name_rrn_authVerify that a Korean name and resident registration number (RRN) match a real registered person. 성명과 주민등록번호의 일치 여부(실명 존재 여부)를 확인합니다. name, rrn1(앞 6자리), rrn2(뒤 7자리)를 모두 입력해야 합니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 50포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | 한글 성명 | |
| rrn1 | Yes | 주민등록번호 앞 6자리 숫자 | |
| rrn2 | Yes | 주민등록번호 뒤 7자리 숫자 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, and the description adds useful details: required fields, the existence check, the legal prerequisite, and the cost of 50 points per call. This goes beyond the annotations 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: three sentences that cover purpose, usage warning, and cost with no filler. It earns its place by being bilingual and efficient.
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 3-parameter verification tool with a readOnly annotation, the description covers purpose, required inputs, legal prerequisite, and cost. No output schema exists, but the verification result is implied, and the information is sufficient for an agent to decide when to use it.
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 descriptions for name, rrn1, and rrn2. The description repeats the format (앞 6자리/뒤 7자리) but adds no new semantic meaning beyond the schema, so the baseline of 3 is appropriate.
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: verifying that a Korean name and RRN match a real registered person. Both English and Korean sentences reinforce this specific purpose. It does not explicitly differentiate from the sibling 'account_realname', so it misses the top score.
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 a clear usage condition: only use when a legal basis such as data subject consent is secured. This gives clear context but does not mention alternatives or explicit when-not-to-use scenarios relative to sibling 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.