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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds real context beyond them: the upstream data source and coverage (Open Food Facts, 3M+ products), the scoring scale and buckets, and the internal reason breakdown (Nutri-Score, additive risk, processing level). It does not discuss miss behaviour or rate limits, which keeps it from a 5.

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?

Front-loaded with what it does and the data source, then the use case, then the precision tip. Three sentences, no padding. The first sentence is long and could be split, but every clause carries information.

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 previewing the return value (score bands plus reasoning categories). Combined with annotations that cover the safety and idempotency profile, an agent has everything needed to call this correctly without further lookup.

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 schema already explains all three parameters including the name-with-brand format and barcode example. The description's 'a barcode gives the most precise match' adds a preference ordering the schema does not state, but no other parameter detail is contributed. Baseline 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 gives a specific verb (looks up), a concrete resource (packaged food or drink in Open Food Facts, with dataset size), the two lookup keys, and the exact shape of the answer (0-100 score with named reason categories). This is distinguishable at a glance from explain_additive (single additive deep-dive) and find_healthier_alternatives (comparison shopping).

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 states the triggering question type ('whether a product is healthy') and a clear preference rule: a barcode gives the most precise match, so name search is the fallback. It stops short of naming the sibling tools or saying when to prefer them over this one, so it is strong context rather than a full routing rule.

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