word_cloudGenerate a word cloud image (JPEG) from input text, sizing each word by frequency. 입력 텍스트를 구성하는 단어의 중요도(빈도수)에 따라 서로 다른 크기의 단어로 이루어진 워드클라우드 이미지(JPEG)를 생성해 반환합니다. [호출당 10포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | 워드클라우드를 생성할 텍스트 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds valuable details: the output format (JPEG), the frequency-based sizing behavior, and a per-call point cost. It does not contradict the annotations, though it omits return transport details like URL vs base64.
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 English sentence is concise and front-loaded. The Korean repetition adds minor redundancy but includes the cost note. Overall the description is well-sized, though it could be tightened by removing the duplicate.
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 read-only tool, the description is mostly complete. However, it lacks an output schema and does not specify the return format (URL, binary, etc.) or any input limitations/error behaviors. It is minimally viable but not fully transparent.
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 documents the single 'text' parameter with 100% coverage, so the description does not need to add parameter details. It only explains tool behavior, not parameter constraints, aligning with the baseline 3 for high 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 generates a JPEG word cloud image from input text, with word sizing based on frequency. It uses a specific verb-resource pairing and distinguishes itself from sibling media analysis tools (e.g., face_detection, video_to_mp3).
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 explains what the tool does, but does not explicitly state when to use it or mention alternatives. Usage is implied by the tool name and purpose; siblings are clearly different, but no direct 'use this when...' guidance is provided.
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: 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.
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.
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.
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.