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

With no annotations, the description carries the full behavioral burden and does so thoroughly. It discloses the crowd-sourced Open Food Facts source, uneven coverage, possible empty nutrition values, a completeness flag, nulls instead of estimates, not_found behavior, non-charge for not_found, and a specific cost. This is exceptional transparency for a lookup tool.

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?

Every sentence earns its place: the first states action and result fields, the second explains data quality behavior, and the third covers not-found handling and cost. It is front-loaded with the core operation and then layers necessary caveats without fluff.

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 simple single-parameter tool with no output schema, the description is remarkably complete. It covers expected response fields, data-quality caveats, completeness signaling, null behavior, not_found semantics, and cost. Nothing essential is missing for an agent to decide whether to call it and interpret the result.

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%, and the schema already provides the barcode format and an example. The tool description only repeats that it is a barcode lookup and adds no new parameter-level meaning beyond the schema. 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 uses a specific verb and resource ('Look up a packaged food by its barcode') and enumerates the exact fields returned. It clearly separates this tool from the unrelated siblings by anchoring it to barcode-based food lookup. The highlighted use case, 'can this person eat this?', adds practical purpose without ambiguity.

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 clearly frames when to use the tool: when you have a food barcode and need dietary-relevant facts. It provides helpful context about data source, coverage, and pricing. It does not explicitly name alternatives or exclusion conditions, but the sibling tools appear unrelated, so this is a minor gap.

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