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draiagent

funasr-zh-tw-mcp

by draiagent

音檔轉繁體逐字稿

transcribe_audio

Convert local audio files to Traditional Chinese transcripts with on-device processing, supporting wav, mp3, m4a, flac, ogg, and mp4. Privacy ensured: files never leave your machine.

Instructions

將本機音檔轉成繁體中文逐字稿,支援 wav/mp3/m4a/flac/ogg/mp4 等格式。全程在本機運算,音檔不會上傳到任何雲端服務。注意:模型第一次載入約需 40 秒,若使用者在意等待時間可先呼叫 preload_model。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
traditionalNo
with_timestampsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes逐字稿(預設為繁體中文、台灣用詞)
raw_textYes模型原始輸出,未經繁體轉換的簡體中文
audio_pathYes辨識的音檔路徑
timestampsNo每個字的 [起始毫秒, 結束毫秒],僅在 with_timestamps=true 時回傳
duration_secondsNo音檔長度(秒)
Install Server

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral disclosure burden. It does well by revealing that processing is fully local, audio is not uploaded to any cloud service, and the first model load takes about 40 seconds with a mitigation path. It does not describe output details, but an output schema exists to cover that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three short sentences with no filler: core purpose, privacy behavior, and latency mitigation are each given exactly one sentence. It is front-loaded and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a relatively simple tool with one required parameter and an output schema, the description covers purpose, supported formats, local processing, privacy, and the relevant sibling relationship. The main gap is parameter-level guidance for with_timestamps, but the boolean name and schema default provide enough signal for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description needed to compensate for parameters like file_path, traditional, and with_timestamps. It only implicitly hints at traditional via 繁體中文 and at file_path via 本機音檔, while with_timestamps is never explained. An agent would have to rely on parameter names and defaults alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb and resource: converting local audio files into Traditional Chinese transcripts. It also lists supported formats and implicitly differentiates itself from the sibling preload_model by describing the actual transcription task.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly establishes when to use the tool: whenever a local audio file needs a Traditional Chinese transcript. It also gives an explicit conditional alternative: call preload_model first if the user cares about the ~40s model loading delay. No when-not-to-use scenario is stated, but there are no competing transcription siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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