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 set readOnlyHint=true and openWorldHint=false, and the description adds valuable details: the output is a ZIP archive, each page becomes a PNG, and there's a per-call cost of 2 points. It doesn't contradict annotations and provides useful operational context beyond the structured fields.
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 content is short but includes both English and Korean versions of the same sentence, which is redundant. The cost note is useful, but the duplication wastes space and doesn't help an AI agent.
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 converter, the description covers input, output, and a key restriction. It doesn't mention error handling, page count limits, or potential timeouts, but the simplicity of the tool and the schema's parameter detail make this adequate.
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 description covers 100% of the single parameter (pdf_url) with format, allowed type, and size limit. The description adds no new parameter-level details beyond reinforcing the PDF-only restriction, so it doesn't exceed the baseline for full schema coverage.
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 converts each page of a PDF into PNG images and returns them as a ZIP archive. It uses a specific verb ('Convert'), identifies the resource (PDF pages), and specifies the output format, distinguishing it from sibling conversion tools.
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 gives clear context: it's for converting PDF pages to PNG images and notes that only PDF files are allowed. It doesn't explicitly mention when not to use it or point to alternatives, but the purpose is unambiguous enough for an agent to select it appropriately.
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.