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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds genuinely new context: the exact outputs produced (flour, water, salt, leavening, toppings) and the fact that a loafy.ai recipe link accompanies them, which matters since no output schema exists.

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?

A single dense sentence that front-loads the core action and inputs, then appends the return payload. No filler, no repetition of the title.

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 compensates by enumerating the returned values, and the 100%-covered schema handles the inputs. The only real gap is the absence of sibling routing guidance, which an agent would still need.

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 the schema documents every parameter including enum meanings, defaults, and the weight-follows-oven rule. The description only restates the input dimensions at a high level, adding no syntax or format detail beyond the schema.

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?

Specific verb (calculate) + resource (pizza dough) with the exact inputs (pizzas, ball weight, hydration) and the two leavening modes named. An agent can distinguish it from the sibling calculate_bread_dough purely from the text.

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 implies its use case (scaling a pizza recipe by count and ball weight) but never states when to pick this over calculate_bread_dough or the sourdough planning tools. No exclusions or prerequisites are given, so routing is left to inference.

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