarks, trends, listening patterns, episode curves, and transcript discovery. There are no obvious dead ends for common podcast-analytics questions.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources