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peterkolbe

io.github.peterkolbe/ableton-for-ai

by peterkolbe

analyze_stems

Generate summaries, spectrograms, and analysis data for all audio tracks after recording or effect changes.

Instructions

DEEP AUDIO ANALYSIS: Triggers the generation of summaries and spectrograms for all tracks.

This is a heavy operation that:

  1. Generates '.summary.json' (Frames, Transients, LUFS) for AI consumption.

  2. Generates '.spectrogram.webp' (Log-frequency visualizations).

  3. Generates '.analysis.json' (Full resolution data for internal use).

ACTION: Use this when you need fresh audio data (e.g., after the user recorded new parts or changed audio effects that significantly alter the sound).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the operation is heavy and lists generated artifacts, but with no annotations, it provides limited behavioral detail such as idempotency, failure modes, or prerequisites.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, well-structured with a clear header, bullet points for generated files, and a usage guidance sentence, all without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, generated artifacts, and usage context, but lacks prerequisite information or potential side effects on previously generated analysis.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters, the description adds meaning about the scope (all tracks) and the generated files, which is sufficient and goes beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool triggers deep audio analysis generating specific file types (summaries, spectrograms, analysis) for all tracks, distinguishing it from the read-only and parameter-setting siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use (after recording or effect changes) and labels it a heavy operation, but does not mention when not to use or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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