tts_jobs_statusGet the public status and result availability of a TTS job. TTS 작업의 waiting, processing, completed, cancelled, failed 상태와 MP3·ASS 자막 준비 여부를 조회합니다. [무료]
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | 작업 접수에서 받은 32자리 ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true, so the description only needs to add context; it adds 'public', '[무료]' (free), the exact status set, and whether MP3/ASS output is ready. It does not contradict the annotations and gives a reasonable picture of side-effect-free behavior.
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 compact and front-loaded with the core action, and the Korean sentence adds specific statuses and asset-readiness details rather than repeating the English. The bilingual redundancy is minor and does not hurt readability.
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 one-parameter, read-only status tool, the description covers invocation and what to expect back at a high level (statuses and MP3/ASS readiness). With no output schema, it could add a little more on the exact response shape, but nothing essential for selecting and calling the tool 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?
Schema coverage is 100%: job_id is fully documented with format, length, pattern, and origin ('작업 접수에서 받은 32자리 ID'). The description itself adds no extra parameter semantics beyond tying the job to a TTS job, so the baseline 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 uses a specific verb ('Get') and resource ('public status and result availability of a TTS job'), and enumerates the concrete statuses and MP3/ASS readiness. This positions it clearly against sibling tools like tts_jobs_create, tts_jobs_cancel, tts_jobs_result, and tts_jobs_subtitles.
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: checking status and result availability fits naturally after tts_jobs_create and before tts_jobs_result/tts_jobs_subtitles. However, the description never names an alternative or states when not to use it, so an agent must infer the workflow from sibling names.
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.