Search Brazilian marketplaces from a product photo

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

With no annotations provided, the description carries the full burden and does well: it discloses the cost ('Paid ($0.05)'), the reliance on a vision model, the possibility of misreading ('if the model misread the photo'), and the behavior of returning interpreted_query. It could add more about failure modes or rate limits, but the disclosed traits are substantive.

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 three sentences, front-loads the core purpose in the first sentence, and every sentence adds value: what the tool does, how the output relates to search_products, a caveat about interpreted_query, and pricing. There is zero wasted text.

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 tool is moderately complex (vision model, paid, merged marketplaces) and has no output schema, so the description compensates by clarifying the return value (same as search_products plus interpreted_query) and the potential misread pitfall. It doesn't explain error handling or all return fields, but it is sufficient for a competent agent given the schema.

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 100%, so the description does not need to repeat parameter details. It adds some semantic context by explaining that the vision model reads 'brand, model and attributes' and the query runs across all marketplaces, but no parameter-specific guidance is given. The baseline of 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 specific action ('Search from a product PHOTO') and the resource ('Brazilian marketplaces'), and distinguishes it from siblings by noting it returns 'the same normalized offers as search_products, plus interpreted_query'. This makes the tool's unique purpose obvious.

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 the tool should be used when the user has a product photo, and it contrasts with search_products by noting the photo-based input and the added interpreted_query field. It does not explicitly state when not to use it or name an alternative, but the context is clear enough for an agent to select it appropriately.

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