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, so the read-only nature is covered. The description adds meaningful context: returns a masked image in base64 in the masked_image field, expects specific image formats, and requires a valid legal basis. 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 front-loaded with the core purpose, followed by usage details and legal/cost notes. The bilingual repetition (English and Korean) adds length but is acceptable for a Korean-specific tool. No superfluous content.
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?
With no output schema, the description's mention of the masked_image base64 field gives a clue about the response. It covers input format, legal requirements, and cost. It does not enumerate which fields are extracted, but for a single-parameter tool this is adequate.
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%: the image_url property already includes its type, allowed formats, and maximum size. The description repeats the format requirement but does not add new parameter-level meaning. Baseline of 3 applies because the schema handles the semantics.
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?
States a specific verb (extract and mask) and a specific resource (Korean resident registration card). It clearly distinguishes itself from siblings like driver license, passport, and residence card by document type, so an agent can identify the right tool.
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?
Specifies the document type, which implicitly tells the agent to use it only for 주민등록증, and provides explicit input requirements (single PNG/JPEG image) and a legal precondition (consent). However, it does not directly name alternatives or exclusion conditions, so it is clear but not exhaustive.
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.