get_us_event_reaction

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context: it uses only completed daily candles (excludes in-progress), and it automatically aligns the reaction based on session (pre/post). This goes beyond the annotations and helps the agent understand the tool's internal logic.

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 front-loaded with a clear one-line summary, followed by a concise rationale for the BMO/AMC distinction, and then a structured argument list. It is compact and every sentence earns its place. The structure makes it easy to scan and understand.

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?

The description covers the core logic and all parameters. Since an output schema exists, return value details are not needed. It does not explicitly mention edge cases like missing earnings data or handling of 'unknown' session, but the tool is fairly self-contained. It is complete enough for an agent to call correctly.

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%, but the description compensates fully with an explicit 'Args' section. It explains each parameter: ticker (US ticker), event_date (announcement date in YYYY-MM-DD), session (auto/pre/post/unknown with semantics), and after (number of trading days after, default 5, max 20). This is essential because the schema provides no descriptions, and the description fills the gap completely.

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 purpose: aligning US stock price reaction to the correct trading day based on earnings announcement session (BMO/AMC). It clearly distinguishes from generic get_event_reaction by focusing on US earnings and session-aware alignment. The verb '정렬' (align/sort) and resource '주가 반응' (price reaction) are specific and unambiguous.

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 explains the context (US earnings with pre/post market announcements) and the automatic alignment logic, making it clear when to use this tool. However, it does not explicitly name alternatives or state when not to use it, though the specificity implicitly excludes generic event reactions. It could be improved by referencing sibling tools like get_event_reaction for non-US events.

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