compute_targets

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

Annotations already cover safety (readOnly, idempotent, closed-world), and the description adds real value beyond them: the output includes the formula and inputs, a calorie floor is applied, results are estimates not medical advice, and nothing is stored. It doesn't mention validation failures (e.g., age outside 18-100) or rounding behavior, keeping it below a 5.

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?

Four tight sentences: what it computes, when to use it, what the result contains, and a disclaimer. The purpose is front-loaded and no sentence is filler.

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?

An output schema exists, so the description needn't detail the return shape, yet it still flags the formula echo, calorie floor, and disclaimer — useful for an agent deciding whether to trust the numbers. The only gap is silence on input validation boundaries defined in the schema.

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 0%, so the description carries the burden; it explains that 'activity' is a multiplier and 'goal' is a goal adjustment, adding meaning beyond the enum lists. The remaining four parameters (sex, age, height_cm, weight_kg) are left to titles and min/max bounds, so compensation is partial.

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?

States a specific verb and resource: 'Estimated daily calorie and protein targets for an adult,' plus the exact method (Mifflin-St Jeor with activity multiplier and goal adjustment). This cleanly separates it from the sibling retrieval tools (search_food, get_nutrition, find_foods) and the adjacent compute_meal.

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?

'Use it when asked how much someone should eat in a day' gives a clear triggering context. It stops short of naming alternatives (e.g., use compute_meal for a per-meal breakdown), so differentiation is implied rather than explicit.

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