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suno_upload_cover

Generate AI cover versions of uploaded audio in new styles. Submit an audio ID to reinterpret your music with custom genres and moods.

Instructions

Create an AI cover of an uploaded audio (your own music).

Similar to suno_cover_music but works with audio you uploaded via
suno_upload_audio. Re-arranges your music in a different style.

Use this when:
- You uploaded your own music and want a cover in a different style
- You want to hear your song re-interpreted by AI

Returns:
    Task ID and the cover audio information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audio_idYesID of the uploaded audio to create a cover of. Must be an audio uploaded via suno_upload_audio.
styleNoTarget music style for the cover.
modelNoModel version to use.chirp-v5-5
callback_urlNoWebhook callback URL for asynchronous notifications.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the core behavioral trait ('Re-arranges your music in a different style') and hints at async behavior via 'Task ID' return value. However, it lacks disclosure of side effects, persistence, rate limits, or auth requirements expected for a creation/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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections: definition, sibling differentiation, usage conditions, and return value. Every sentence earns its place. The front-loaded opening immediately establishes the tool's purpose without waste.

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 4-parameter schema with 100% coverage and existing output schema, the description adequately covers the workflow prerequisites and return summary. Minor gap: could explicitly mention the async callback pattern for the callback_url parameter, though this is present in the 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 coverage is 100%, establishing a baseline of 3. The description reinforces the workflow constraint that audio_id must come from suno_upload_audio, but this is already documented in the schema. No additional semantic meaning (format examples, style syntax guidance) is added beyond the structured schema descriptions.

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 ('Create an AI cover of an uploaded audio') and explicitly distinguishes from sibling tool suno_cover_music by stating it 'works with audio you uploaded via suno_upload_audio.' This clearly defines scope and differentiates from alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit 'Use this when:' section with bullet points covering the prerequisite (uploaded your own music) and the goal (different style/re-interpretation). It also references the specific sibling alternatives, giving clear guidance on tool selection.

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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