Rescale a list of ingredients

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

Beyond the readOnly and idempotent annotations, the description explains per-line outcomes ('scaled', 'rounded', 'unscaled'), states that no unit-system conversion occurs, and notes that approximate measures like a pincée retain their own size. This is meaningful behavioral disclosure.

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 efficiently structured: purpose and mode first, then return behavior, then boundary constraints. Every sentence adds useful information without repetition or fluff.

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 has complete schema coverage, meaningful annotations, an output schema, and the description covers key edge behavior such as unscaled lines and approximate measures. An agent has enough to invoke it correctly and interpret results.

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 coverage is 100%, so the schema already documents every parameter. The description reinforces the either factor or from_servings/to_servings relationship and clarifies 'together', but it does not add substantial new parameter-level meaning 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?

The description states a specific verb and resource: it rescales a list of French ingredient lines, offline. It clearly distinguishes this transformation tool from the sibling recipe lookup tools by emphasizing the computational and offline nature.

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 gives clear context for when to use the tool: rescaling ingredient lines offline, with either a factor or serving-count conversion. It does not explicitly name alternatives or exclusion conditions, but the purpose is sufficiently distinct from the sibling tools.

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