ocr_identi2Extract key fields from a Korean driver license image via OCR. 운전면허증 사진에서 이름, 면허번호, 생년월일 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?
Annotations already declare readOnlyHint=true, indicating a non-mutating operation. The description adds the requirement for legal grounds (consent) and discloses the cost per call, which are useful behavioral and compliance details. However, it does not describe what happens on failure (e.g., invalid image, non-Korean license) or how the image is handled, leaving some gaps. The added consent and cost info go beyond annotations, warranting a 3.
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 concise, using two sentences (one English, one Korean) plus a brief legal notice and cost. The primary purpose is front-loaded, followed by the legal requirement and cost. No filler or redundancy is present, and it efficiently covers the essential aspects without being verbose.
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 single-parameter tool with no output schema, the description is fairly complete. It specifies the input (Korean driver license image), the extracted fields (name, license number, DOB), the return type (structured results + raw_text), and the legal/cost constraints. While it omits edge-case error handling and examples, the core information needed to call the tool correctly is provided.
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 description coverage is 100%, with image_url fully documented (https URL, allowed formats, max size). The description adds the crucial expectation that the image should be a Korean driver license, which is not explicit in the schema. This adds semantic value, but since the schema already documents the parameter well, the 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 clearly states a specific purpose: extracting key fields from a Korean driver license image via OCR. It names the resource (Korean driver license) and the action (extract/return structured results and raw text). It does not explicitly differentiate from its many sibling OCR tools (ocr_identi1, ocr_identi3, identity_document_driver_license, etc.), but the specificity to Korean driver licenses is enough to distinguish it from generic OCR.
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 a legal precondition (obtaining consent or legitimate processing grounds) and a cost note (12 points per call), but does not offer any guidance on when to use this tool versus its many siblings. It does not mention alternatives or exclusion criteria, leaving the agent to infer that this is the correct tool for Korean driver licenses from the purpose statement alone.
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.