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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe read and potentially varying output. The description adds context that the output is a list of specific section codes and Korean names, and that these codes are inputs to other tools. This provides useful behavioral context beyond 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 a single sentence that immediately conveys the tool's purpose, usage context, and output details. No wasted words.

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 the tool has zero parameters and a simple purpose, the description is fully complete. It explains what the tool does, when to use it, and what the output contains. The presence of an output schema further fills in details.

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 input schema has no parameters, so baseline is 4. The description adds meaning by explaining the output structure (section codes and Korean names) and their use case, though not directly about parameters.

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 returns section codes and Korean names used for other tools like list_popular_news and list_recommended_articles. It uses specific verbs '가져옵니다' (brings) and explicitly lists the codes. This distinguishes it from sibling tools that fetch news articles.

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 explicitly specifies when to use: when users ask '어떤 분야가 있어?' (what sections are there?) or '뉴스 카테고리 목록' (news category list), or when checking section codes. It implicitly guides not to use for article retrieval.

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