get_car_scrapCheck whether a Korean vehicle has a scrap/total-loss accident record, by VIN or license plate number. 차대번호(VIN) 또는 차량번호로 폐차사고처리 여부를 조회합니다. 중고차 구매 전 확인 등에 사용합니다. [호출당 10포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | 조회 종류. 1: 차대번호(VIN), 2: 차량번호 | |
| value | Yes | 차대번호(type=1, 17자리) 또는 차량번호(type=2) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true, so the description adds value by stating this is a lookup/조회 operation and disclosing the cost '호출당 10포인트' per call. It does not describe output format, but the read-only, inquiry nature is clear.
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, with an English sentence, a Korean equivalent, a use-case line, and a cost note. Minor redundancy exists with the bilingual repetition, but every part adds useful context and no filler is present.
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 simple two-parameter lookup with readOnlyHint, the description is sufficiently complete: it states the purpose, input types, and cost. It does not explain the return format, but the word '여부' (whether or not) implies a boolean-like result, and no output schema exists to require further detail.
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%, so the schema already fully documents the type and value parameters. The description adds only a high-level mention of VIN or license plate, which does not materially surpass the schema's detailed Korean descriptions.
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 checks whether a Korean vehicle has a scrap/total-loss accident record by VIN or license plate. It uses a specific verb ('Check') and names the resource ('Korean vehicle scrap/total-loss record'), which distinguishes it from sibling tools like get_car_flooding.
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?
Provides a clear use case: '중고차 구매 전 확인 등에 사용합니다' (used for checking before purchasing a used car). This gives helpful context, though it does not explicitly mention when not to use it or name alternative tools.
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.