tts_jobs_resultDownload the completed MP3 result once as base64. 완료된 TTS MP3 결과를 base64로 한 번 내려받습니다. 호출이 시작되면 서버 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?
Annotations already mark the tool as destructive and non-idempotent, and the description strengthens this with concrete behavioral detail: the server original is consumed at call start and cannot be re-run. It also adds billing transparency with '[추가 과금 없음]', which is valuable beyond the schema or 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 description is short and front-loaded with the core action. The main inefficiency is that the English and Korean first sentences repeat the same information, but the one-time consumption warning and billing note are valuable additions. Overall it is appropriately compact.
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 simple single-parameter tool with no output schema, the description provides the essential information: what is downloaded, the base64 format, the completed-state requirement, the one-time nature, and billing clarity. Nothing critical is missing for an agent to invoke this tool correctly.
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 provides complete coverage for the single job_id parameter, including type, pattern, length, and a description that it must be a 'completed 상태인 32자리 ID.' The description adds no additional parameter-level semantics beyond reiterating that the result is completed, so the schema carries the burden.
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 and resource: 'Download the completed MP3 result once as base64.' It clearly distinguishes this tool from TTS siblings like tts_jobs_create, tts_jobs_status, and tts_jobs_cancel by focusing on result retrieval. The purpose is immediately understandable.
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 clearly implies use after a TTS job reaches 'completed' status and explicitly warns that the result can only be downloaded once. It does not enumerate alternative tools or explicitly say when not to use it, but for a one-shot result downloader the usage context is sufficiently clear.
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.