AetherWave Studio

Generate music (Suno)

aetherwave_generate_music

Generates AI music via Suno. Returns two tracks per submission. Default model is V5.5 (newest, best quality). For instrumental output set instrumental: true. Music gen typically takes 30-90s - this tool polls with up to a 6-minute budget. Note: the title param is advisory for instrumentals - Suno often writes its own title from the prompt content for instrumental generations. Transient GENERATE_AUDIO_FAILED errors are common; retry once before degrading the model version.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Suno model version. Defaults to V5_5 (current best).
`title`	No	Optional title for the generated tracks.
`lyrics`	No	Custom lyrics. If omitted, Suno will generate lyrics from the prompt (unless instrumental=true).
`prompt`	Yes	Style/mood/topic description. E.g. 'Lo-fi ambient track, rain sounds, warm pads' or 'High-energy synthwave with driving bass'.
`audioWeight`	No	0 to 1. Weighting on the audio character of the generation. Only applies with lyrics.
`styleWeight`	No	0 to 1. How closely to follow the style description. Higher sticks to it, lower lets the model roam. Use when someone asks to stay closer to, or further from, a described sound. Only applies with lyrics.
`vocalGender`	No	Vocal gender, 'm' or 'f'. Only applies when lyrics are supplied. Default 'm'.
`instrumental`	No	If true, no vocals. Default false.
`negativeTags`	No	Comma-separated things to AVOID, e.g. 'heavy metal, screaming, distorted guitar'. Use when someone says they do not want a particular sound. Only applies with lyrics.
`weirdnessConstraint`	No	0 to 1. How experimental the result is. Higher is stranger and more unexpected, lower is safer and more conventional. Use when someone asks to make it weirder or more normal. Only applies with lyrics.

TDQS

A4.6/5.0

Behavior5/5

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

Discloses non-obvious behaviors beyond annotations: two tracks returned, default model, 6-minute polling budget, advisory title for instrumentals, and common transient errors with retry strategy. This is rich behavioral context.

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: four sentences, every one adding operational value. It front-loads the main action and then packs specifics efficiently 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?

With no output schema, the description mentions two tracks but not the response structure. However, it covers timing, errors, defaults, and key parameter nuances, which is a strong level of context for a complex generation tool. Missing only formal return format details.

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 already describes all parameters with 100% coverage, so baseline is 3. The description adds extra semantics for the instrumental param and notes about title behavior, plus retry guidance, which is additional value over the schema.

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 it generates AI music via Suno, using a specific verb and resource. It distinguishes itself from sibling tools by naming music generation specifically and adds unique details like returning two tracks per submission.

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?

Provides clear operational context: instrumental flag, polling duration, retry behavior. However, it does not explicitly compare to alternatives or state when not to use this tool, falling short of full exclusion guidance.

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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TDQS

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct media operation (image, video, audio, listing, mastering, etc.) with clear boundaries. Even similar tools like generate_image and edit_image are differentiated by their primary intent (creation vs. modification) and model selection guidance.

Naming Consistency5/5

All tools follow the 'aetherwave_verb_noun' pattern consistently, using snake_case. Verbs and nouns are descriptive and predictable (e.g., generate_image, list_video_models, remove_background_video).

Tool Count4/5

16 tools is slightly above the ideal range (3-15) but remains well-scoped for a multimedia generation platform covering image, video, audio, and user management. Each tool serves a distinct purpose, and no obvious bloat exists.

Completeness4/5

The tool surface covers core creation, editing, listing, and enhancement workflows for images, videos, and audio. Minor gaps exist (e.g., no delete tool, no get-single-creation tool), but the essential lifecycle is well-covered.

Resources

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