Magic Master MCP
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AlicenseAqualityCmaintenanceEnables MCP clients to measure a track's loudness and peak levels, strip artifacts left by AI generators, compare mastering presets, and master single tracks or batches to streaming targets via a hosted remote server or a local stdio bridge. Tracks are supplied as public HTTPS links, with analysis, cleanup and previews free and full masters limited by a daily free quota or credits.9MIT- AlicenseAqualityDmaintenanceAn MCP server that masters audio with a professional signal chain using ffmpeg, enabling loudness analysis, vocal-over-beat mixing, full mastering to target LUFS, and Dolby codec export.49 npmMIT
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AudioPod MCP Serverofficial
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TDQS
Scored across 11 tools
Most tools are cleanly separated: analyze_track, clean_ai_trace, preview_styles, master_track, and batch_master all have clearly different purposes. The only mild overlap is among get_service_info, get_pricing, and check_limits, all of which touch cost/account state and require careful reading.
Names are uniformly lowercase snake_case and mostly follow a verb_noun pattern like list_presets, analyze_track, and get_job. The only notable deviation is batch_master, which reads more like a compound noun than the verb_noun pattern used by master_track.
Eleven tools is well within the ideal range and each one earns its place in the mastering workflow. The set covers orientation, presets, pricing, limits, analysis, cleanup, preview, single and batch mastering, job polling, and token purchases without filler.
The full workflow is closed: agents can check limits, analyze, preview, master or batch master, poll for results and downloads, and create a top-up link when tokens run out. There are no dead ends in the core lifecycle.