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asr_transcribe

Convert audio files into subtitle formats like SRT, LRC, or plain text using speech recognition.

Instructions

必剪 ASR 语音转字幕(无需登录)。audio_path 支持 flac/aac/m4a/mp3/wav; fmt: srt / lrc / txt / json / segments。长音频上传+识别可能需要 1-3 分钟。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fmtNosrt
audio_pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that no login is required, mentions the latency for long audio ('可能需要 1-3 分钟'), and lists supported formats. It does not cover potential errors, side effects, or asynchronous behavior, but it provides meaningful operational context beyond the schema.

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 two sentences: the first states purpose and a key condition, the second provides parameter details and latency. It is front-loaded with the primary function, uses no filler, and every sentence adds value. It is concise yet comprehensive.

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 tool with two parameters and no output schema, the description covers the essential aspects: input formats, output formats, no-login requirement, and expected latency. It does not detail the structure of returned data for json/segments formats, but that may be acceptable given the fmt parameter. Overall, it is nearly complete for a straightforward transcription tool.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It does so thoroughly: it specifies valid audio_path formats (flac/aac/m4a/mp3/wav) and the exact fmt values (srt/lrc/txt/json/segments), which the schema lacks. This is essential for correct invocation.

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 the exact function ('必剪 ASR 语音转字幕') with a specific verb and resource, and explicitly notes '无需登录' (no login required). It also lists supported input and output formats, making it clear this is a transcription tool, distinct from siblings like get_video_subtitles (fetching existing subtitles) or download_audio (downloading audio).

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

Usage Guidelines3/5

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

The description implies usage (it is for transcribing audio to subtitles) and mentions a condition ('无需登录'), but it does not explicitly compare against alternatives or state when not to use it. No exclusions or references to sibling tools are provided, so the agent must infer the appropriate context.

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