tts_jobs_subtitlesDownload the completed ASS subtitles once as base64. 완료된 TTS의 발화 타이밍 ASS 자막을 base64로 한 번 내려받습니다. 호출이 시작되면 자막 원본이 소모되므로 재실행할 수 없습니다. [추가 과금 없음]
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | completed 상태인 32자리 ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond destructiveHint=true, it specifies exactly what gets consumed ('자막 원본이 소모되므로 재실행할 수 없습니다') and adds that there is no extra charge. It also discloses the return format as base64, which is essential since there is no output schema.
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 each sentence adding useful information. A small redundancy exists between 'once' and the later '재실행할 수 없습니다,' and the English/Korean bilingual phrasing repeats some content.
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 tool with destructive semantics and no output schema, the description covers purpose, return format, consumption behavior, and billing. A minor gap is the lack of explicit guidance on what happens if the job is not yet completed or if the download fails.
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 schema already fully documents job_id with a 32-character hex pattern and the 'completed 상태' requirement. The description only restates 'completed TTS' and adds no new parameter-level detail, so the high schema coverage baseline of 3 applies.
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 names the exact action (download), the resource (completed ASS subtitles), and the format (base64). This clearly distinguishes it from sibling TTS tools like tts_jobs_status or tts_jobs_result, which target different resources.
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 usage context: only for completed TTS jobs and only once, because the source is consumed. It does not explicitly name alternatives or exclusion conditions, 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.