identity_document_driver_licenseExtract key fields from a Korean driver license image and mask the last 6 digits of the RRN. 운전면허증(주민등록번호 표기형) 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 indicate readOnlyHint=true, meaning the tool is non-destructive. The description adds valuable behavioral context: it returns a masked image in base64 within the masked_image field, notes a cost of 30 points per call, and requires legal processing basis. This goes beyond what the annotations provide, though it could be even more explicit about the return format or potential errors.
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 somewhat lengthy due to bilingual repetition (English and Korean) but the core purpose is front-loaded. It includes necessary details like cost and legal requirements, though some redundancy exists. Overall, it stays focused and structured, with no wasted 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?
Given the single parameter and no output schema, the description provides sufficient information to call the tool: it states the input format, the output field (masked_image as base64), the cost, and the legal prerequisite. It does not enumerate the extracted 'key fields' but that is acceptable since the tool's main purpose is masking and the response format is described. The absence of an output schema makes the description's mention of the return field particularly useful.
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 schema covers 100% of the parameter (image_url) with details on URL, format, and size. The description reinforces that only one PNG or JPEG image is needed and that it should be passed directly, which adds clarity. Since schema coverage is high, the baseline is 3, but the description's reinforcement and clarification earn a 4.
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 identifies the tool as extracting fields from a Korean driver's license and masking the last 6 digits of the RRN. It specifies the exact document type (운전면허증), distinguishing it from sibling tools like identity_document_passport or identity_document_id_card. The verb 'extract' and 'mask' are specific and action-oriented.
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 condition: use only when legal basis (consent) is secured, which is a valid usage guideline. It also states the input requirement (single PNG/JPEG image) but does not explicitly mention when to use this tool over alternatives (e.g., for other ID types). The context of Korean driver's license is implicit, but the exclusion of non-driver-license images is not stated.
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.