text_summarySummarize a long text (up to 100,000 characters) into a concise Korean summary. 입력 텍스트(최대 10만 자)의 핵심 내용을 간결하고 정확하게 요약합니다. 모델·파라미터는 서버가 고정하며 빠른 응답에 최적화되어 있습니다. 토큰 수와 무관하게 요청당 고정 포인트가 차감됩니다. [호출당 100포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | 요약할 원문 텍스트 (최대 100,000자) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses that the model and parameters are fixed server-side, the response is optimized for speed, and a fixed point cost is deducted per request regardless of token count. This adds useful behavioral and cost context that is not covered by 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 short (three sentences), but it repeats the same core information in English and Korean, making it slightly redundant. The extra details (cost, speed, server-fixed parameters) are valuable, and the structure is front-loaded with the primary purpose.
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, the description covers the key aspects: input length, output language, server configuration, cost, and speed. Without an output schema, it implicitly describes the return value as a Korean summary, which is sufficient for an agent to understand what to expect.
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 already describes the 'text' parameter with a 100,000 character limit (100% schema coverage). The description does not add further semantic detail about the parameter itself—it only provides tool-level behavior like output language and cost—so the baseline score of 3 is appropriate.
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 a specific action ('Summarize a long text') and the output language ('concise Korean summary'). It also distinguishes from sibling tools like text_polish and llm_chat by focusing on summarization rather than polishing or conversational tasks.
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?
It provides clear context for when to use the tool: summarizing long text (up to 100,000 characters) into Korean, with fast response and fixed cost. However, it does not explicitly name alternative tools or state when not to use it, so it lacks the explicit exclusions typical of a 5.
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.