compute_nutrition_targets

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

With no annotations, the description carries the full burden. It discloses that the tool uses patient profile and latest labs and follows specific guidelines, which is useful. However, it does not state whether the operation is read-only or if there are side effects, nor does it mention behavior on missing data. The 'compute' naming implies pure calculation, but explicit safety details are absent.

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 a single, compact sentence that packs in the input basis, guideline names, and output nutrients. It is front-loaded with the core purpose and contains no wasteful words or repetition.

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 simple (one parameter) and has an output schema provided, so the description does not need to explain return values. It clearly states the necessary inputs (profile + labs) and outputs (nutrition targets). The only minor omission is handling of missing lab data, but given the output schema exists, this is not critical for an agent to decide invocation.

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?

There is only one parameter, patient_id, and the description indirectly references it by mentioning the patient profile. The schema has no description coverage, so the description must compensate. While patient_id is semantically obvious from the field name, the description does not explicitly state that it is the identifier used to fetch the profile and labs, leaving a slight gap.

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 uses a specific verb ('output') and resource ('individualized nutrition goals') and clearly lists the nutrient categories, distinguishing it from sibling tools like generate_meal_plan or get_labs. It also names the guideline versions (KDIGO 2024 / PRNT 2020), making its purpose unmistakable.

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 implicitly gives context by stating it uses the patient profile and latest labs, making it clear when this tool is appropriate (after those data are available). It does not explicitly name alternatives or exclusions, but the context is sufficient for typical use cases.

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