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suno_remaster_music

Remaster audio tracks to improve clarity, dynamics, and overall sound quality. Upgrade previously generated songs to production-ready standards using newer AI models.

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
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses async behavior via 'Returns: Task ID', specifies the enhancement targets ('clarity, dynamics'), and constrains inputs to previously generated songs. Missing details on whether the original is preserved or overwritten, but includes critical async pattern.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

Well-structured with front-loaded purpose, followed by elaboration, bullet-pointed usage guidelines, and return value specification. Minor redundancy between first two sentences ('improve audio quality' vs 'enhance clarity, dynamics'), but second sentence adds specific technical attributes that justify its inclusion.

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 presence of an output schema and 100% input schema coverage, the description appropriately focuses on high-level purpose and usage patterns. It mentions the Task ID return (critical for async polling) and covers the three-parameter complexity adequately without unnecessary duplication of schema details.

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%, providing detailed descriptions for audio_id, model, and callback_url. The description mentions 'previously generated song' which aligns with audio_id semantics, but adds no syntax details beyond the comprehensive schema. Baseline 3 appropriate given schema completeness.

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?

Description opens with specific verb 'Remaster' and clear resource 'existing song'. The phrase 'previously generated song' effectively distinguishes this from siblings like suno_generate_music or suno_extend_music, clarifying this operates on existing audio rather than creating new content.

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 'Use this when:' section provides three concrete scenarios (improve quality, update old models, production-ready polish). However, it lacks explicit exclusions or named alternatives (e.g., 'do not use for initial generation, use suno_generate_music instead').

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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