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Image 워터마크 삽입

draw_watermark_image
Read-only

Draw a visible text watermark across an image. 이미지 파일에 텍스트 워터마크를 삽입한 PNG 이미지를 반환합니다. 글자 크기·색상·투명도·밀집도를 조절할 수 있으며, PNG, JPEG 등 일반 이미지 포맷을 지원합니다. [호출당 10포인트]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wmYes워터마크 문자열 (최대 30자)
colorNo워터마크 색상 HEX (000000 ~ FFFFFF, 기본값 EEEEEE)
densityNo워터마크 글자 밀집도 (5 ~ 15, 기본값 10)
opacityNo워터마크 투명도 (0 ~ 1, 기본값 0.5)
font_sizeNo워터마크 글자 크기 (10 ~ 100, 기본값 10)
image_urlYes다운로드 가능한 https URL (허용 형식: image/png, image/jpeg, image/webp, image/bmp) (최대 50MB)

TDQS

B3.4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, meaning the tool does not modify the input. The description adds that it returns a PNG image and mentions a per-call point cost, which are useful. However, it does not explain that the original image remains untouched (though implied by readOnly), nor does it detail potential failures or the exact watermark rendering behavior (e.g., tiling or placement). It adds some context but not rich behavioral detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with the core purpose front-loaded in the first sentence. The Korean text repeats some information but adds the supported formats and cost. While bilingual redundancy is slightly inefficient, the overall length is appropriate and no unnecessary fluff is present.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 6 parameters and no output schema, the description is moderately complete. It states the return type (PNG) and supported input formats, but leaves operational details unclear—such as how density maps to rendering, whether the watermark is tiled across the image, and any behavior on invalid inputs. The absence of an output schema makes the simple return statement acceptable, but more behavioral context would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter already has a description. The tool description adds only that these properties are adjustable, which repeats the schema. It does clarify that the input image_url must be downloadable and returns a PNG, but these are already implied by the schema descriptions. It provides no extra meaning beyond the schema, so it stays at the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Draw a visible text watermark across an image' which clearly specifies the action and target resource. It also mentions adjustable properties (font size, color, transparency, density) and supported image formats, giving a solid sense of scope. It does not explicitly name a sibling alternative, but the mention of 'image' distinguishes it from the similar draw_watermark_pdf tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for watermarking image files by stating it works on PNG, JPEG, etc., and returns a PNG. However, it offers no explicit comparison to sibling tools like draw_watermark_pdf or set_watermark, nor does it state conditions for choosing this tool over them. The guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation5/5

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.

Naming Consistency3/5

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.

Tool Count3/5

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.

Completeness4/5

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.