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by app.apick

개인정보 마스킹(주민등록번호)

hide_rrn
Read-only

Mask resident registration numbers found in an image and return the masked image. 이미지에서 주민등록번호를 인식해 지정한 방식으로 가린 이미지를 반환합니다. 이미지 파일과 type(1: 주민등록번호 전체 가림, 2: 뒷자리 전체 가림, 3: 뒷자리 첫 숫자 제외 가림, 4: 주민등록번호와 주소 가림)을 모두 입력해야 합니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 20포인트]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes가림 처리 타입 (1: 주민등록번호 전체, 2: 뒷자리 전체, 3: 뒷자리 첫 숫자 제외, 4: 주민등록번호+주소)
image_urlYes다운로드 가능한 https URL (허용 형식: image/jpeg, image/png) (최대 50MB)

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, which the description aligns with by describing an image processing operation that returns a result. The description adds a per-call cost (20 points) and a legal obligation, which are behavioral constraints not captured in annotations. It does not contradict annotations and adds useful context about constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is bilingual but each sentence contributes value: purpose, required inputs, type explanation, legal condition, and cost. Though slightly verbose, it is well-structured with the main purpose first and no fluff. The redundancy between English and Korean is minor and aids clarity for different users.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description does not specify the return format (e.g., whether the masked image is returned as a URL, base64, or file). Given there is no output schema, this is a gap. However, the operation is simple and the parameters are fully explained, so an agent can likely call it correctly. Missing return details makes it incomplete for a no-output-schema tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides full coverage (100%) for both type and image_url, including allowed formats and size limit. The description reinforces the meaning of the type values by enumerating them (1-4) in the main text, which adds clarity beyond the schema. While not strictly necessary, it re-states the parameter semantics in a more narrative form.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool masks resident registration numbers in an image and returns the masked image. It specifies four masking types with concrete explanations, distinguishing it from sibling tools like face_blur or draw_watermark. The verb 'mask' plus the specific resource ('resident registration numbers') makes the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a legal prerequisite (must have consent or lawful basis), but does not explicitly state when to use this tool versus alternatives. No mention of when not to use it or how it differs from siblings such as face_blur or draw_watermark. The context is clear enough but lacks explicit exclusion or alternative routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation4/5

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 Consistency3/5

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.

Tool Count3/5

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.

Completeness4/5

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.

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