ocrExtract text from an image file (OCR). 이미지 파일에서 텍스트를 추출해 전체 텍스트(full_text)를 반환합니다. 문서 사진, 스캔 이미지, 캡처 화면 등 범용 이미지에 사용합니다. [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, so no contradiction. The description adds value by stating the return format (full_text) and the cost ('호출당 12포인트'), which are behavioral traits not covered by the annotations. It does not describe potential error conditions, but for a simple read-only OCR tool this is reasonably transparent.
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 two short sentences with key information front-loaded ('Extract text from an image file (OCR)'). It includes use cases and cost efficiently. The bilingual repetition (English and Korean) adds some redundancy but is not excessive. Overall, it is concise and well-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?
For a simple tool with one parameter fully described in the schema, annotations for read-only, and an output schema absent but with return value disclosed in the description, the definition is nearly complete. It covers what, when, cost, and return format. Missing details like error handling or image quality requirements are minor given low complexity.
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 provides 100% description coverage for image_url, including allowed MIME types and size limit. The description does not add any additional semantic meaning about the parameter beyond calling it an image file. Baseline 3 is appropriate because the schema already documents the parameter thoroughly.
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 and resource: 'Extract text from an image file (OCR)', and indicates it returns full_text. It also notes use cases (document photos, scanned images, screenshots) and implicitly differentiates from specialized OCR tools like identity_document_* and ocr_identi* by calling itself general-purpose ('범용 이미지').
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 gives clear context on when to use the tool ('문서 사진, 스캔 이미지, 캡처 화면 등 범용 이미지'), but does not explicitly name alternatives or when NOT to use it. However, the sibling list includes specialized OCR tools, and '범용' suggests avoiding those specialized cases. This is adequate guidance without explicit exclusions.
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.