lookup_product

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds valuable behavioral traits beyond annotations: it costs 1 credit, guessing the wrong identifier_type returns no results, and ASIN is accepted as an input for validation. This goes beyond the schema and annotations without contradicting them.

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 well organized: purpose first, then use cases, then the critical semantic warning, the sibling alternative, and cost. It is slightly verbose with the metaphorical 'bridge' sentence, but every sentence contributes operational value and the key guidance is front-loaded.

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?

For a 3-parameter, no-output-schema tool, the description covers the input space, the main error condition, the sibling routing, and the credit cost. It does not detail the return payload beyond 'essential product snapshot,' but that is acceptable since no output schema exists and the core lookup purpose is clearly satisfied.

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 baseline is 3. The description adds meaningful parameter-level nuance: it stresses that identifier_type must match the code or results are empty, explains digit lengths for UPC/EAN/ISBN, and notes that ASIN is also a valid identifier_type for validation. This enriches the enum semantics beyond the schema's short descriptions.

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 precise verb-resource pair: convert external product identifiers (UPC, EAN, GTIN, ISBN, JAN, MINSAN) into Amazon ASINs. It further scopes the output as 'an essential product snapshot' and explicitly names search_products as the sibling for keyword queries, distinguishing this tool from alternatives.

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?

It gives explicit when-to-use context ('when the user has a barcode or standard product code') and a concrete when-not-to-use rule ('If the user gives a keyword instead of a code, use search_products instead'). It also warns that identifier_type must match and provides digit-length checks, which are actionable usage instructions.

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