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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds value beyond that: it describes the return ('the same 0-100 score and reports any missing data') and discloses that missing data is tolerated rather than fatal, which no structured field conveys.

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?

Three sentences, purpose front-loaded, then input-fidelity guidance, then return behavior. No filler, though the middle sentence packs several distinct ideas (source of data, fidelity, E-numbers, bracketed words) into one run-on.

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 usefully states the return (0-100 score plus a missing-data report). For a 7-parameter tool with a nested nutrition object, it covers the key decision points (which inputs, what basis) without being exhaustive; the remaining gap is the lack of interplay guidance between category/is_drink and the nutrition basis.

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 71%, so many fields (salt_g, sodium_mg, serving_size, basis) are self-documenting. The description still earns credit by explaining input fidelity requirements ('exactly as printed, including E-numbers and words in brackets') and by clarifying that ingredient list and nutrition facts are alternatives and that per-100 g versus per-serving are both acceptable inputs.

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 specific verb and resource ('Scores a food from its label') and enumerates the accepted inputs (ingredient list and/or nutrition facts, photo or pasted text). The clause 'including products that aren't in the database' implicitly separates it from a lookup tool like check_food, but no sibling is named explicitly.

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?

Usage is implied rather than stated: use this when the label is available or the product is not in the database. No explicit routing versus check_food, find_healthier_alternatives or explain_additive, and no statement of when this tool is the wrong choice.

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