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

The annotations are neutral/uninformative, so the description carries the behavioral burden. It discloses useful non-obvious behavior: parallel execution, side-by-side answers, measured platform cost metadata, latency reporting, and that the beta platform covers the user charge ($0.00). This goes well beyond what the annotations or schema convey.

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 front-loaded with the core behavior and then adds cost, use-case, and an example. It is slightly longer than strictly necessary, but every sentence adds useful context rather than padding.

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?

Given that an output schema exists, the return values do not need to be explained in the description. The description covers purpose, cost implications, and the decision context. It is missing explicit alternative routing, but that is a minor gap.

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 description coverage is 100%, so the schema already documents all parameters. The description reinforces the core 'prompt' concept and provides an example URL, but it adds little parameter-level nuance beyond what the schema already states.

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 states a specific verb and resource: run one prompt across multiple LLMs in parallel and return answers side by side with cost metadata and latency. It clearly distinguishes this from sibling tools like ask_model or model_costs by focusing on comparison rather than single-model behavior or cost lookup alone.

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?

The description explicitly frames when to use it: 'which model should I actually use for this kind of task?' with data instead of guesswork. It gives clear context for use, though it does not explicitly name alternative tools or state exclusion criteria.

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