io.github.peterkolbe/ableton-for-ai
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TDQS
Scored across 12 tools
Each tool targets a distinct action or resource: analysis, session overview, single/bulk track inspection, device parameter control (single and bulk), and individual mixer controls. No overlapping purposes; descriptions clearly differentiate them.
All tool names use snake_case and follow a verb_noun pattern, but verbs vary (get_, set_, list_, read_, analyze_). While readable, the mix is slightly inconsistent; e.g., list_resources vs get_track.
12 tools is well-scoped for an Ableton Live MCP server covering session discovery, track inspection, device parameter control, mixer control, and audio analysis. No unnecessary tools; each serves a clear purpose.
The set covers core inspection, parameter control, and mixer operations. Minor gaps exist (e.g., no track/device creation or deletion, no transport control), but the domain focus on AI-driven adjustment and analysis makes it acceptably complete.