event_study_guide

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

No annotations are provided, so the description carries full burden. It doesn't disclose any behavioral traits such as whether it generates code, provides explanations, requires specific inputs beyond parameters, or has side effects. The term '가이드' (guide) implies informational output, but this is not explicitly stated, leaving the tool's behavior unclear.

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, concise phrase that is front-loaded with the key term '사건연구(Event Study)'. There is no wasted text, but it may be overly terse given the lack of detail. It efficiently conveys the topic without redundancy.

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 no annotations, no output schema, and a vague description, the tool definition is incomplete. The description doesn't clarify what the tool produces (e.g., a report, code, or instructions), leaving significant gaps in understanding its function and output. For a tool with 3 parameters and complex context (event study design), this is inadequate.

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 100%, so the schema already documents all parameters (event_type, staggered, estimator) with descriptions and an enum for estimator. The description adds no additional meaning beyond what the schema provides, such as explaining relationships between parameters or usage examples. With high schema coverage, the baseline score of 3 is appropriate.

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 '사건연구(Event Study) 설계 가이드' (Event Study Design Guide) states the general purpose but is vague about what the tool actually does. It mentions '설계 가이드' (design guide) which suggests guidance or assistance, but doesn't specify a clear action verb like 'generate', 'create', or 'analyze'. It distinguishes from siblings by focusing on event studies, but lacks specificity about the output or function.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or compare to sibling tools like 'causal_design_guide' or 'timeseries_guide'. Users must infer usage from the title alone, which is insufficient for effective tool selection.

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