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 declare readOnlyHint=true, and the description does not contradict this. It adds behavioral detail beyond annotations: the output is a JSON response containing a masked_image field with base64-encoded data, and it mentions the 30-point cost per call. This clarifies what the tool returns and its resource consumption.
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 core purpose but contains redundant bilingual text (English and Korean repeating the same points). The legal note and cost info are useful, but the duplication makes it slightly less concise than optimal.
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?
The description explains the input, output structure (masked_image field), and legal requirement, but does not enumerate the 'key fields' extracted from the driver license. This omission could leave an agent uncertain about the tool's full capabilities. With no output schema, the description bears more responsibility for return-value details.
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 already provides 100% coverage for the single parameter (image_url) including format and size constraints. The description redundantly states 'PNG 또는 JPEG 이미지 파일 하나만' (a single PNG or JPEG image), adding little 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 clearly states a specific verb-resource pair: extract key fields from a Korean driver license image and mask the last 6 digits of the RRN, returning a masked image. It names the document type (driver license), distinguishing it from sibling tools like identity_document_id_card or identity_document_passport.
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?
It provides clear usage context: pass a single PNG/JPEG image and only use with a legal basis such as consent. While it doesn't explicitly contrast with sibling ID-document tools, the driver-license specificity inherently guides selection, and the legal prerequisite is a valuable usage boundary.
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.