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

With no annotations, the description carries the full burden, and it discloses return ordering (cheapest-first), the presence of per-call coin cost, and the default-model policy for each media type — meaningful behavioral context. It still doesn't state whether the list is paginated, cached, or requires auth, so it falls just short of fully self-sufficient.

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, all front-loaded: what it returns, when to call it, and the defaults/escalation rule. Every clause carries decision-relevant information 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 and no parameters, the description must convey the shape of the result, which it does (model list, cost ordering, coin cost). The main remaining gap is that it never states the response format or whether the catalog is exhaustive/stable.

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 there is no parameter surface for the description to explain; the baseline of 4 applies. The description correctly wastes no space on input semantics.

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?

States a specific verb and resource ('List Dora generation models') and adds distinguishing traits the siblings don't have: cheapest-first ordering and per-call coin cost. An agent can immediately tell this discovery tool apart from generate_image 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?

Explicitly enumerates the three triggering situations (user asks what's available, cost matters, verifying a model name) and then gives actionable routing advice: default to nano_banana_2 for images and seedance_1_5_pro for videos, stepping up only on explicit user requests for higher quality, face preservation, or audio.

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