identity_document_id_cardExtract key fields from a Korean resident registration card image and mask the last 6 digits of the RRN. 주민등록증 이미지에서 지정 정보를 추출하고 주민등록번호 뒷자리 6자리를 마스킹한 이미지를 함께 반환합니다. PNG 또는 JPEG 이미지 파일 하나만 전달하면 되며, 마스킹된 이미지는 JSON 응답의 masked_image 필드에 base64로 포함됩니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 30포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | 다운로드 가능한 https URL (허용 형식: image/jpeg, image/png) (최대 50MB) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description aligns by describing extraction and masking without side effects. It adds valuable contextual details beyond annotations: output is a base64 image in masked_image field, input must be PNG/JPEG, a cost per call (30 points), and a legal compliance warning. This enriches the agent's understanding without contradiction.
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 efficiently structured, front-loaded with the core action and then giving input/output format and legal/cost notes. It is bilingual, which adds length but serves multilingual agents, and no sentence is superfluous. Minor redundancy between English and Korean prevents a 5.
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 tool with one simple parameter and no output schema, the description covers the return format (base64 in masked_image), input constraints, legal usage, and cost. It does not cover error cases or multiple-image handling, but those are not essential for basic operation. Adequate for the tool's complexity.
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 parameter is fully documented in the schema (https URL, image/jpeg and image/png, max 50MB). The description redundantly mentions PNG/JPEG and 'one image' but does not add new semantic meaning beyond the schema. Baseline 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 states a specific verb ('extract') and resource ('Korean resident registration card') plus the masking action, and clarifies it returns both extracted fields and a masked image. The name and content clearly distinguish it from sibling tools for driver licenses, passports, and residence cards, so an agent can select it without ambiguity.
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 gives clear context: it is for ID cards, accepts one PNG/JPEG image, and requires lawful processing basis. It does not explicitly mention when not to use it or name alternatives, but the sibling names and descriptive resource make the applicable scenario evident. No exclusions are stated, which slightly lowers the score from 5.
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.
Each tool has a distinct purpose: masking (hide_rrn), text-based verification (identi_cardN), image-based verification (identi_card_imageN), extraction with masking (identity_document_*), and name/RRN matching (name_rrn_auth). Within each group, the document type is clearly specified, so there is no ambiguity between tools.
Naming follows clear patterns: identi_card* for verification (with _image for image-based), identity_document_* for extraction, and descriptive names like hide_rrn and name_rrn_auth. While the prefixes differ across functional groups, each group is internally consistent and the names are readable and predictable.
16 tools is on the higher end but appropriate for the server's scope, covering five document types with two verification modes (text and image), four extraction/masking tools, a generic masking tool, and a name/RRN auth check. Each tool fills a needed role, though the number is slightly elevated due to the text/image split.
The server covers the core operations for identity document verification and extraction across all major Korean ID types. A minor gap is the lack of an extraction/masking tool for the resident registration certificate (identi_card4), but this is not a critical omission given the existing verification and masking capabilities.