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

No annotations are provided, so the description carries the burden of behavioral disclosure. It usefully states that the tool is deterministic and uses no LLM for calculation, but it does not mention return shape, error handling, or how optional financial parameters affect the result.

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 two concise sentences with the purpose front-loaded and a concrete example. The deterministic note adds useful information without unnecessary verbosity.

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?

For a tool with nine parameters, no output schema, and no annotations, this description is too thin. An agent would struggle to know how to populate most optional parameters or what the normalized result looks like, and sibling differentiation is missing.

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% and there are nine parameters, so the description must compensate. It illustrates the text parameter with examples like '6x330 ml' and implies price, but leaves tax, discount, dilution, shipping, currency, yield_pct, and label semantically unexplained.

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 clearly states the operation: normalizing a product pack such as '6x330 ml' into comparable quantity and effective unit price. It describes a specific transformation and resource, though it does not explicitly name sibling tools to differentiate them.

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?

Usage is implied: use this when a raw pack string and price need to be converted into a normalized quantity and unit price. However, there is no explicit guidance about when to prefer this over siblings like compare_pack_values or calculate_delivered_unit_cost.

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