get_totals누적 방문 · 주문 수와 끝난 월까지의 월별 합계를 돌려줍니다(표본 규모 확인용). 맛보기 한도를 1회 씁니다.
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_totals"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| agent | No | 이 도구를 부르는 AI 에이전트 · 서비스 이름(예: Claude, ChatGPT). 밝히시면 하루 한도가 늘어납니다 |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description adds two things the annotations do not: the data window ('끝난 월까지' – up to the last completed month) and the cost of calling it ('맛보기 한도를 1회 씁니다'), which is genuinely useful for budgeting calls.
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 short sentences with the return content front-loaded and the quota cost appended as a caution. Nothing is repeated and no sentence could be removed without losing information.
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 no-parameter, no-output-schema aggregate tool with strong annotations, the description covers what is returned, the time boundary, and the call cost. It is slightly short on how it differs from get_data_status and whether it is the recommended first call to gauge whether a dataset is large enough for other analyses.
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% and the single optional 'agent' parameter is fully documented in the schema, including its quota incentive. The description adds no parameter-level information, so the baseline of 3 applies.
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: it returns cumulative visit/order counts plus monthly totals through the last completed month, and labels the purpose as sample-size verification. An agent can tell this is an aggregate-counts tool, though it never names the sibling it should prefer over (e.g. get_data_status or get_distribution).
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 parenthetical '표본 규모 확인용' implies the use case, and the quota warning gives a reason to be sparing, but there is no explicit when-not-to-use guidance or named alternative among the many sibling get_* tools. The agent must infer selection from context alone.
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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