base64_to_imageDecode a base64-encoded image string back into an image file. base64 로 인코딩된 이미지 문자열을 원본 이미지 파일로 디코딩해 반환합니다. "data:image/타입;base64," 접두어가 붙은 문자열도 허용됩니다. [호출당 2포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| base64 | Yes | 이미지 base64 문자열 (data:image/타입;base64, 접두어 허용) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=false. The description goes beyond by disclosing support for data URI prefixes and mentioning a per-call cost of 2 points, which are not in annotations. This adds useful behavioral context without contradicting the read-only signal.
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, front-loaded with the core action, and includes both English and Korean equivalents. Each sentence adds value: purpose, input specifics, and cost. The bilingual repetition doubles length but is still efficient for the target audience.
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?
The tool is simple: one parameter, no output schema, and clear annotations. The description states the return type ('original image file'), accepted input format, and cost. No further details, such as error handling, are necessary for this low-complexity operation.
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%, and the parameter description already explains the base64 string and allowed data URI prefix. The tool description repeats the same information without adding new semantic details, so the baseline of 3 applies.
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 function: decode a base64-encoded image string back into an image file, using the verb 'decode' and resource 'base64-encoded image string'. It also specifies accepted input variants (data URI prefix). This uniquely identifies the tool among siblings like pdf_to_image or docx_to_pdf.
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: convert a base64 image string to an image file. It does not explicitly mention alternatives or exclusions, but the context is clear enough, and the supported prefix adds practical guidance. No competing sibling has a similar purpose, so ambiguity is low.
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.