List video models

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

Annotations already declare this as a read-only, non-destructive, closed-world operation, so the safety profile is covered. The description adds useful context about what information is returned, but does not disclose behavioral details like pagination, ordering, rate limits, or authentication requirements.

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?

The description is two tight sentences with zero waste. The primary purpose is front-loaded, followed immediately by the invocation guidance.

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 zero-parameter read-only listing tool with annotations covering safety and an output schema covering return values, the description is complete enough. It states what the tool returns and when to call it, leaving no critical gap for an agent.

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?

The tool takes zero parameters, so parameter semantics are inherently minimal. With no inputs to explain, the baseline score is 4 and the description does not need to compensate for schema gaps.

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?

The description uses a specific verb and resource: list every available video generation model. It also enumerates the data returned (capabilities, durations, API availability) and distinguishes itself from the generate_video sibling by positioning itself as the prerequisite lookup step.

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?

It gives clear usage context: call this to retrieve available model ids before calling generate_video. It does not explicitly state when not to use it or name alternatives such as list_image_models, so it falls short of full when/when-not guidance.

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