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

With no annotations, the description carries the full burden, and it does disclose that three specific datasets are consulted, which is meaningful scope information. It does not state read-only nature explicitly, permissions, or how results from the three sources are combined, but the word 'check' and the dataset list convey a safe lookup operation.

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 tight sentence, front-loaded with the action and the three data sources, with zero filler. Nothing to trim.

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 simple one-parameter lookup with no annotations, the description adequately conveys scope but omits what the response looks like (whether it returns matched records, a boolean, or per-dataset breakdowns) and gives no output schema to compensate. An agent can call it correctly but cannot anticipate the return shape.

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% for the single 'name' parameter, so the schema already documents it as '產品或品牌名稱'. The description reinforces that the input is a product or brand name but adds no format, matching, or language details beyond the schema, so baseline 3 applies.

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 action (cross-check a product/brand across three named datasets: drug recalls, illegal food ads, international food alerts) and clearly distinguishes itself from the single-dataset siblings get_drug_recalls, get_food_ad_violations, and get_international_food_alerts by aggregating all three at once. It stops short of explicitly naming those sibling tools as alternatives, but the enumerated scope effectively differentiates it.

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?

Uses 'if a product or brand has any records' to imply a screening/existence-check use case, which is adequate implied guidance. However, it never states when to prefer this aggregate check over calling the three individual getter tools, nor any prerequisites or restrictions on input.

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