get_car_floodingCheck whether a Korean vehicle has a flood damage record, by VIN or license plate number. 차대번호(VIN) 또는 차량번호로 자동차의 침수 이력 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?
With readOnlyHint=true and openWorldHint=true annotations already declaring the safety profile, the description adds the cost aspect ('[호출당 10포인트]' = 10 points per call) and the query scope (flood history by VIN/plate). This gives useful operational context beyond the annotations, though it does not detail response format or rate limits.
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 short and front-loaded with the English purpose, then the Korean equivalent, then the use case and cost. Slight redundancy exists from bilingual repetition, but every sentence serves a purpose and the cost note is placed at the end without clutter.
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 read-only lookup with robust annotations and full schema coverage, the description provides all essential context: what the tool does, how to invoke it (by type/value), and when to use it. The lack of an output schema is mitigated by '~여부' (whether or not) implying a yes/status response, which is adequate.
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 already fully describes both parameters (type: 1=VIN, 2=plate; value: VIN or plate number). The description mentions 'by VIN or license plate' but adds no syntax, constraints, or default behavior beyond the schema. Baseline 3 is appropriate given the 100% schema coverage.
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 a specific action—'Check whether a Korean vehicle has a flood damage record'—with explicit input methods (VIN or license plate). This uniquely distinguishes it from sibling tools like get_car_scrap, which covers scrap history rather than flood records. The bilingual title and description reinforce the purpose.
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 includes a concrete use case—'중고차 구매 전 확인 등에 사용합니다' (used for checking before buying a used car)—which helps an agent know when to select this tool. It does not explicitly contrast with alternatives like get_car_scrap, but the context is clear enough for a straightforward lookup tool.
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.