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

With no annotations provided, the description bears the full burden. It adds valuable behavioral context: every nutrient value reports how many sources contributed, which ones, and the per-source spread, and the data is reconciled rather than asserted. It could mention not-found handling, but the key non-obvious traits are disclosed.

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 with no waste: the first states what the tool returns, the second explains why the data is more trustworthy than single-source alternatives, and the third describes acceptable inputs. Every sentence earns its place and the key information is front-loaded.

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?

The tool is a simple read-only lookup with one required string and an optional filter, and the description covers input formats, data provenance, and the useful IFCT 2017 coverage edge case. Since there is no output schema, the description sufficiently predicts the return content. A minor gap is explicit guidance for handling unknown foods.

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, but the description adds concrete input examples for the food parameter ('bitter-gourd', 'karela', 'chickoo') and explains that the optional nutrients parameter filters the result. This goes beyond the schema's generic type descriptions.

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 leads with a specific verb and resource: 'Get the full per-100 g nutrient profile of a whole plant food.' It also distinguishes itself from sibling lookup tools by emphasizing multi-source reconciliation across up to 11 national datasets, which is a unique, clearly stated capability.

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 explains acceptable input forms ('slug, common name, or regional alias') and highlights a unique coverage area (Indian foods from IFCT 2017). It does not explicitly say when to use a sibling tool like search_foods instead, but the context is clear enough for routing.

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