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suno_remaster_music

Improves audio quality of previously generated songs by applying remastering to enhance clarity, dynamics, and sound quality.

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?

No annotations provided, so the description carries the full burden. It explains the tool takes a previously generated song and returns a remastered version, which implies non-destructive behavior. However, it lacks details on rate limits, permissions, or what happens to the original audio, which would be helpful for an AI agent.

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 very concise: one sentence states the main purpose, followed by bullet points for usage scenarios, and a return line. Every sentence adds value with no fluff, and it is front-loaded.

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 has 3 parameters, an output schema, and many siblings, the description covers its purpose, when to use, and return value. It could briefly explain the model parameter's impact, but the schema provides enum details. Overall, it is complete enough for an AI agent.

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% with each parameter having a description. The tool description adds minimal extra meaning, such as clarifying audio_id is from a previous generation. This aligns with the baseline 3 expected when schema is comprehensive.

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 tool remasters an existing song to improve audio quality, using specific verbs and resource. It distinguishes from sibling tools by focusing on audio quality enhancement of previously generated songs, which is not covered by other siblings like suno_cover_music or suno_extract_vocals.

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 cases with bullet points, such as improving quality of a generated song or making an older model sound better. It does not mention when not to use or alternatives, but the context is clear and helpful for 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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