List AI models

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

Annotations already declare this a safe, closed read (readOnlyHint true, destructiveHint false, openWorldHint false), so the bar is lower. The description still adds real value by disclosing response shape: credit costs, capabilities, and that video models expose durations, resolutions, aspect ratios and support flags like startFrame/endFrame/reference/audio — important since there is no 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 sentences, both earning their place: the first defines the output, the second routes the agent to the consuming tools. Scope and the key return fields are front-loaded with no filler.

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 so for the model catalog, especially video models. It is slightly thinner on what image and llm entries contain, but for a single optional-param listing tool this is close to complete.

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 coverage is 100% and the single `type` enum is fully documented in the schema, including the omit-default and the llm case. The description adds no syntax or semantics beyond that, so the baseline of 3 applies.

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 opens with a specific verb and resource ('List the AI models available to this account') and names what the response contains: credit cost and capabilities. No sibling tool lists models, so the purpose is unambiguous without opening any schema.

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 states the downstream condition clearly: use the returned `slug` as the `model` argument of vivid_generate_image / vivid_generate_video, which implies calling this before generation. It gives no explicit 'do not call' case or note about caching, so it falls just 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.