sttConvert a speech audio file to text (STT). 음성 파일을 텍스트로 변환합니다. MP3, WAV, M4A, AAC, OGG, FLAC, WEBM 등 일반적인 오디오 포맷을 지원하며, 변환된 텍스트를 JSON으로 반환합니다. [호출당 50포인트]
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | 추출 언어 코드 (예: ko, en, ja). 기본값 ko | |
| audio_url | Yes | 다운로드 가능한 https URL (허용 형식: audio/mpeg, audio/mp3, audio/wav, audio/x-wav, audio/mp4, audio/aac, audio/ogg, audio/flac, audio/webm) (최대 200MB) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds useful behavioral context: supported audio formats, JSON return format, and a per-call cost (50 points). It does not describe error conditions or limitations beyond those already in the schema, but the additional details exceed what annotations provide.
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 two sentences, front-loaded with the primary action, and includes all key information: purpose, supported formats, return type, and cost. There is no wasted text or unnecessary detail.
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?
Given the tool's simplicity (2 params, no output schema), the description is nearly complete: it states the input type, output format, and cost. It lacks error handling notes, but with annotations and schema coverage, the remaining gaps are minor.
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 already fully documents both parameters. The description mentions supported formats and JSON output, which are partially redundant with the schema, but it does not add meaningful new meaning to the parameters themselves (e.g., language default behavior).
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 clearly states the action ('Convert') and the resource ('a speech audio file to text'), explicitly identifying it as STT. It also lists supported audio formats and notes the JSON output, distinguishing it from sibling tools like TTS or voice_change.
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 provides clear context for when to use the tool (speech-to-text conversion), but does not explicitly mention alternatives or when not to use it. The sibling context includes TTS and voice_change, but the description itself lacks direct comparison or exclusion, 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.