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mcp_opendaw_split_stems

Split audio tracks into isolated stems (vocals, drums, bass, etc.) using open-source AI models. Choose from modes for multi-stem separation, vocal extraction, de-reverb, drum splitting, or noise cleanup.

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
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses local GPU execution with hardware specs and runtime estimate, automatic resampling to 44100Hz, mode-specific behaviors, DAW bridge requirement for import_to_daw, and return value format. This goes far beyond the schema in explaining what happens when the tool is invoked.

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?

The description is well-structured, starting with a concise purpose statement, a brief hardware note, a detailed but organized mode list, and clear workflow examples. Every sentence contributes value, and the formatting makes it easy to scan.

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 (multiple modes, file I/O, optional DAW integration, performance characteristics), the description covers purpose, parameters, return values, prerequisites, and even output_dir defaults. The only omission is error handling, but that is not critical given the thorough coverage elsewhere.

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 input schema has 0% description coverage, but the description fully compensates by explaining each parameter: input_path must be an absolute path, mode has seven enumerated options with semantics, output_dir has a default and pattern, and import_to_daw describes its effect and prerequisite. This adds critical meaning beyond the bare schema definitions.

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 opens with a specific verb+resource: 'Split an audio file into stems using SOTA open-source separation models.' This clearly states the tool's function and differentiates it from sibling tools like mcp_opendaw_export_stems or mcp_opendaw_separate_stems by emphasizing input is an audio file and the action is splitting into stems.

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 a clear use case ('Split an audio file into stems') and detailed mode explanations with specific trade-offs (e.g., 'ensemble: Max quality, 4 passes... Slowest, best SDR'). It includes workflow examples but does not explicitly compare with alternatives like mcp_opendaw_export_stems, so usage context is clear but exclusion criteria are absent.

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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