ocr_identi5Extract key fields from a Korean alien registration card (residence card) image via OCR. 외국인등록증 사진에서 이름, 외국인등록번호, 발급일자 등 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?
The annotations already declare readOnlyHint=true, so a read-only operation is expected. The description adds that it returns structured results and raw_text, and mentions the sensitivity of personal data and the need for legal basis. However, it does not disclose potential failure modes, accuracy limitations, or handling of unsupported image types beyond the schema's format restriction. The description adds some context but not deep behavioral disclosure.
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 relatively compact, combining an English and Korean explanation. It front-loads the purpose, then adds return details, legal warning, and cost. It is efficient but includes a redundant bilingual restatement of the same intent, which could be trimmed. Still, it earns a good score for being concise and structurally sound.
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?
For a simple tool with one parameter, no output schema, and a read-only annotation, the description covers the essential facts: what it extracts, that it returns raw_text, and the legal obligation. It does not specify the exact structure of the structured result, nor does it explain how it differs from the many similar OCR tools, which would help an agent decide correctly. Overall, it is mostly complete but with minor gaps.
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 input schema has 100% description coverage for the single parameter (image_url), including format and size limits. The description does not add any extra meaning to the parameter beyond what the schema already provides. According to the calibration rule, with high schema coverage the baseline is 3.
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 (extract) and resource (Korean alien registration card image) and lists the key fields (이름, 외국인등록번호, 발급일자). It is not a tautology and distinguishes itself from a generic OCR tool. However, it does not differentiate from the many sibling tools with similar names (ocr_identi1-5, identity_document_residence_card), leaving the agent to guess which one to pick for a specific card variant.
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 only usage condition is a legal one (obtain proper consent/legal basis). There is no guidance on when to use this tool versus alternatives like ocr_identi1-4 or identity_document_residence_card. The description implies it is for alien registration cards, but does not explicitly say 'use this for X, use that for Y'. With many similar OCR siblings, this is a significant gap.
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.