get_us_search

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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds useful behavioral context beyond that: search may return multiple candidates, and the agent must confirm with the user rather than guessing. This is meaningful but not extensive.

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 compact and front-loads the core purpose before examples and usage rules. There is minor bilingual redundancy ('US stock search' repeated), but the warning, examples, and Args section all earn their place.

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 read-only search with an output schema available, the description is complete: it explains what to search, when to invoke, and how to handle ambiguous results. No critical calling information 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 coverage is 0%, so the description carries the full burden. It defines query as '검색어 (회사명·티커, 한/영 무관)'—search term, company name or ticker, and language-agnostic. This fully compensates for the bare input schema and makes the single parameter unambiguous.

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 a specific verb and resource: US stock search / ticker lookup, with concrete example queries like 'Apple 티커' and 'Tesla symbol'. The 'US' qualifier and examples distinguish it from broader sibling search tools such as search and search_stock.

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?

It gives an explicit trigger condition: if the user provides only a company name like '애플' or '테슬라', call this tool first. It also prescribes the post-search behavior—ask the user to confirm when multiple results appear and never guess—which is actionable guidance an agent can follow directly.

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