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mcp_opendaw_transcribe_melody

Transcribe a monophonic audio melody into MIDI notes on a DAW track using pitch detection, frequency-to-MIDI conversion, and note grouping.

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 provided, the description carries full burden. It explains the pipeline (WAV parsing, pitch detection, note creation) and lists return values (notes created, note count, bpm, duration, clarity). No side effects are mentioned but none expected.

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?

Well-structured with front-loaded purpose, pipeline, use cases, parameter list, and examples. Every sentence adds value 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?

Given the tool's complexity and presence of an output schema, the description covers input format, monophonic requirement, parameter details, use cases, and examples. It lacks information on error handling or file size limits but is otherwise comprehensive.

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?

Schema description coverage is 0%, so the description must compensate. It provides detailed parameter explanations for filename, bpm (with auto-detect), unit_index, and track_index, adding significant meaning 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 verb 'transcribe' and the resource 'monophonic melody from an audio file into MIDI notes on a DAW track'. It distinguishes itself from siblings like transcribe_audio and transcribe_drums by specifying monophonic melody.

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?

Provides explicit use cases and examples for when to use the tool, but does not explicitly state when not to use it or mention alternatives. The monophonic limitation implies exclusion of polyphonic content.

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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