List generation models

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

Annotations already declare readOnlyHint/destructiveHint=false, so the safety profile is covered; the description earns credit for what the annotations can't say — that the call is free, that only measured combinations are returned, and that invalid ones fail with unsupported_combination before any charge. It omits the response shape (presumably a list keyed by model) and whether results are cacheable/static.

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?

One dense sentence that front-loads the purpose, then extends to return fields, then the error/charging guarantee, plus a one-word cost note. No filler and no repetition of the title.

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?

With no output schema, the description carries the return-value burden and does it: it names the fields returned and the unit_credits semantics (per image vs per second of clip). Combined with the free/error behavior, an agent has everything needed to call this zero-param tool correctly.

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?

Zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. Nothing in the text contradicts the empty schema.

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?

A specific verb (List) plus a specific resource (the models consumed by generate_image and generate_video), and it explicitly ties itself to those two sibling tools. The enumeration of returned attributes (modes, aspect ratios, resolutions, durations, dimensions, unit_credits) removes any ambiguity about what 'models' means here.

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?

'Only measured combinations are listed; any other is refused with unsupported_combination before any charge' tells the agent this is a pre-flight lookup that gates the generate/quote siblings. It never states outright 'call this before generate_image', so the routing is strongly implied rather than prescribed.

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