text_polishPolish a text (up to 100,000 characters) by fixing grammar, spelling, and awkward phrasing. 입력 텍스트(최대 10만 자)의 문법 오류, 맞춤법·오타, 어색한 표현, 문장 순서를 의미를 유지한 채 자연스럽게 다듬습니다. 모델·파라미터는 서버가 고정하며 빠른 응답에 최적화되어 있습니다. 토큰 수와 무관하게 요청당 고정 포인트가 차감됩니다. [호출당 100포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | 다듬을 원문 텍스트 (최대 100,000자) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds meaningful behavioral details: maximum 100,000 characters, server-fixed model/parameters, fast-response optimization, and fixed point deduction per request regardless of token count. It also notes meaning is preserved. No contradictions with 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 English purpose sentence is front-loaded and effective. The following Korean sentence largely repeats the same content but adds useful specifics (sentence order, meaning preservation), and additional sentences cover server configuration and cost. Slight redundancy, but no wasted sentences.
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 single-parameter text transformation tool with read-only annotations and no output schema, the description covers input constraints, operation details, service behavior, and cost. The output (polished text) is implied by the tool's name and purpose, so not explicitly describing it is acceptable.
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 fully describes the only parameter 'text' with the same maximum length. The description restates this limit but adds no new parameter semantics beyond what the schema provides, 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 the tool's purpose: 'Polish a text' with specific actions (fixing grammar, spelling, awkward phrasing) and a max length. This distinguishes it from sibling tools like text_summary and llm_chat by emphasizing grammatical cleanup rather than summarization or conversation.
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 implies when to use this tool: when input text needs grammar/spelling/awkward phrasing fixes while preserving meaning. It adds context about fixed server parameters and cost, but does not explicitly mention alternatives or when not to use it, so it falls short 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.