generate_music

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

No annotations, so description must fully disclose behavior. It covers clip duration, generation limits, language support, output MIME types, image input formats, output path, and return type as MCP audio content. Very transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is dense but efficient; every sentence adds value. Could be slightly more structured (e.g., separate paragraphs for models, inputs, outputs), but not overly long.

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

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

For a complex tool with 11 parameters and no output schema, the description covers all key aspects: model variants, duration limits, languages, input types, output path, and return type. No missing critical information.

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

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

Schema has 100% coverage, yet description adds significant value: explains fixed 30s for clip vs pro, audio specifications (44.1 kHz, stereo), and limitations (no negative prompting, audio/video refs not accepted). Enhances schema meaning.

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

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

Clearly states 'Generate music using Lyria models' – a specific verb+resource. Distinguishes from siblings like generate_image, generate_speech, etc., which are for different modalities.

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

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

Provides detailed guidance on model selection (clip vs pro), language directions, output formats, and limitations (no negative prompting, no audio/video references). Does not explicitly state when not to use, but covers context well.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.