URL 화면캡처
url_screenshotCapture a screenshot of a web page and return it as a JPEG image. 입력한 URL의 화면을 캡처해 JPEG 이미지로 반환합니다. [호출당 5포인트]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | 페이지 URL (예: https://www.naver.com/) |
url_screenshotCapture a screenshot of a web page and return it as a JPEG image. 입력한 URL의 화면을 캡처해 JPEG 이미지로 반환합니다. [호출당 5포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | 페이지 URL (예: https://www.naver.com/) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds valuable context: it returns a JPEG image and costs 5 points per call. These are behavioral traits beyond the annotations. No contradictions or missing major behaviors are apparent for a simple screenshot tool.
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 short and front-loaded with the English purpose, then repeats in Korean. The cost note is useful. The bilingual repetition is slightly redundant but acceptable. No unnecessary filler.
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 one-parameter tool with good schema and annotations, the description covers the core action and return type (JPEG). It does not mention edge cases like invalid URLs, timeouts, or screenshot resolution, but these are less critical for a simple tool. Overall, it is fairly complete.
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 schema covers 100% of the parameter (url) with an example and description. The tool description does not add further meaning beyond repeating 'URL'. The schema already handles the semantics sufficiently.
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 action 'Capture a screenshot of a web page' and the output format 'JPEG image'. This specifically distinguishes it from sibling tools like url_html (fetch HTML) or url_similarity.
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 implies usage when a screenshot of a URL is needed, but does not explicitly mention when to prefer this over alternatives (e.g., url_html) or any exclusions or prerequisites. No direct guidance is provided beyond the basic action.
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.