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reaper_analyze_mix

Read-onlyIdempotent

Measure LUFS, loudness range, peak, frequency balance, and stereo correlation of a rendered mix. Optionally get AI feedback from Gemini with concrete REAPER fixes.

Instructions

Analyze a rendered mix: local DSP measurements + AI listening feedback.

Two layers. Local DSP (numpy/pyloudnorm) measures the trustworthy numbers — integrated LUFS, loudness range, sample peak, crest factor, clipped samples, per-band frequency balance, and stereo correlation/width/balance. Then (unless include_ai=false) Gemini is given BOTH the audio file and those measurements and returns grounded mix/master feedback with concrete REAPER fixes.

Requires the optional deps: pip install -e .[analyze], and GEMINI_API_KEY in the environment for the AI layer. Pass a reference_path to compare against a pro track.

This reads files and calls an external API; it never modifies the Reaper project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audio_pathYesPath to the rendered audio file to analyze (WAV/MP3/FLAC/AAC). Render the project first (reaper_render_project) and pass that file.
focusNoOptional free-text note on what to focus on, passed to Gemini (e.g. 'the vocal sounds buried', 'too boomy on small speakers').
reference_pathNoOptional path to a reference/commercial track to compare against.
include_aiNoIf true, send the audio + metrics to Gemini for written feedback. If false, return only the measured DSP metrics (no API call).
modelNoGemini model that listens to the mix: 'gemini-2.5-flash' (fast/cheap) or 'gemini-2.5-pro' (deeper analysis).gemini-2.5-flash
response_formatNo'markdown' for human-readable output or 'json' for machine-readablemarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond annotations (readOnly, destructive, idempotent), the description adds valuable behavioral details: it reads files, calls an external API (Gemini), requires specific deps and environment variable, and explicitly states it never modifies the project. This fully informs the agent of side effects.

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

Conciseness4/5

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

The description is well-structured with front-loaded purpose and efficient paragraphs, each sentence adding value. Slightly verbose but still concise enough for an AI agent.

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

Completeness5/5

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

Given the tool's complexity (two layers, dependencies, external API, optional reference), the description covers all necessary aspects: required setup, behavior, output options, and non-destructive nature. With an output schema present, the description is fully complete.

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

Parameters3/5

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

Schema coverage is 100% with good parameter descriptions. The description adds context for the tool's two layers but does not significantly enhance understanding of individual parameters beyond the schema. Baseline of 3 is appropriate.

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 analyzes a rendered mix using two layers (local DSP and AI listening), specifies verb+resource, and distinguishes from sibling tools like reaper_analyze_project by focusing on mix analysis and mentioning it never modifies the project.

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 provides context on prerequisites (deps, API key), optional reference path, and the include_ai flag, and implies when to use this tool (after rendering, for mix analysis). However, it does not explicitly compare to reaper_analyze_project or other siblings.

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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