identi_card1Verify the authenticity of a Korean resident registration card (jumin-deungnokjeung) using text input. 주민등록증의 기재 정보를 입력해 진위 여부를 확인합니다. name, rrn1, rrn2, date 네 항목을 모두 입력해야 하며, date는 숫자만 허용됩니다(예: 20230101). 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 40포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | 발급일자 (숫자만, 예: 20230101) | |
| name | Yes | 성명 | |
| rrn1 | Yes | 주민등록번호 앞 6자리 | |
| rrn2 | Yes | 주민등록번호 뒤 7자리 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, but the description adds meaningful behavioral context: legal basis requirement, cost (40 points per call), and field requirements. This goes beyond what annotations convey 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 compact and front-loaded with the main purpose. Some redundancy exists due to bilingual repetition and restating schema-required fields, but it remains efficient 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?
Includes important legal and cost context, but with no output schema, it does not describe the return value or result format. Given the tool's simple verification purpose, this is a moderate gap.
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%, with each parameter already described (name, rrn1, rrn2, date). The description repeats the all-required and date-format constraints but adds minimal new semantic value beyond the schema.
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 ("Verify the authenticity of a Korean resident registration card") and specifies the input mode ("using text input"), distinguishing it from image-based sibling tools. Both English and Korean reinforce the same 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?
Provides explicit preconditions: all four fields must be entered, date must be numeric, and it requires legal processing grounds (consent). However, it does not explicitly contrast with alternatives like identi_card_image1 or identi_card2, though the 'text input' phrase implies a text-only use case.
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.