lookup_food

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

No annotations are provided, so the description carries the full burden. It usefully discloses the dataset size (59 foods), the provenance (ICMR-NIN tables), the return shape (per 100 g and per katori plus a source URL), which is more than most; but it is silent on lookup failure behavior (misspelled or unknown food), matching semantics, and whether results are cached or live.

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?

A single dense sentence with no filler; the key content (what values, what units, what source) is front-loaded. It is slightly over-packed — dataset size and provenance could be trimmed — but nothing is wasted.

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 only two fully documented parameters and no output schema, the description supplies enough: it names the returned nutrients, both unit bases, and the source URL. The main remaining gap is behavior on an unrecognized food name, which an agent would need to handle.

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% and the enum for nutrient is fully documented, so the schema already does the heavy lifting. The description adds only marginal meaning beyond it by tying the four nutrients to the returned values and mentioning per-katori units, which is not a parameter concept.

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 names the resource (nutrition values for an Indian food), the specific fields (calcium, fibre, iron, protein), the units (per 100 g and per katori), and the data source (ICMR-NIN composition tables). It does not explicitly differentiate from siblings such as compare_foods or protein_cost, so it falls short of a 5.

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 only implied: an agent can infer this is for looking up a single food's nutrition, and the '59 foods' note hints at the dataset's bounded scope. There is no explicit statement of when to prefer compare_foods or protein_cost instead, and no guidance on what to do if a food is outside the 59.

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