get_event_reaction

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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses that when analysis is impossible (outside data range, trading halt, missing supply/demand), it returns a validation status and code rather than fabricating numbers. It also explicitly states it does not assert causation or make trading recommendations. This is significant behavioral context that adds value beyond the annotations.

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 well-structured: a one-line summary, then usage context, then behavioral notes, then an Args list. It is somewhat long but every sentence adds value. The key purpose is front-loaded. Minor deduction for verbosity in the usage examples, but it remains efficient.

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 read-only tool with an output schema, the description is complete. It covers all parameters, explains edge-case behavior (validation status), and clarifies the tool's limitations. An agent can confidently call this tool correctly without additional information. The presence of an output schema means return values need not be described.

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?

The schema has zero description coverage (0%), but the description's Args section thoroughly explains each parameter: code as a 6-digit stock code, event_date with format options and a recommendation, before with default 5 and max 60, after with default 20 and max 60. This fully compensates for the schema gap and provides meaning beyond the bare schema definitions.

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's purpose: it sorts price, volume, and supply/demand reactions around a specific event date. It uses a specific verb (정렬합니다) and resource (주가·거래량·수급 반응), and differentiates from the US counterpart by its focus on Korean events. It also mentions related tools (scan_earnings_season, list_disclosures) that feed into it, giving strong context.

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 explicit usage guidance: it tells the agent to pass the disclosure acceptance date from scan_earnings_season or list_disclosures, and explains it is for time-axis alignment, not for causal claims or buy/sell decisions. However, it does not explicitly contrast with the sibling get_event_reactions (plural), so it's not fully exhaustive but clear enough.

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