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

No annotations are provided, so the description carries the full burden. It discloses the credit-back feature and its eligibility constraint but omits behavioral details like idempotency, rate limits, or side effects. The description adds moderate value beyond the schema.

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 of 24 words, front-loading the primary action and key options. Every word contributes value with no 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 the tool's simplicity (5 parameters, no nested objects, no output schema), the description covers the core functionality. It does not explain return values, which are absent from the output schema, but that is acceptable. The purpose and primary options are well-covered.

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 high (80%), so the schema documents most parameters. The description adds context about credit-back eligibility but does not elaborate on individual parameters beyond what the schema provides, meeting a baseline score.

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 verb 'submit feedback' on a 'generated image' with a specific voting mechanism (thumbs up/down). It distinguishes itself from sibling tools which are all image-editing operations.

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 after image generation, and the context is clear. However, it does not explicitly state when to use this tool versus other feedback methods or alternative tools, though no such alternatives exist among siblings.

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