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generate_music

Generate AI music from text descriptions using paid Lightning Network micropayments. Provide a prompt, model ID, and valid payment ID to create custom audio tracks.

Instructions

Generate music using AI. Requires a valid paid payment ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paymentIdYesValid payment ID (must be paid)
promptYesDescription of the music to generate
modelIdYesThe AI model database ID
lyricsNoOptional lyrics for the music
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the payment requirement, which is useful context about authentication/access needs. However, it doesn't describe what the tool actually does beyond 'generate music'—no information about output format (e.g., audio file, MIDI), processing time, rate limits, or error conditions. For a tool with no annotations, this leaves significant behavioral gaps.

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 extremely concise—two short sentences with zero wasted words. It's front-loaded with the core purpose and follows with a critical constraint. Every sentence earns its place by providing essential information without redundancy.

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?

Given the complexity of AI music generation (a non-trivial operation with 4 parameters, no annotations, and no output schema), the description is incomplete. It lacks information about what the tool returns (e.g., audio data, job ID), how to handle the output, error handling, or performance characteristics. The payment requirement is noted, but other critical context is missing.

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 schema already documents all four parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain parameter interactions, default behaviors, or provide examples. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra value.

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 clearly states the verb 'generate' and resource 'music using AI', making the purpose immediately understandable. It distinguishes from siblings like generate_image or generate_text by specifying music generation. However, it doesn't fully differentiate from generate_3d_model or generate_video beyond the resource type.

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

Usage Guidelines3/5

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

The description provides one explicit usage guideline: 'Requires a valid paid payment ID.' This indicates a prerequisite but doesn't explain when to use this tool versus alternatives like generate_audio or synthesize_speech (if they existed), nor does it provide context about when this tool is appropriate versus other music-related operations. The guidance is limited to a single constraint.

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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