compute_meal

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

Annotations only cover the read-only/idempotent/closed-world profile, and the description goes well beyond them: atomic failure behavior ('If an item fails, the error names it and no totals are returned'), lower-bound semantics via `total_incomplete`, partial coverage of glycemic load, the no-cooking-loss caveat for folate/vitamin C/B12, 2-decimal rounding, and null-vs-0 convention. These are exactly the traits an agent needs to interpret results correctly.

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?

Front-loads purpose and scope in the first clause, then layers caveats in decreasing order of importance: failure atomicity, lower bounds, cooking-loss caveat, rounding/null convention. Dense but every sentence carries an operational fact; slightly long, though nothing 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, yet the description still pre-explains the fields an agent is most likely to misread (`total_incomplete` as lower bounds, `glycemic_load_missing`, `raw_ingredient_totals`). Combined with the error and rounding semantics, an agent can call this and correctly interpret edge-case results with no further information.

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?

Top-level schema description coverage is reported as 0%, so the description has to compensate, and it does partially: it fixes the item count range and routes the agent to get_nutrition for argument format. However, the amount/unit/food_id semantics it relies on are only implicit ('each a food id, amount and unit'), and the nested $defs do the real work, so this is adequate rather than additive.

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 (compute) and resource (meal nutrients and glycemic load) with explicit scope: 1 to 30 items, each a food id + amount + unit. The mention of 'as in get_nutrition' and the totals framing lets an agent distinguish it from the single-item sibling get_nutrition and from search_food/find_foods without opening any schema.

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?

Gives a clear directive: 'Use this for any total instead of adding numbers yourself,' which tells the agent when to reach for this over manual arithmetic or repeated get_nutrition calls. It stops short of explicitly excluding the single-item sibling or naming a failure-mode alternative, so it is clear context rather than full when/when-not guidance.

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