html_to_pdfRender HTML code into a PDF file. HTML 코드를 렌더링해 PDF 파일로 변환합니다. HTML 문자열을 입력하면 변환된 PDF 파일을 반환합니다. [호출당 30포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | 변환할 HTML 코드 | |
| pagination | No | 페이지 번호 표시 여부 (0: 없음(기본값), 1: 표시) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful context beyond annotations: it specifies the output is a PDF file and notes a per-call cost ('호출당 30포인트'), which is a rate-limit style detail. No contradiction with annotations.
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, but it repeats the same English and Korean content in nearly identical sentences. The cost note is valuable and compact, though the redundancy prevents a top score.
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?
With only 2 params, full schema coverage, and no output schema, the description adequately covers purpose and return type ('변환된 PDF 파일을 반환'). It does not mention pagination, but the schema handles that. Overall complete for a simple conversion tool.
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%, as both html and pagination parameters have descriptions. The tool description does not add meaning beyond the schema beyond restating that HTML is a string, so 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 'Render HTML code into a PDF file', specifying both the verb and the resource. It distinguishes this tool from siblings like docx_to_pdf or pdf_merge by focusing on HTML input, making its purpose unambiguous.
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 for raw HTML strings via 'HTML 문자열을 입력하면' but does not explicitly state when to use this tool versus alternatives like url_screenshot or url_html. No exclusions or alternative tool mentions are provided, so guidance is only implied.
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.