tts_jobs_cancelCancel a waiting or processing TTS job without refunding the accepted charge. waiting 또는 processing 상태의 TTS 작업을 취소합니다. 접수 시 과금된 금액은 환불되지 않습니다. [추가 과금 없음]
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | 취소할 32자리 ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the destructiveHint=true annotation, it discloses that the previously accepted charge is not refunded and no additional charge is applied. These billing behaviors are valuable. It also restricts cancellation to waiting or processing states, though it does not describe failure behavior for jobs outside those states.
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 front-loaded and captures the core action, state restriction, and billing caveat in one sentence. The Korean repetition and bracketed '[추가 과금 없음]' add some redundancy but are not misleading or excessively long.
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 destructive operation with no output schema, it covers the valid target states and the important financial consequences. It omits explicit guidance for jobs that are already completed and the response format, but those are minor given the simple schema and clear scope.
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 description coverage is 100%, so the schema fully documents job_id with type, length, pattern, and description. The tool description does not add meaning beyond identifying the job as the TTS job to cancel, which is already evident from the tool name.
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?
Description uses a specific verb 'Cancel' and resource 'TTS job', and narrows scope to waiting or processing states. It is clearly distinguished from sibling tools such as tts_jobs_create, tts_jobs_status, and tts_jobs_result, which are not cancellation operations.
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?
States the applicable condition explicitly: only waiting or processing TTS jobs can be canceled. It also conveys the cost consequence of no refund. It does not mention alternative tools or what to do when the job is in another state, but no sibling tool competes for this cancellation purpose.
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.