biz_detailLook up general status information of a Korean business by its 10-digit business registration number. 사업자등록번호로 해당 사업자의 일반 현황 정보(대표자, 주소, 직원수, 설립일, 업종, 업태, 종목, 연락처, 사업자상태, 과세유형 등)를 조회합니다. [호출당 50포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| biz_no | Yes | 사업자등록번호 (숫자 10자리, 하이픈 제외, 예: 4398700761) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses a per-call cost ('[호출당 50포인트]') and enumerates the returned fields (대표자, 주소, 직원수, etc.). This adds operational context not available in annotations.
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 front-loaded with the primary purpose in English, followed by a Korean expansion listing specific fields and the cost. While there is some bilingual redundancy, the additional details justify the additional sentences.
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 one parameter and no output schema, the description adequately conveys the scope of returned data and the cost. It could mention the response format or error handling, but the listed fields give sufficient expectation for an agent.
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 the sole parameter biz_no (10 digits, no hyphen, with an example). The description only repeats '10-digit business registration number' without adding new semantic information, so the baseline score 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 clearly states the tool's function: 'Look up general status information of a Korean business by its 10-digit business registration number.' It specifies the exact resource (business info) and the identifying input, and lists the types of data returned (representative, address, employees, etc.).
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 implies usage: when you need business registration details, use this tool. However, it does not explicitly state when to use it over sibling tools like venture_biz_info or other lookup tools, nor does it provide exclusion criteria.
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.