generate_music

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

No annotations provided; the description only describes what the tool generates (music) and supported prompt features. It does not disclose behavioral traits such as whether the operation is destructive, required authentication, rate limits, or output format beyond the existence of an output schema.

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?

Two precise sentences with no extraneous words. The core action and supported features are front-loaded, making it efficient for an agent to parse.

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?

Given that an output schema exists, the description need not explain return values. It covers the input capabilities (genres, structure tags, lyrics) and model choices. Could mention prerequisites or limitations, but is largely adequate for the tool's complexity.

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 covers 100% of parameters. The tool description repeats the prompt details found in the schema and adds no new meaning. The model parameter is already fully described in the schema. Baseline 3 due to full schema coverage.

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?

Specifically states 'Generate music from a text prompt using Google's Lyria models,' clearly identifying the verb, resource, and tool. Distinguishes from sibling tools like generate_image or generate_audio by focusing on music generation.

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?

Clear that this tool is for music generation from text, but does not explicitly state when not to use it or mention alternatives like generate_audio. Context from sibling tools makes the distinction obvious, so the guidance is implicit rather than explicit.

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