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mcp_opendaw_split_stems

Split audio files into isolated stems (vocals, drums, bass, guitar, piano) and perform specialized separation like vocal extraction or noise cleanup using open-source models.

Instructions

Split an audio file into stems using SOTA open-source separation models.

Runs locally on GPU (GTX 1650 4GB, ~4.5 min for 4-min track). All models trained at 44100Hz — auto-resampling handled internally.

input_path: Absolute path to input audio file (WAV/MP3/FLAC/OGG). mode: Separation mode (default "bs6"): - "ensemble": Max quality, 4 passes (bass/drums/vocals/other). Slowest, best SDR. - "scnet": 4-stem (drums/bass/other/vocals). Best single-pass multi-stem. - "bs6": 6-stem (bass/drums/other/vocals/guitar/piano). Fast, low bleeding. - "polarformer": Vocal extraction only (vocals/instrumental). - "dereverb": Remove reverb from vocals (dry/reverb). - "drumsep": Drum separation (kick/snare/cymbals/toms). - "denoise": Noise cleanup for low-quality sources (clean/noise). output_dir: Directory for stem files (default: /tmp/stems_). import_to_daw: If True, load each stem into the DAW and return sample IDs for use with place_audio_region. Requires DAW bridge running.

Returns list of stem file paths (and sample IDs if import_to_daw=True).

Workflow: split_stems("track.wav", "bs6") → 6 stem WAVs split_stems("track.wav", "ensemble", import_to_daw=True) → 4 stems loaded into DAW

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNobs6
input_pathYes
output_dirNo
import_to_dawNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the burden. It reveals performance (GPU, time estimate), resampling behavior, and return value format. It does not mention destructiveness or auth requirements, but the mutation aspect (creating files) is implied.

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 a clear hierarchy: purpose, hardware notes, parameter list, return value, and workflow examples. It is moderately long but every sentence adds value.

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 4 parameters, no schema descriptions, and an output schema, the description covers all parameters, return values, and provides workflow examples. It is comprehensive for the tool's complexity.

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 coverage is 0%, yet the description explains each parameter thoroughly: input_path (format), mode (all options with descriptions), output_dir (default format), and import_to_daw (behavior and return value impact).

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 'Split' and resource 'audio file into stems', with specific mention of SOTA models. It distinguishes itself from siblings like 'separate_stems' by detailing the model options and their stem counts.

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 detailed guidance on when to use each mode (e.g., 'ensemble: Max quality, 4 passes', 'bs6: 6-stem, fast, low bleeding') and explains the import_to_daw workflow. However, it does not explicitly compare with the similar sibling 'mcp_opendaw_separate_stems' or state when not to use this tool.

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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