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

Discloses parallel execution, side-by-side answers, measured cost metadata, latency, and that the beta platform absorbs user cost. This adds meaningful behavior beyond the sparse annotations, though it does not dwell on output details.

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 tight sentences plus a concrete example URL. No filler; each sentence contributes either capability, cost context, or use-case 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 3-parameter, 1-required tool with a rich output schema, the description is complete. It covers the operation, the comparison value, cost behavior, and gives an invocation example.

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 schema already documents prompt, models, and system. The description adds no parameter-specific detail beyond reinforcing that ONE prompt is sent to multiple models, but with full schema coverage a baseline 3 is appropriate.

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 action: run one prompt across multiple LLMs in parallel and return answers side-by-side with cost and latency. This clearly differentiates it from siblings like ask_model, list_models, and model_costs.

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 frames when to use it: when the agent needs data to decide which model to use for a task, 'instead of guesswork'. Does not explicitly contrast with ask_model or model_costs, but the use-case context is strong enough to route an agent.

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