docx_to_pdfConvert a DOCX (Word) file to a PDF file. DOCX 파일을 PDF 파일로 변환해 반환합니다. DOCX 형식의 파일만 허용됩니다. [호출당 80포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| docx_url | Yes | 다운로드 가능한 https URL (허용 형식: application/vnd.openxmlformats-officedocument.wordprocessingml.document) (최대 25MB) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true, so safety is covered. The description adds the fact that the tool 'converts and returns' the PDF, and specifies a per-call point cost, which is useful behavioral context regarding return and pricing. 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?
Description is short and front-loaded with the main action. However, it repeats the same information in English and Korean ('DOCX파일을 PDF파일로 변환'), causing slight redundancy, and the allowed-format line echoes the schema.
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?
Tool is simple with one parameter and good schema coverage, but there is no output schema and the description does not specify the structure of the returned PDF (e.g., file URL, base64, etc.). It only says 'converts and returns,' leaving a gap for agents unsure about output format. Cost and format constraints are covered, but return semantics are incomplete.
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 single parameter docx_url is fully documented in the schema with URL type, allowed MIME, and max size (100% coverage). The description only restates that DOCX files are allowed, adding no new semantic detail beyond the schema.
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?
Description clearly specifies the conversion action: 'Convert a DOCX (Word) file to a PDF file.' This distinguishes it from sibling tools like pdf_to_docx and pdf_to_image by naming the exact source and target formats.
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?
Gives clear context for when to use the tool (any DOCX to PDF conversion). States a constraint: 'Only DOCX format files are allowed,' which implies not to use for other formats. However, it does not explicitly name alternative tools for other use cases.
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.