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

The annotations already establish safety characteristics (read-only, idempotent, non-destructive), and the description adds behavioral value by specifying the result ordering: verified first, then claimed, then listed. It also discloses the dependency on decision_id and the claim-status dimension of the records. It does not over-explain, but it goes beyond the annotations usefully.

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 two short sentences with no filler. The core purpose and key output ordering are front-loaded, and the required input dependency is stated in the second sentence. Every clause earns its place.

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 core inputs, output type, and ordering behavior are all represented, and the presence of an output schema reduces the need to describe return shapes. The description could be more explicit about how gtin versus query are used, but overall it is sufficient for an agent to understand what the tool does and what it needs. Given the moderate parameter count and supportive annotations, this is a solidly complete description.

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 0%, so the description must compensate for parameter meaning. It explains decision_id ('of an allow decision'), market ('in a market'), and product scope ('this product or product type'), which loosely maps to gtin and query. However, it does not clarify the distinction between gtin and query or mention limit, leaving some parameter semantics to inference from names.

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 states a specific resource—supplier records for brands and manufacturers—and clarifies the scope as a product or product type in a market. It also adds distinguishing output details (claim status and ordering) that set it apart from sibling tools like verify_gtin or get_item. Though it lacks a direct verb, the intent is immediately clear from the phrase 'Verified makers and brands for...'.

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 gives clear context: use it to fetch verified supplier records for a product or market, and it requires the decision_id from an allow decision. It includes a meaningful prerequisite but does not explicitly name alternatives or say when not to use this tool. This is clear context without formal exclusions.

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