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

Annotations already cover readOnly/idempotent/openWorld/non-destructive, so the bar is lower, and the description still adds genuinely useful traits: ideas rotate daily (non-deterministic output), it deliberately refuses calorie/weight targets, and it emits shop links (with a no-shop-links carve-out for weight-loss). It does not describe the response shape or pagination, but the added context is substantive.

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-loaded with purpose, then examples, then the allowed values, then the exclusions — a logical progression with little waste. The opening sentence largely repeats the title verbatim, a minor redundancy that keeps it out of 5 territory.

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 no output schema, the description carries the return-value burden and does so acceptably by noting rough macros, rotating ideas, and shop links. Combined with the explicit scope exclusions, an agent has enough to call it correctly, though the exact response structure is only implied.

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 each parameter is already documented there, so baseline is 3. The description adds only marginal value beyond the schema — it clarifies that user phrasing belongs in q and reiterates the allowed goal/diet/meal values — without new syntax or interaction rules (e.g. how q combines with the structured filters).

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+resource (generate simple meal ideas) and enumerates the three axes it filters on (goal, diet, meal) plus rough macros. An agent can distinguish this idea-generation tool from the nutrition-lookup siblings (platepal_get_food_nutrition, platepal_get_barcode_nutrition) 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 concrete trigger phrases ("high protein halal dinner idea", "quick vegan lunch") and routes free-form user wording to q. It also defines the negative case (weight-loss requests) and its specific response behaviour. It never explicitly names an alternative sibling tool, so it falls just short of 5.

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