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suno_underpainting

Add AI-generated instrumental accompaniment to your uploaded vocal track, turning an acapella into a full musical arrangement.

Instructions

Add AI-generated accompaniment/instrumental background to uploaded audio.

Takes your uploaded vocal track and adds an AI-generated instrumental
accompaniment beneath it (underpainting = adding music under vocals).

Use this when:
- You have a vocal recording and want to add music behind it
- You want to give an acapella track a full musical arrangement
- You need to add instrumental backing to existing vocals

Returns:
    Task ID and the audio with accompaniment added.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audio_idYesID of the uploaded audio to add accompaniment to. Must be uploaded via suno_upload_audio.
underpainting_startNoStart time in seconds for adding accompaniment. Default is 0.
underpainting_endNoEnd time in seconds for adding accompaniment. Must be less than total song duration.
modelNoModel version to use.chirp-v5-5
callback_urlNoWebhook callback URL for asynchronous notifications.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must fully convey behavioral traits. It mentions returning a Task ID (suggesting async operation) but does not disclose whether the original audio is modified, rate limits, failure modes, or authentication needs. This is insufficient for safe agent invocation.

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 concise and well-structured: a brief explanation of the term, a bulleted usage list, and a return value summary. No unnecessary words or repetition.

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 parameter count (5), full schema coverage, and presence of an output schema, the description adequately explains the tool's operation and parameters. It does not cover prerequisites like user authorization or detailed output, but these are partially offset by the 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 description coverage is 100%, so baseline is 3. The description adds useful context for audio_id (must be uploaded via suno_upload_audio) and constraints for underpainting_end (must be less than total duration). These modest additions keep the score at 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool adds AI-generated accompaniment to uploaded audio, using specific verbs and explaining the term 'underpainting'. It references vocals and acapella, but does not explicitly differentiate from similar siblings like overpainting, leaving some ambiguity.

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 bulleted list of when to use the tool (vocal recording, acapella, etc.) and implies prerequisites via the parameter description for audio_id. However, it lacks explicit when-not-to-use guidance and alternatives among sibling tools.

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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