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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real value by enumerating what the response contains across ten-plus distinct data categories, which matters because there is no output schema. It stops short of stating failure behavior or coverage gaps (e.g., whether fields are null when demand forecasting hasn't occurred).

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?

A single dense sentence that front-loads the object ('공모주 하나의 상세') and then lists return categories in a scannable order. No filler or repetition, though the list is packed enough that an agent must parse comma-separated categories rather than distinct clauses.

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 single-parameter read tool with no output schema, the description carries the return-value burden and does so thoroughly. The remaining gap is operational: nothing about empty/unavailable fields, error cases, or the fact that this is the detail counterpart to the listing tool.

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?

With a single parameter at 100% schema coverage, the schema already defines corp_code as an 8-digit DART identifier and its provenance. The description adds nothing about the parameter itself, so this is the baseline case where the schema does all the work.

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?

Names a specific resource (a single IPO offering) and enumerates the exact data categories returned — price band, institutional demand-forecast ratio, retail subscription ratio, comparable companies, use of proceeds, post-listing performance. That is far more specific than the title alone, though the verb is implicit ('상세') rather than an explicit 'retrieve'. Differentiation from list_ipo_offerings exists only indirectly through the schema's reference to that tool.

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?

Usage is implied rather than stated: the schema tells the agent corp_code comes from list_ipo_offerings results, which effectively routes list-then-detail workflows. There is no explicit when-to-use/when-not-to-use statement and no mention of what to do if the code is unknown or invalid.

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