A
licenseA
qualityC
maintenanceEnables AI agents to master audio tracks to target LUFS/True Peak levels, remove Suno/Udio AI fingerprints, and retrieve mastering passports via a hosted MCP server.
11
28 npm
2
MIT
Echoteka MCP
Officialby Echoteka
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TDQS
A4.4/5.0
Scored across 9 tools
Disambiguation5/5
Each tool has a clearly distinct purpose, and descriptions explicitly cross-reference near-neighbors (e.g. list_presets vs preview_styles vs get_service_info, analyze_track vs get_job metrics). master_track vs batch_master differ clearly by cardinality, and the info tools (get_service_info/check_limits/list_presets) are cleanly separated by scope. Little risk of misselection.
Naming Consistency5/5
All nine tools follow a consistent snake_case verb_noun pattern: get_service_info, list_presets, check_limits, analyze_track, master_track, batch_master, clean_ai_trace, preview_styles, get_job. No mixed conventions or casing. batch_master is the only slight variant but stays within the same readable pattern.
Tool Count5/5
Nine tools is well-scoped for an audio mastering service, with no redundant or filler entries. Each tool maps to a distinct step (orient, check limits, list presets, analyze, preview, master single/batch, clean, poll).
Completeness4/5
The surface covers the full mastering lifecycle: orientation, limits, presets, pre-analysis, preview, single/batch mastering, cleanup, and result retrieval. Minor gaps exist, such as no job cancellation or way to list/enumerate existing jobs (only get_job by id), but core workflows are fully supported.
Maintenance
ActivityMaintained
ResponsivenessNo issues