score_product

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

Given no annotations, the description fully covers behavioral traits: explains that scores are category-specific and never merged, details each category's sub-scores (e.g., food: nutrition/additive/processing/organic), describes per-lens fit, and states that data_quality/coverage is always included. This is comprehensive.

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 reasonably concise, starts with the main purpose, and adds necessary details about categories and data quality. It could be slightly shorter, but every sentence contributes useful information. It is well-structured.

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?

Without an output schema, the description must explain return values. It does so thoroughly: category-specific scores, per-lens fit, match coverage, and data_quality/coverage. The parameter schema is complete, so the description compensates for missing output schema.

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?

The input schema has 100% coverage, describing all three parameters. The description adds significant value by explaining the meaning behind the output and category-specific logic, which goes beyond the schema's parameter descriptions. It does not repeat parameter details but enriches context.

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 it returns a product quality score by barcode, with category-specific scores for food, skincare, and supplement. It also mentions per-lens fit with match coverage. This distinguishes it from sibling tools like strain_lookup or check_ingredient, which serve different purposes.

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 when to use this tool (to get a quality score by barcode) but does not explicitly state when not to use it or provide alternatives. However, the unique nature of the tool (scoring products) is clear, and the sibling list hints at other tools.

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