check_spam_numberCheck whether a phone number has been reported for spam, advertising, or criminal use in Korea. 스팸/광고/범죄에 사용된 전화번호인지 조회합니다. 수신 전화 필터링, 이상 거래 탐지 등에 사용합니다. [호출당 10포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| number | Yes | 전화번호 (예: 01012341234) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds context about the cost per call, the Korean geographic scope, and the types of reports checked, which goes beyond the annotations without contradicting them.
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 appropriately short, front-loaded with the core purpose, and includes a bilingual translation and cost note. Every sentence adds value without unnecessary repetition, making it highly concise and well-structured.
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 simplicity (one parameter, no output schema, and strong annotations), the description covers purpose, use cases, cost, and scope quite well. However, it does not explicitly state the return format, though it is implied by the verb 'check', so it falls just short of a 5.
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 100% coverage for the single 'number' parameter, including a format example. The description does not provide additional parameter details beyond what the schema already specifies, so it earns the baseline score of 3.
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 specifically states the tool checks phone numbers for spam, advertising, or criminal use in Korea, using a clear verb and resource. This clearly distinguishes it from siblings like check_phone_valid, which likely only validates number format.
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 explicit use cases (incoming call filtering, anomaly detection) but does not mention exclusions or direct alternatives. While context is clear, it lacks explicit guidance on when not to use this tool, so it doesn't achieve a 5.
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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Most tools target distinct resources and actions, but there is potential overlap between biz_detail and venture_biz_info, and the PCCC tools (req_pccc, get_pccc, check_pccc) require careful reading. Descriptions are clear enough to avoid major misselection.
Tool names mix verb-led patterns (check_*, get_*, req_*, search_*) with noun-phrase patterns (biz_detail, holiday_info, land_rt_price, parcel_tracking). This inconsistency makes it less predictable than a uniform verb_noun scheme, though names remain readable.
With 16 tools, the set is slightly above the typical 3-15 range, but the broad scope of the API—spanning business info, personal validation, car history, parcel tracking, and address search—justifies the count. No tool feels redundant.
The API covers its core verification workflows, but notable gaps exist: no tool for listing parcel carriers despite being referenced in parcel_tracking, no business search by name, and car history only covers flooding and scrap, missing general accident records. These gaps could limit agent workflows.