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suno_remaster_music

Remaster an existing song to enhance clarity, dynamics, and overall sound quality. Get a polished, production-ready version of previously generated audio.

Instructions

Remaster an existing song to improve audio quality.

Takes a previously generated song and applies audio remastering to enhance
clarity, dynamics, and overall sound quality.

Use this when:
- You want to improve the audio quality of a generated song
- You want a song generated with an older model to sound better
- You need a polished, production-ready version

Returns:
    Task ID and the remastered audio information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audio_idYesID of the audio to remaster. This is the 'id' field from a previous generation.
modelNoModel version to use for remastering. Newer models produce better results.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, the description bears the full burden. It notes the return of Task ID and remastered audio info, but does not disclose side effects (e.g., creation of a new song), idempotency, or authorization requirements.

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, front-loaded with the action, and structured with bullet points for usage. 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 simplicity and the presence of an output schema (not shown but implied), the description adequately covers purpose, usage, and return. It lacks mention of error conditions or limits, but is otherwise complete.

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%, and the description adds little beyond the schema: it mentions using older models for improvement, but the schema already includes a well-described enum for model versions.

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 that the tool remasters an existing song to improve audio quality. It explicitly differentiates from siblings by focusing on quality enhancement of previously generated songs, not on generation or modification.

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 explicit 'Use this when' scenarios, which are helpful for an AI agent. However, it does not specify when not to use it or explicitly contrast with alternatives like 'suno_cover_music' or 'suno_extend_music', leaving some ambiguity.

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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