HTML PDF 변환
html_to_pdfRender HTML code into a PDF file. HTML 코드를 렌더링해 PDF 파일로 변환합니다. HTML 문자열을 입력하면 변환된 PDF 파일을 반환합니다. [호출당 30포인트]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | 변환할 HTML 코드 | |
| pagination | No | 페이지 번호 표시 여부 (0: 없음(기본값), 1: 표시) |
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?
The description adds valuable context beyond annotations: it mentions the cost ('30 points per call') and explicitly states the input (HTML string) and output (PDF file). Annotations already declare readOnlyHint, so the description adds further detail about the operation's behavior without conflicting.
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 brief and front-loaded with the core action. It avoids redundancy except for the intentional bilingual repetition, and includes the cost in a compact manner.
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 two-parameter tool, the description plus schema and annotations cover the essential information: input, output, cost, and side-effect hints. It does not detail the return format (e.g., base64 vs. file path), but this is a minor gap given the tool's simplicity.
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 provides 100% coverage for both parameters with clear descriptions. The description only reiterates the html input ('HTML 문자열') and does not add meaning for the pagination parameter, so it stays at the baseline.
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 tool's function with a specific verb and resource: 'Render HTML code into a PDF file.' This distinguishes it from siblings like pdf_merge and pdf_to_image, and the bilingual text reinforces the exact purpose.
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?
Usage is implied by the clear name and description, but there is no explicit guidance on when to use this tool versus alternatives, nor any mention of exclusions or prerequisites. It simply states what it does without context.
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.
Each tool has a clearly distinct purpose, including the watermark pair: draw_watermark_image is visible text while set_watermark embeds an invisible code. The TTS job lifecycle tools are also cleanly separated by action and output type.
The set mixes conventions: conversion tools use input_to_output, watermark tools use verb_noun, TTS jobs use a tts_jobs_ prefix, and stt is a bare acronym. The names are readable but do not follow one predictable pattern.
At 19 tools, the server sits in the borderline 16-25 range and spans document conversion, image processing, watermarking, audio/video, and async TTS. Most tools earn their place, but the overall surface feels somewhat heavy for a single conversion-focused server.
The server covers its core domains well: document conversions, watermarking with both visible and invisible methods, PDF operations, and a full async TTS workflow. Minor gaps exist, such as missing image-to-PDF or Excel-to-JSON inverse conversions, but agents can generally complete workflows without dead ends.