pdf_to_imageConvert each page of a PDF file to PNG images, returned as a ZIP archive. PDF 파일의 각 페이지를 PNG 이미지로 변환하고 ZIP 파일로 묶어 반환합니다. PDF 형식의 파일만 허용됩니다. [호출당 2포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| pdf_url | Yes | 다운로드 가능한 https URL (허용 형식: application/pdf) (최대 25MB) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is clear. The description adds useful behavioral details: it converts each page, returns a ZIP archive, and costs 2 points per call. These go beyond the annotations and help the agent plan accordingly.
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 and front-loaded with the key functionality. The bilingual repetition (English and Korean) adds minor redundancy but is not excessive. The cost note is a useful addition that is clearly separated. Overall, it is efficiently written.
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 one-parameter tool with no output schema, the description is sufficiently complete. It explains the input format, the output format (ZIP of PNGs), and the per-page behavior. Combined with the schema and annotations, an agent has everything needed to invoke the tool correctly.
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 coverage is 100% for the single parameter (pdf_url), which already includes a detailed description ('다운로드 가능한 https URL (허용 형식: application/pdf) (최대 25MB)'). The description only reinforces the PDF-only constraint and the conversion behavior, adding no new parameter-specific semantics, 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 states a specific action: 'Convert each page of a PDF file to PNG images, returned as a ZIP archive.' This clearly identifies the resource (PDF), the verb (convert), and the output format, distinguishing it from sibling tools like pdf_to_docx or pdf_merge.
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 clear context on when to use the tool (when needing PDF pages as PNG images in a ZIP). It also includes a prerequisite/constraint ('PDF 형식의 파일만 허용됩니다'). However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5.
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.