get_event_reaction

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

With no annotations, the description fully discloses edge-case behavior: unanalyzable windows return a validation status/code, missing data is not shown as 0% or zero net trading, and non-trading days shift to the next trading day. This goes beyond a simple operation summary.

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 organized into purpose, usage context, behavioral caveats, and parameter details. Every sentence carries operational value, and the final Args block is compact and consistent with the schema.

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?

Given four parameters, no annotations, and no schema descriptions, the description covers purpose, usage, edge cases, and parameter semantics thoroughly. An output schema exists, so return-value details are not required.

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: it explains code format with an example, accepted event_date formats, and before/after defaults and maximums. This adds meaning the schema lacks.

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 opens with a specific action: sorting price/volume/flow reactions before and after a given event_date. It clearly names the resource (event reactions) and scope (time-axis alignment around disclosures), and differentiates itself by stating it is for temporal alignment, not causal or buy/sell judgment.

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 explicitly says to pass disclosure receipt dates from DartLens scan_earnings_season/list_disclosures and notes holiday handling. It also states when not to use it ('원인 단정·매수/매도 판단 아님'), but it does not name alternative sibling tools, so it falls just short of a 5.

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