Retrieves the list of stock market sectors from Naver Securities, sorted by daily change. Use it to answer questions about sector performance, rankings, and gainers/losers.
업종목록 — 네이버 증권의 업종(섹터) 목록을 가져옵니다. "업종별 현황", "섹터 리스트", "업종 등락률" 같은 질문에 사용합니다. 약 79개 업종이 전일대비 등락률 순으로 정렬됩니다.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
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| result | Yes |
Changes observed during successful MCP inspections.
v0.4.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive, covering the safety profile. The description adds useful behavioral context: it returns approximately 79 sectors and sorts them by daily change rate. This goes beyond the annotations but is not extensive; there is no mention of any other side effects or edge cases, which is acceptable given the simple read-only nature.
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 concise: three sentences. The first sentence states the core action, the second gives usage examples, and the third notes the output size and sort order. There is no wasted text, and the essential information is front-loaded.
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 no parameters, an output schema (present), and annotations covering safety, the description is largely complete. It states what is returned and how it is ordered. It does not mention any caveats like possible latency or regional specificity, but these are minor and likely covered by the output schema or not critical for a simple list retrieval. Overall, it is adequate for an agent to call correctly.
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 tool has zero parameters, so the schema covers all parameters trivially (100% coverage). The description adds no parameter information because none is needed. Per the baseline, a tool with 0 parameters gets a 4, and the description does not introduce any ambiguity.
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 fetches the list of industries (sectors) from Naver Securities, sorted by daily change rate. It uses a specific verb and resource, and the examples of user queries ('industry status', 'sector list', 'industry fluctuation rate') clarify intent. However, it does not explicitly differentiate from sibling tools like list_themes or get_sector_stocks, though the resource (sectors vs themes) and the overall list nature are implied.
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 clear usage context by listing example questions that should trigger this tool, such as 'industry status' and 'sector list'. It does not explicitly state when not to use it or mention alternative tools for related but distinct needs (e.g., getting stocks within a sector), so it stops short of a full when/when-not guide, but the context is sufficiently clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.