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 include readOnlyHint=true, and the description aligns with that. It adds useful behavioral context by stating the return format (structured result and raw_text) and the cost per call (12 points). This goes beyond the annotation without contradicting it.
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 compact—four sentences including the legal note and cost. It front-loads the purpose, then adds necessary context. There is no redundant verbiage, though the bilingual repetition (English and Korean) doubles some content without loss of clarity.
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 and no output schema, the description covers the main necessities: what the tool extracts, the return type, the legal precondition, and cost. It does not enumerate all extracted fields exhaustively, but the '등' (etc.) indicates partiality. Overall, an agent can call this tool correctly with the given information.
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%, so the image_url parameter is fully documented in the schema. The description subtly links the parameter to the document type ('Korean driver license image') but adds no new details about the parameter itself. Baseline 3 is appropriate given high schema coverage.
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 the verb 'Extract' and the resource 'key fields from a Korean driver license image', with specific fields listed. It distinguishes from generic OCR by specifying the document type, but does not explicitly differentiate among the closely named siblings (ocr_identi1, 3, 4, 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 an explicit usage condition: '정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오' (use only when legal basis such as consent is secured). This is clear context but does not mention alternatives or when not to use the tool relative to sibling OCR tools.
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.
Each tool has a clearly distinct purpose: the generic `ocr` handles any image, while `ocr_identi1` through `ocr_identi5` are each tied to a specific document type (resident card, driver license, passport, certified copy, alien card). There is no ambiguity about which tool to use for a given input.
All tools follow a consistent `ocr` prefix with snake_case, and the specific ones use `ocr_identi` + numeric suffix. While the pattern is predictable, the numeric suffixes are not descriptive of the document type, which reduces self-documentation slightly.
Six tools is well-scoped for an OCR server. The generic tool handles broad use cases, and the five specific tools cover common identity documents without redundancy or bloat.
The server provides both a general OCR tool and structured extraction for the most common Korean ID documents. There are no obvious dead ends; the domain is fully covered for typical OCR and identity-document needs.