주식 배당 정보를 조회합니다.
Args:
company_name: 회사명 (예: "삼성전자", "SK하이닉스")
year: 결산년도 (예: "2025"). 빈 문자열이면 최근.
num_results: 조회 건수 (기본 20, 최대 100)
Returns:
배당률, 배당금, 배당기준일, 배당지급일
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| num_results | No | ||
| company_name | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It provides useful details: year empty means latest, num_results default 20 and max 100, and lists return fields. However, it omits error behavior, data freshness, or any side effects (though a query is naturally read-only). The description adds some behavioral context but lacks depth.
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 compact and well-structured, leading with the purpose and then listing parameters and returns in a clear block. Every part earns its place: the Korean opener, parameter explanations with examples, and return fields. No fluff or 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 the tool's low complexity and the presence of an output schema, the description covers the essential behavior: what it returns and how the parameters behave. It lacks explicit error handling or prerequisites, but for a simple query tool with all parameters optional, this is adequate. An agent can call it correctly with the given info.
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%, so the description must fully explain parameters. It does so effectively: company_name with examples, year with the empty-string meaning 'latest', and num_results with default and maximum constraints. This adds meaning well beyond the schema's bare titles and defaults, making it easy to invoke correctly.
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 states '주식 배당 정보를 조회합니다' (query stock dividend information), which is a clear verb+resource pair. It distinguishes itself from sibling tools like get_stock_price and get_price_trend by focusing specifically on dividend data, though it does not explicitly name alternatives.
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?
No guidance is provided on when to use this tool versus other stock-related tools. There is no mention of scenarios, prerequisites, or exclusions. The description only explains parameters, leaving usage context to the agent's inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.