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

No annotations are provided, so the description carries the full burden, and it does substantial work: it names the data source, mandates attribution with the exact `url`/`cite` from each result, and disclaims that results reflect official records rather than being a safety rating, lab test, or medical advice. It omits auth/permission requirements and pagination behavior, but the source and interpretation caveats are valuable beyond-structured context.

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?

Front-loaded with the core operation and cap, then layers attribution and disclaimer boilerplate. The compliance sentences are long but arguably earn their place given the stated citation contract; the description is dense but not padded with trivia.

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 and no annotations, the description still conveys what a result contains (`url`/`cite` fields), the source provenance, and the interpretation limits. For a 3-param read tool this is nearly complete, missing only matching rules and result-shape detail.

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 0%, so the description must compensate, and it does: it explains `q` as a word matched against product or brand name, `brand` as an optional single-brand filter, and conveys the 10-item cap that aligns with the `limit` maximum. It doesn't clarify matching semantics (substring vs exact, case sensitivity), leaving a minor gap.

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?

States a specific verb and resource with scope: find K-beauty products by a word in the product or brand name, capped at 10 and optionally scoped to one brand. This distinguishes it from get_product (single lookup) and search_ingredients (different resource), though it doesn't name siblings explicitly.

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?

Implies usage via 'find products by a word in the product or brand name' and the optional brand narrowing, but never states when to prefer this over get_product or other search tools, nor any exclusions. Usage must be inferred from the semantics.

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