list_models

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

With no annotations provided, the description carries the full burden, and it delivers: it discloses what the returned capabilities are, the credit cost basis, and a server behavior detail about modes and image inputs. The 'every mode listed except lip_sync' phrase is slightly ambiguous and could be clearer, but it is a substantive behavioral disclosure beyond what the schema states.

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?

Three sentences with no filler; the core purpose is front-loaded and the usage guidance follows immediately. The only weak point is the slightly awkward 'except lip_sync' clause, but overall the description is compact and readable.

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?

There is no output schema, so the description compensates by spelling out exactly what the listing returns: maximum length, sizes, sound support, and credit cost. For a low-complexity list tool, this is sufficient; error behavior and further edge cases are not necessary for correct invocation.

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%, so the mode and needsAudio parameters are already well documented. The description adds useful context about cost basis and image input behavior, but those details are not tightly tied to the two parameters of this specific tool, so the added semantic value is moderate rather than transformative.

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 ('List') with a clear resource and object ('the AI video models available') and enumerates concrete capabilities (max length, sizes, sound, credit cost). It also orients the agent relative to its siblings ('before quoting or generating'), making it easy to tell apart from quote_video and generate_video.

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 an explicit when-to-use instruction: 'Use this before quoting or generating so the settings you pick are ones a model can serve.' It does not explicitly name alternative tools or state when not to use it, but the timing guidance is clear enough for an agent to route correctly.

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