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suno_optimize_style

Transform rough music style descriptions into optimized prompts that improve Suno AI music generation quality and genre accuracy.

Instructions

Optimize a music style description for better generation results.

Takes a rough style description and refines it into an optimized style
prompt that Suno can better understand and produce higher quality music for.

Use this when:
- You have a vague style idea and want to refine it
- You want better style prompts for suno_generate_custom_music
- You need suggestions for style terms

Returns:
    Optimized style description ready for use in music generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesStyle prompt words that need to be optimized. Examples: 'rock guitar', 'jazz smooth', 'electronic dance party'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. Explains the transformation semantics (rough → optimized) and value proposition (higher quality music). Lacks disclosure of error conditions, rate limits, or determinism, but adequately covers the core behavioral contract for a simple single-parameter transformation 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?

Perfectly front-loaded with purpose in first sentence. Zero waste: uses structured formatting (bullet points for usage, clear return section) to maximize information density. Every sentence earns its place.

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?

Complete for a single-parameter transformation tool. Documents return value ('Optimized style description ready for use') despite output schema existing. Establishes clear relationship to sibling ecosystem (suno_generate_custom_music). No critical gaps given tool complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with examples provided in schema. Description adds value by characterizing the input as 'rough' and the process as 'refinement', providing semantic context about expected input quality and transformation nature that the schema's technical description lacks.

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?

Opens with specific verb 'Optimize' and clear resource 'music style description'. Distinguishes itself from sibling generation tools (suno_generate_music, suno_generate_custom_music) by explicitly positioning as a preprocessing/refinement utility rather than a content creator.

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?

Explicit 'Use this when:' section lists three specific scenarios including vague ideas and downstream usage with suno_generate_custom_music. Directly names a sibling tool as the consumer of this tool's output, providing clear workflow context.

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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