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

Annotations declare readOnlyHint=true, so the description is not required to restate safety. It adds the behavioral detail of ranking by a value metric but does not disclose pagination behavior, data freshness, or how 'available benchmark signal' is computed.

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 a single, concise sentence (10 words) with no filler. It is front-loaded with the verb 'Rank' and efficiently conveys the tool's core functionality.

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?

The description is too brief given the tool has 4 optional parameters and a ranking operation. It lacks explanation of how the ranking is performed or how parameters influence results. The presence of an output schema partially compensates for missing return value details.

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?

Although schema description coverage is 100%, all parameter descriptions are minimal (e.g., 'provider', 'benchmark id'). The tool description provides high-level context but does not explain individual parameter usage, meaning, or valid values.

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 clearly states it ranks models by benchmark signal per token price, directly specifying the verb, resource, and criterion. This distinguishes it from sibling tools like find_cheapest (price-only) and compare_models (general comparison).

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or exclusions, leaving the agent to infer context from the tool name and description alone.

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