get_data_status인용 전에 확인할 리포트별 데이터 상태(데이터 점검 중 · 확인 필요 · 주의)와 사유를 돌려줍니다. 맛보기 한도를 쓰지 않습니다.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "get_data_status"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| code | No | 리포트 코드 4자리(비우면 전체) | |
| include_ok | No | 이상 신호가 없는 리포트도 포함(기본 false) |
Changes observed during successful MCP inspections.
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 genuinely new behavioral context that annotations cannot express: the call does not consume the "맛보기" (trial/preview) quota, and it returns reasons alongside status. It stops short of explaining what drives each status, hence not a 5.
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?
Two tight sentences: purpose and return content first, the quota exemption second. Nothing is wasted and the key 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?
With no output schema, the description carries the burden of describing the return value, and it does so by naming the three status levels and the accompanying reasons. Combined with rich annotations and fully documented optional parameters, an agent has everything needed to call it, though the meaning of the individual status values is left implicit.
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 both parameters (code, include_ok) are fully documented in the schema. The description only adds "리포트별", implying the default granularity is per-report, which is marginal value — baseline 3 is appropriate when the schema already carries the parameter detail.
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 gives a specific verb and resource ("리포트별 데이터 상태...와 사유를 돌려줍니다") and even enumerates the three status values, so an agent knows exactly what comes back. It does not name a sibling tool or explicitly contrast itself with get_totals/get_ai_insights, so it falls just short of full sibling differentiation.
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?
"인용 전에 확인" clearly states the trigger context — call this before citing/reporting figures — which is actionable usage guidance. It offers no when-not conditions or named alternatives, but for a pre-check tool the single condition is the meaningful one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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