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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds genuine behavioral context beyond them: missing nutrients are silently treated as 0 and reported back, and the result is explicitly a draft requiring verification before printing. It stops short of describing the shape of the returned label/report, which matters because there is no output schema.

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?

Two dense sentences: the first front-loads what is produced, the second front-loads the input contract and the reliability caveat. Despite the long enumeration, every clause maps to a distinct output component or input rule, and nothing is repeated from the schema or annotations.

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 complex, deeply nested tool with no output schema, the description covers inputs semantics, the zero-fill behavior for missing nutrients, and the verification caveat. Gaps remain around what the returned label payload actually looks like and how the 'auto' format decision is made, but the essentials for correct invocation are present.

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?

The description supplies the single most error-prone semantic that the schema alone leaves ambiguous: ingredient quantities are batch amounts while nutrients are per 100 g of each ingredient. That prevents a category-level mistake an agent could easily make from the raw nested schema, and it complements the inline schema notes on RACC ids, density, and processing loss.

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 opens with a specific verb and resource ('Calculate a US FDA Nutrition Facts label from a formula') and enumerates the concrete outputs: FDA rounding per 21 CFR 101.9, %DVs, RACC-based serving size, servings per container, single vs dual column, ingredient/allergen/net-quantity statements, and nutrient-content claim checks. That scope is unmistakably different from the sibling lookup tools (list_racc_categories, search_label_ingredients), so an agent can select it without opening the schema.

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 by the input expectations ('from a formula', per-100 g nutrients, batch quantities) and by the closing caution that the label 'must be verified before printing'. However, there is no explicit statement of when to use this versus the sibling lookups (e.g., when RACC data or ingredient label names are unknown and should be fetched first), and no stated prerequisites or exclusions.

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