check_pricing

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

No annotations are provided, so the description must disclose behavioral traits. It only states the purpose, without mentioning whether the operation is read-only, requires authentication, has rate limits, or any other behavioral details. This is insufficient for a tool without annotations.

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 sentence that is concise, front-loaded with the action, and contains no unnecessary words. It efficiently delivers the core purpose.

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 there is no output schema and the tool is simple, the description should explain what the return value (pricing info) entails. It does not, leaving the agent without full context. The description is too minimal for complete understanding.

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 input schema describes both parameters with enums and descriptions, achieving 100% coverage. The description adds no additional parameter meaning, so 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?

The description clearly states the action ('look up') and resource ('RunAPI pricing') and specifies the model line ('gpt-4o-image'). It effectively distinguishes from sibling tools 'get_task' and 'text_to_image', which serve different purposes.

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 implies usage for pricing queries, but does not explicitly state when to use this tool versus alternatives or provide guidelines on prerequisites or exclusions. The context is implied by the name and sibling differentiation.

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