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mcp_opendaw_transcribe_melody

Convert monophonic audio recordings into editable MIDI notes on a DAW track. Extract bass, vocal, or lead melody from WAV files for further music production.

Instructions

Transcribe monophonic melody from an audio file into MIDI notes on a DAW track.

Audio-to-MIDI melody transcription — converts a monophonic instrument recording (bass, vocal, lead synth, horn) into MIDI notes. Pure Python, no external deps.

Pipeline:

  1. Parse WAV file

  2. Frame-by-frame autocorrelation pitch detection

  3. Convert frequency → MIDI pitch (with cents deviation for tuning accuracy)

  4. Group consecutive similar-pitch frames into sustained notes

  5. Estimate velocity from frame energy

  6. Create MIDI notes on the specified track via create_notes_batch

Use cases:

  • Extract a bass line from a Suno track → reuse as MIDI

  • Transcribe a vocal melody → harmonize or transform

  • Capture a horn line → arrange for other instruments

  • Convert any monophonic audio to editable MIDI

filename: WAV file name (in exports dir) or absolute path. bpm: Tempo for beat conversion (0 = auto-detect via detect_bpm). unit_index: AU index with note tracks. track_index: Track to place transcribed notes.

Returns: notes created, note count, bpm, duration, average clarity.

Example:

Transcribe a bass line from a Suno track

result = transcribe_melody("suno_bass.wav", bpm=120)

Auto-detect BPM

result = transcribe_melody("vocal.wav") # bpm=0 → auto-detect

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bpmNo
filenameYes
unit_indexNo
track_indexNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description takes on the full burden and largely succeeds: it details the complete 6-step pipeline (WAV parsing, pitch detection, frequency-to-MIDI conversion, note grouping, velocity estimation, and note creation), mentions BPM auto-detection, and discloses the return summary. It does not state whether existing notes on the target track are overwritten or appended, which is a notable omission for a mutation tool.

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 sections for overview, pipeline, use cases, parameters, returns, and examples. It is somewhat long but each section serves a purpose; no filler. It could be tightened, but the structure helps the agent parse it quickly.

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 (audio processing pipeline), no annotations, and a schema with zero parameter descriptions, the description is remarkably complete. It covers the entire workflow, parameter meanings, return values, and includes usage examples. The only minor gap is the effect on existing track data, but overall it fully supports agent invocation.

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

Parameters5/5

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

The schema has 0% description coverage (only titles like 'Filename'), so the description must explain each parameter. It does so thoroughly: 'filename: WAV file name (in exports dir) or absolute path', 'bpm: Tempo for beat conversion (0 = auto-detect via detect_bpm)', and clearly explains unit_index and track_index as targets. This fully compensates for the sparse 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 opening line clearly states the tool's purpose: 'Transcribe monophonic melody from an audio file into MIDI notes on a DAW track.' It specifies the resource (audio file), action (transcribe to MIDI notes), and destination (DAW track), and the use cases (bass line, vocal, horn) further distinguish it from siblings like transcribe_drums.

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 states this is for monophonic melody transcription and provides concrete use cases (e.g., 'Extract a bass line from a Suno track'). It implies not for polyphonic audio or drums but does not explicitly name alternative tools or state exclusions, which is a minor gap.

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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