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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds genuinely useful behavior beyond that: it is a paid call ($0.001, x402), it draws from external open databases, and wrong check digits are reported with the correct digit.

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?

Dense but front-loaded, leading with the core lookup action and code formats before the data-fetch and pricing details. Every clause carries information, though the long enumeration of formats and databases pushes it toward verbosity.

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?

With no output schema, the description compensates by naming the returned fields (name, brand, quantity, categories, image, labels, Nutri-Score, or title/authors/publisher/cover). Pricing and open-world sourcing are disclosed, leaving little an agent would need to call it correctly.

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 coverage is 100%, so both parameters are already fully documented in the schema. The description restates the lookup flag's effect but adds no new syntax or format detail beyond the schema, which is the expected baseline-3 situation.

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?

States a precise verb and resource ('Look up a product barcode number') and enumerates the exact code families handled, plus the downstream data fetch. This clearly separates it from siblings like read_barcode and generate_barcode, which imply different 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?

Gives clear context: validate/convert vs. fetching product data, with the 'lookup=false' path spelled out. It does not explicitly route the agent away from sibling tools such as read_barcode or generate_barcode, so the when-not guidance is implicit rather than stated.

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