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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful context about language support and query semantics, but it does not disclose return format, ranking, or pagination behavior. This is acceptable given the annotations but not richly transparent.

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 a single, front-loaded sentence with no filler. Every clause adds value: the search behavior, supported languages, and example query intents.

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 search tool with strong annotations, the description is nearly complete. It does not explicitly describe the return value, but the tool name and search framing make it clear that matching products are returned. A brief note on result ordering or limits would make it fully complete.

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%, so the description must compensate. It does so by explaining that the query should be a customer-style phrase in Thai or English and may reference an ingredient, concern, or brand. This adds meaningful guidance beyond the bare 'query' string field.

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 tool searches the shop using natural-language customer-style queries, with specific examples (ingredient, concern, brand). This distinguishes it from sibling tools like list_products or get_product, which imply structured listing or exact lookup.

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: when a search should be phrased as a customer would ask, in Thai or English. It does not explicitly name alternatives or exclusions, but the natural-language framing is enough to differentiate from the more structured sibling tools.

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