F
licenseNot graded
qualityD
maintenanceEnables AI assistants to programmatically edit, analyze, and export audio projects through MCP tools, including multi-track editing, effects, transcription, and semantic search.
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Podcast MCP
by calebn
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TDQS
D1.7/5.0
Scored across 160 tools
Disambiguation2/5
Several clusters overlap heavily (cut_time_range vs ripple_delete vs strip_silence; correct_transcript vs apply_transcript_cleanup vs reconcile_transcript; multiple speaker_* and play_* tools), and many tools have empty descriptions, so an agent cannot reliably pick the right one. The few descriptive entries (e.g. preview_inaudible_cut vs suggest_handoff_cut) show the set can disambiguate, but most tools lack that clarity.
Naming Consistency2/5
Names mix noun_verb patterns (track_add, history_undo, pipeline_run) with verb_noun patterns (add_effect_tool, list_clips_tool, create_review_share_tool), and the _tool suffix is applied inconsistently (track_remove_tool vs track_add, add_comment vs delete_comment_tool). This makes the naming pattern unpredictable even though everything is lower_snake_case.
Tool Count1/5
160 tools is an extreme count for a single MCP server and far beyond the 3-15 well-scoped range. The surface could be split into focused sub-servers (editing, transcript, review, recording, session) or consolidated into compound operations; as-is it will overwhelm context and selection.
Completeness4/5
The tool surface is exceptionally broad for podcast production—ingest, transcription, editing, mixing, review, history, session control, recording, and social clips are all covered. Minor gaps remain (e.g., episode lifecycle is only episode_create, no track listing, no effect parameter updates), but agents can usually work around them.
Maintenance
ActivityMaintained
ResponsivenessResponsive