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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that values are aggregated across up to 11 datasets, reports per-source contribution and spread, and covers IFCT 2017. This goes well beyond a generic lookup description, though it does not describe error handling or exact return formatting.

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 compact and front-loaded. The first sentence states the core action and scope, the second explains the key differentiator, and the third covers input flexibility and unique coverage. Every sentence contributes necessary information without redundancy.

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 tool with no output schema and no annotations, the description does well by explaining what data will be returned (nutrient profile, source counts, spread) and how inputs are accepted. It does not specify the exact response structure or behavior for unknown foods, but what is present is sufficient for correct selection and invocation in most cases.

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 100%, so the baseline is 3. The description adds meaningful value by clarifying accepted input formats for 'food' with examples ('bitter-gourd', 'karela', 'chickoo') and explaining that omitting 'nutrients' returns the full ~168-nutrient profile. These details help an agent construct valid calls more confidently.

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: 'Get the full per-100 g nutrient profile of a whole plant food.' It also differentiates the tool from siblings by emphasizing multi-dataset reconciliation and Indian-food coverage, so an agent can distinguish it from search_foods, nutrient_ranking, and get_recipe.

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 provides clear context for when to use this tool: when a detailed, source-reconciled nutrient profile is needed, including regional Indian foods not available elsewhere. It does not explicitly name alternatives or state when not to use it, but the specialization and input format guidance make the intended use clear.

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