ocr_identi4Extract key fields from a Korean certified copy of resident registration (deungbon) image via OCR. 주민등록등본 사진에서 주요 정보를 추출해 구조화된 결과와 원문 텍스트(raw_text)를 반환합니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 12포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | 다운로드 가능한 https URL (허용 형식: image/png, image/jpeg) (최대 50MB) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations mark readOnlyHint=true, and the description does not contradict this. It adds useful behavioral context beyond the annotations by specifying that output includes structured results and raw_text, restricting image formats (png/jpeg) and max size (50MB), and noting a per-call cost of 12 points and the legal basis requirement. These details help the agent understand side effects and constraints beyond the read-only flag.
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 efficient, containing the essential purpose in the first sentence, a redundant Korean translation, a legal usage note, and a cost indicator. The key information is front-loaded, and the extra lines (translation and cost) are minor but not verbose. It could drop the Korean repetition for an English-only agent, but it remains clear and structured.
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 there is no output schema, the description should explain what the tool returns. It mentions 'structured results and raw_text' but does not enumerate the key fields, provide error handling, or describe the structured result format. For an agent deciding whether to call this tool, the field list is crucial. This is a notable gap for a specialized OCR tool, so a 3 is appropriate.
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% for the single parameter image_url, which already includes allowed formats and max size. The description repeats these constraints without adding new meaning, such as URL syntax, optional parameters, or edge-case behavior. With full schema coverage, a baseline of 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 explicitly states the tool extracts key fields from a Korean certified copy of resident registration (deungbon) image via OCR, and that it returns structured results and raw_text. This is a specific verb+resource pairing that clearly distinguishes it from generic OCR tools (e.g., 'ocr') and other OCR variants for different documents (e.g., ocr_identi1–5).
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 clear context: it is for deungbon images and includes a legal prerequisite (proper consent) before use. However, it does not explicitly address when to use this tool versus alternatives like other ocr_identi* tools or identity_document_*, though the document type is implicitly distinctive. No exclusions are stated, which fits the 'clear context, no exclusions' level.
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.