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동영상 미리보기 이미지 추출

extract_video_thumbnail
Read-only

Extract preview thumbnail images from a video at regular intervals and return them as a ZIP file. 동영상에서 일정 구간마다 미리보기 이미지를 추출해 ZIP 파일로 반환합니다. [호출당 10포인트]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo추출할 이미지 개수 (범위: 0 ~ 200, 기본값 100)
widthNo이미지 가로 길이 (범위: 100 ~ 2000, 기본값 480). 세로 길이는 가로 비율에 따라 자동 계산
video_urlYes다운로드 가능한 https URL (허용 형식: video/mp4, video/quicktime, video/x-msvideo, video/webm, video/x-matroska) (최대 200MB)

TDQS

A4/5.0
Behavior4/5

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

The readOnlyHint annotation already indicates a safe read operation, and the description adds valuable context: it returns a ZIP file, extracts at regular intervals, and costs 10 points per call. This goes beyond the annotation and helps the agent understand side effects and cost. No contradiction detected.

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, with a single English sentence and a Korean duplicate alongside the cost note. It front-loads the core action and output format with no unnecessary filler. The bilingual repetition is acceptable and the cost disclosure is important, so every part earns its place.

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?

Given the tool's moderate complexity, the description covers the essential purpose, output format, and cost. The schema fully documents parameters and limits (e.g., max 200MB). It does not explain processing time or failure behavior, but for a read-only extraction tool with strong schema coverage, this is reasonably complete.

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?

The schema descriptions cover all parameters 100%, so the baseline is 3. The description does not add further semantic detail about the parameters beyond what the schema already provides. It mentions 'regular intervals' but does not clarify count or width behavior beyond their schema descriptions.

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

Purpose5/5

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

The description clearly states a specific action ('Extract preview thumbnail images') on a specific resource ('from a video'), and specifies the output format ('return them as a ZIP file'). This distinguishes it from sibling tools like face_detection or video_to_mp3, which serve different purposes.

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 when to use it (when you need preview images from a video) but does not explicitly mention alternatives or exclusion criteria. It would benefit from stating that it is not for audio extraction or detection tasks. Usage is implied rather than explicitly guided.

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: video thumbnail extraction, face detection, image similarity, NSFW detection, video-to-audio conversion, and word cloud generation. There is no overlap between any two tools, so agents can easily select the correct one.

Naming Consistency3/5

All tool names use snake_case, but the pattern is mixed: most are noun-based (face_detection, image_similarity, nsfw_detection, word_cloud) while extract_video_thumbnail starts with a verb and video_to_mp3 is a conversion style. This inconsistency makes the naming slightly less predictable.

Tool Count5/5

With 6 tools, the count is well within the typical 3-15 range and feels well-scoped for a media processing toolkit. Each tool adds a distinct capability without being redundant or overwhelming.

Completeness3/5

The server covers a variety of media operations (video, image, audio, text) but lacks common vision tasks like OCR, object detection, or image classification that one might expect from a 'Vision' server. The set feels like a collection of standalone utilities rather than a complete domain coverage, though no critical gaps cause agents to fail.