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producer_generate_music

Generate AI music from a text prompt. Describe genre, mood, and theme to automatically create lyrics, melody, style, and arrangement.

Instructions

Generate AI music from a text prompt using Producer/Riffusion.

This is the simplest way to create music - just describe what you want and
Producer will automatically generate appropriate lyrics, melody, style, and
arrangement.

Use this when:
- You want quick music generation with minimal input
- You don't have specific lyrics in mind
- You want the AI to be creative with the arrangement

For full control over lyrics and style, use producer_generate_custom_music instead.

Returns:
    Task ID and generated audio information including URLs, title, lyrics, and duration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the music to generate. Be descriptive about genre, mood, instruments, and theme. Examples: 'A happy birthday song with acoustic guitar', 'Epic orchestral battle music with dramatic choir', 'Chill lo-fi hip hop beat for studying'
modelNoProducer model version. 'FUZZ-2.0' is the default and recommended for most use cases. 'FUZZ-2.0 Pro' offers the highest quality. 'FUZZ-2.0 Raw' provides raw unprocessed output.FUZZ-2.0
instrumentalNoIf true, generate instrumental music without vocals. Default is false (with vocals).
callback_urlNoWebhook callback URL for asynchronous notifications. When provided, the API will call this URL when the audio is generated.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must disclose behavior. It explains automatic generation of lyrics/melody/style and mentions return of Task ID and audio info. However, it does not clarify whether the generation is asynchronous or how the callback_url and polling work, which is a notable gap.

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, well-structured with a header, bulleted use cases, and a returns section. Every sentence adds value without repetition.

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?

For a simple generative tool with 4 parameters and an output schema, the description covers purpose, usage guidance, and return info. Minor gaps exist around async behavior and cost, but overall it's sufficiently 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%, so baseline is 3. The description adds no additional meaning beyond the existing schema parameter descriptions (e.g., prompt examples are in the schema itself, not the description).

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 explicitly states 'Generate AI music from a text prompt' and highlights it as the simplest method. It clearly distinguishes from the sibling tool 'producer_generate_custom_music' by contrasting minimal vs full control.

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?

The description provides a 'Use this when:' section with three clear scenarios and explicitly names the alternative for full control. This helps the agent choose the correct tool.

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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