compare_models

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

Adds value beyond annotations by stating the tool costs 1 credit ($0.02) and produces side-by-side rankings. Annotations already confirm read-only and non-destructive nature, so description fills remaining gaps.

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?

Two concise sentences: first describes the full functionality and value, second states the cost. Every sentence adds essential information without redundancy.

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 no output schema, the description adequately hints at the output (side-by-side comparison with rankings) but could briefly mention the presentation format. Still sufficient for an AI agent to understand the tool's purpose.

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?

With 100% schema coverage, the description nonetheless adds critical context: the acceptable range of 2-5 models, comma-separated format, and concrete examples of both IDs and display names, making usage unambiguous.

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?

Clearly states the verb 'Pick between models' and specifies the resource (models) and the provided dimensions (pricing, benchmarks, status, news) with rankings, distinguishing it from sibling tools that focus on individual aspects.

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?

Implicitly clarifies when to use: for multi-model comparison across several criteria, avoiding scraping each provider. Does not explicitly list negative use cases or alternatives, but the sibling context makes differentiation clear.

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