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

With no annotations provided, the description carries the full disclosure burden and does so thoroughly: it flags crowd-sourced data, uneven country coverage, possible empty nutrition fields, null instead of estimated missing values, a completeness flag on every response, not_found behavior, and per-call cost via x402. This is far beyond what structured fields provide.

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 dense but every sentence earns its place: the core function is front-loaded, followed by data-source caveats, return behavior, and pricing. There is no filler or repetition.

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 single-parameter lookup with no output schema, the description is remarkably complete: it explains input, expected fields, data quality caveats, missing-value handling, not_found behavior, and cost. An agent has enough context to invoke it correctly 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%, so the schema already documents the barcode parameter fully, including format, length, and an example. The description adds no new parameter-level meaning beyond reiterating that lookup is by barcode, so the baseline of 3 applies.

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 verb-resource pair ('Look up a packaged food by its barcode') and lists the exact fields returned. It also frames the purpose around a clear user question ('can this person eat this?'), which distinguishes it from the generic sibling tools demand_report and request_data.

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 for when the tool is appropriate: retrieving dietary-relevant food facts from Open Food Facts. It does not explicitly name alternative tools or state when not to use it, but sibling names are generic enough that no exclusion is strictly necessary.

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