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transcribe_audio

Transcribe an audio file into an editable RON score, pitch-tracking the melody, quantizing to a grid, and estimating note velocities for a draft you can revise.

Instructions

Transcribe a WAV, FLAC, mp3, or ogg file into an editable cochlea RON score — the inverse of render_score, and the arrow that closes the compose loop: hear a sketch, get score back, revise it, render it again. Pitch-tracks the melody, reads its timing against a tempo (detected from the audio unless you pass bpm), quantizes to a note grid, and estimates each note's velocity from its peak level. Deliberately monophonic: it hears one line, so chords, drums, and dense mixes come back as whichever line the tracker locked onto. Every assumption — the tempo, the grid, the preset, clamped or dropped notes — comes back in the response, so treat the result as a draft to re-voice rather than a faithful score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bpmNoTempo to notate against, 1..=4000. Detected from the audio when omitted; a wrong tempo renotates the same sound with odd note values.
ppqNoTick resolution of the written score. Default 960.
gridNoQuantization grid as a note duration ("1/16", "1/8", "1/4", "1/8t" for triplets, "1/8." for dotted), or "none" to keep the analyzer's raw timing. Default "1/16".1/16
presetNoInstrument preset for the transcribed track. Default "sine"; call score_reference for the catalog.sine
out_pathYesWhere to write the transcribed RON score.
audio_pathYesPath to a WAV, FLAC, mp3, or ogg file.
track_nameNoTrack name in the written score. Default "lead".lead
Behavior5/5

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

With no annotations, the description carries the full burden and does so exceptionally: it discloses the monophonic limitation, quantize behavior, tempo detection, velocity estimation, and that the result is a draft with assumptions returned in the response. This is far richer than merely stating 'transcribes audio'.

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 longer than average but every sentence adds value, explaining purpose, workflow, behavior, and caveats. The opening metaphor is evocative but not strictly necessary; still, it earns its place by reinforcing the tool's role in the compose loop. Slightly less polish would drop this to 3.

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 7 parameters and a complex audio-analysis behavior, the description covers purpose, limitations, output expectations, and integration with siblings. It is complete enough for an agent to select and invoke the tool correctly, even without an output schema.

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%, so the baseline is 3. The description adds contextual meaning for bpm (detected unless passed) and grid (quantization), but doesn't go beyond the schema for individual parameters. The schema already adequately documents each parameter.

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 uses a specific verb and resource ('Transcribe a WAV, FLAC, mp3, or ogg file into an editable cochlea RON score') and explicitly distinguishes itself from siblings by calling out its relationship to render_score. It also sets clear scope with the monophonic limitation.

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 clearly frames when to use the tool ('the arrow that closes the compose loop') and gives behavioral context, but it doesn't explicitly name alternative tools for cases like polyphonic audio, only implying they exist. This is strong context but lacks an explicit when-not-to-use with alternative names.

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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