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by app.apick

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

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description confirms it returns a new PNG rather than modifying the original. It adds useful behavioral context: adjustable parameters (color, size, opacity, density), supported image formats (PNG, JPEG), and a per-call cost of 10 points. This goes beyond annotations without contradicting them.

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

Conciseness5/5

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

The description is concise, front-loaded with the primary action, and includes all essential details (return format, adjustable settings, supported formats, cost) in three sentences. No extraneous information exists.

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

Completeness4/5

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

For an image processing tool with no output schema, the description tells the agent that the result is a PNG, lists supported input formats, and mentions adjustable parameters. It does not cover error handling or unsupported format behavior, but the schema already constrains image_url. Overall, it is sufficiently complete for an agent to invoke the tool correctly.

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 the baseline is 3. The description mentions that font size, color, transparency, and density can be adjusted, which maps to parameters font_size, color, opacity, and density. However, it does not add any syntax or default value details beyond what the schema already provides. It partially reinforces the semantics but adds little new information.

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 clearly states the verb 'Draw' and resource 'image', and specifies that it returns a PNG with a text watermark. It is distinct from sibling draw_watermark_pdf by the word 'image' in tool name and description, but does not explicitly name that sibling. Thus it is clear but lacks explicit sibling differentiation.

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

Usage Guidelines2/5

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

The description does not provide any guidance on when to use this tool versus alternatives like draw_watermark_pdf or set_watermark. It neither states conditions for selection nor exclusions, leaving the agent to infer from the name alone. No usage context is provided.

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
Disambiguation4/5

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 Consistency3/5

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.

Tool Count3/5

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.

Completeness4/5

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.

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