Look up a food by name and scale it in one step

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

With no annotations provided, the description carries the burden of disclosure. It explains key behavior: automatic best-match selection, nutrient scaling, and returning runner-up matches for correction. However, it does not mention how data_types affects the search or what happens on no match, but the core behavior is transparent.

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 three sentences: the first defines the core action, the second gives a concrete use case, and the third discloses a valuable feature. Every sentence earns its place, and the most important 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?

Given no output schema, the description does a good job of explaining what is returned: nutrients scaled to grams and runner-up matches. It is reasonably complete for a simple lookup tool, though it omits details about the data_types parameter and ambiguity handling.

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 adds meaning to 'query' (search by name) and 'grams' (scaled to given grams), but the 'data_types' parameter is entirely unexplained. With schema coverage at only 33%, the description should compensate for all parameters, leaving one key filter without any semantic guidance.

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 clearly states the tool searches by name, picks the best match, and returns nutrients scaled to grams. It also distinguishes itself from siblings by emphasizing the one-step scaling feature, making the purpose specific and unambiguous.

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?

It provides an explicit use case: "Use this for ordinary 'how much magnesium is in 30g of pumpkin seeds' questions." While it doesn't explicitly state when not to use it or name alternative sibling tools, the example strongly conveys the intended scenario.

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