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generate_music

Turn text prompts into original music tracks. Specify duration, model, and variations to get audio suited to your project.

Instructions

Generate music from text descriptions

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoSeed for reproducible generation
modelNoMusic generation model to use
promptYesText description of the music to generate
durationNoDuration in seconds for the music
num_outputsNoNumber of variations to generate
display_modeNoHow to return the audio: display (show audio), save (return base64 for saving), both (show audio and provide base64)display
save_locationNoDirectory path to save the music. Overrides default save location.
negative_promptNoWhat to avoid in the generation
Install Server

TDQS

C2.9/5.0
Behavior2/5

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

Annotations are absent, so the description carries the full disclosure burden, yet it only restates the core action. It discloses nothing about non-determinism (despite the seed parameter), cost or credit consumption (estimate_cost exists as a sibling), latency, or how generated audio is returned. There is no contradiction, but almost no behavioral context beyond the tool's purpose.

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?

The definition is a single nine-word sentence with zero filler, front-loading the verb and resource. It is efficient, though the brevity reflects under-specification rather than rich compression of information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter generation tool with no annotations and no output schema, this is thin. The description says nothing about output format, the display/save/both behavior of display_mode, model selection, or cost implications, so an agent must infer critical invocation details from the schema alone. A few sentences covering output behavior and model guidance would be needed for completeness.

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%, so the baseline of 3 applies even though the description adds no parameter detail. The only marginal contribution is mapping 'text descriptions' to the prompt parameter; semantics for model, duration, display_mode, and save_location are entirely delegated to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource — 'generate music' from 'text descriptions' — making the tool's core function unmistakable. It does not, however, distinguish itself from the sibling generate_audio, leaving real ambiguity about whether music or general audio generation is intended.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance is provided whatsoever. The description never mentions alternatives, exclusions, or selection conditions, and given the overlapping sibling generate_audio, the boundary between 'music' and 'audio' is left entirely to the agent's inference. This is simply absent 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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