tts_jobs_createCreate an asynchronous Korean narration job with one of 17 voices. 17개 한국어 내레이션 목소리 중 하나로 비동기 TTS 작업을 접수합니다. text는 최대 800자이며 접수 성공 시 과금되고 이후 취소해도 환불되지 않습니다. [100자당 10포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | 합성할 한국어 텍스트 (최대 800자) | |
| voice_id | Yes | 지원 voice_id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses key behavioral consequences: successful submission triggers billing, cancellation does not refund the cost, and the job is asynchronous. The '100자당 10포인트' pricing and non-refundable nature are important additional context for an agent deciding to invoke a money-spending tool.
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 and front-loaded with the main action, but it repeats the same core statement in English and Korean. The Korean sentence largely duplicates the English sentence, so not every sentence earns its place.
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 simply invoking the tool, the description is sufficient: required parameters are covered, the 800-character limit is stated, and the billing behavior is disclosed. However, there is no output schema and no mention of what the create call returns or how to later retrieve the job result, leaving an agent without guidance for the full job lifecycle.
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?
Schema coverage is already 100%, so the baseline is 3. The description adds meaning by clarifying that the text must be Korean, reinforcing the 800-character limit, and exposing the pricing implication of longer text. The voice_id parameter is well-specified by the enum, though the description could have added detail about the naming convention.
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 a specific verb and resource: 'Create an asynchronous Korean narration job with one of 17 voices.' It clearly identifies the action as creating a job, which distinguishes it from the sibling tools tts_jobs_cancel, tts_jobs_status, and tts_jobs_result.
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 establishes clear context for the creation step of a TTS job workflow, but it gives no explicit when-to-use or when-not-to-use guidance versus its siblings. It does not mention that users should later call tts_jobs_status or tts_jobs_result to retrieve the asynchronous output.
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.