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 provide readOnlyHint and openWorldHint. The description adds valuable behavioral detail: the output format (JPEG), the frequency-based sizing of words, and a cost indicator ('10 points per call'). It does not mention limitations like input length, but the added context is useful and does not contradict annotations.
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 extremely concise, with the core meaning in the first English sentence and a Korean duplicate for bilingual support. The cost note is brief and relevant. There is no filler, and the key information is front-loaded.
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 tool with no output schema, the description covers the input, the output format (JPEG), the generation logic, and cost. It is complete enough for an agent to select and invoke the tool correctly. No critical information is missing.
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 input schema fully documents the single 'text' parameter with description '워드클라우드를 생성할 텍스트' (text to generate a word cloud). The tool description echoes 'from input text' and '입력 텍스트', adding no new semantic detail beyond the schema. With 100% schema coverage, this meets the baseline.
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 states the specific action: 'Generate a word cloud image (JPEG) from input text, sizing each word by frequency.' It clearly identifies the resource (text) and the output (word cloud image), distinguishing it from sibling tools like image generation or text analysis.
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?
Usage is implied by the description: one would use this tool to create a word cloud from text. However, there is no explicit guidance on when to use it vs. alternatives, when not to use it, or any prerequisites. The description only states what the tool does, not when to select it.
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.
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 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.
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.
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.