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 declare readOnlyHint=true and openWorldHint=true, so the read-only nature is already known. The description adds the cost note '[호출당 10포인트]' (10 points per call), which is an important operational constraint not found in annotations. It also mentions the tool checks a report database, giving some behavioral context. It doesn't describe output format, but with annotations covering safety, this is adequate.
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 extremely concise—three short sentences in English and Korean that cover purpose, use cases, and cost. It front-loads the core function and wastes no words. The bilingual repetition is unnecessary but not bloated.
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 (1 parameter, no output schema), the description covers purpose, use cases, and cost. It does not mention what the output looks like (e.g., boolean or report details), but for a simple spam-check tool this is a minor gap. The annotations and schema cover safety and parameter format, so overall it is fairly complete.
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 coverage is 100%: the only parameter 'number' has description '전화번호 (예: 01012341234)' (phone number with example). The description adds no extra detail about the parameter beyond what the schema already provides. The baseline for high schema coverage is 3, and the description does not elevate it further.
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's function: 'Check whether a phone number has been reported for spam, advertising, or criminal use in Korea.' It uses a specific verb and resource, and is distinguishable from sibling tools like check_phone_valid (which likely validates number format). The Korean phrase repeats the same purpose, reinforcing clarity.
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 contexts for use: '수신 전화 필터링, 이상 거래 탐지 등에 사용합니다' (used for incoming call filtering, abnormal transaction detection, etc.). This gives practical guidance on when to invoke the tool, though it does not explicitly mention alternatives or exclusions. It does not name sibling tools, so it misses a clear 'when not to use' but still offers solid usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes; even within families like identi_card1-5 vs identi_card_image1-5, the text-input vs image-input distinction is clear. However, the sheer number of tools and some near-synonyms (e.g., ocr_identi1 vs identi_card_image1) could cause occasional misselection, but descriptions mitigate this.
Naming follows a loose verb-first pattern (check_, crawl_, download_, draw_, etc.) but includes significant deviations: bare nouns (bank_code, location, whois), numbered variants (identi_card1, identi_card_image1), and mixed prefixes (ocr_, identity_, etc.). The inconsistency is noticeable but still readable and predictable within functional clusters.
80 tools is far above the typical 3-15, but the server is a broad API aggregator covering many independent domains (banking, ID verification, media conversion, search, LLM, etc.), so the high count is somewhat justified. Still, the sheer number makes the toolkit feel unwieldy and hard to navigate, placing it at the high end of acceptable.
Within its stated purpose as a general-purpose utility API, the toolset covers a wide array of common task families: identity document verification (text and image), OCR field extraction, media conversion, web/search, domain/IP lookup, and LLM chat. Most operations have both get and act variants (e.g., set/get watermark, parcel_tracking/auto), with few obvious dead ends for typical use cases.