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worklab-tools

worklab-tools

by Ryeliu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

transcribe_audio

Convert audio and video files to text with automatic speaker detection. Use for meeting minutes, interviews, or any speech-to-text transcription.

Instructions

Transcribe an audio/video file to text using local Whisper. Automatically detects multiple speakers using pyannote and matches against registered voiceprints. Unknown speakers are labeled as "未知说话人1", "未知说话人2", etc.

Use this when the user provides an audio/video file and wants:
- Speech-to-text transcription
- Meeting minutes / meeting notes
- Interview transcription
- Any audio content converted to text

Args:
    file_path: Absolute path to the audio/video file
    model_size: Whisper model size (default: large-v3 for best Chinese support)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "file_path": "<file_path>"
    },
    "name": "transcribe_audio"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYes
model_sizeNolarge-v3

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses local processing, pyannote-based speaker detection, voiceprint matching, and the exact pattern for unknown speaker labels ('未知说话人1'). This adds meaningful context beyond the bare input schema.

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

Conciseness4/5

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

The description is well-structured: a clear opening sentence, a compact usage list, and a focused Args section. Every part earns its place, though slightly verbose in the use-case enumeration.

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?

With an output schema present, return values need no explanation. The description covers purpose, usage, parameters, and a key behavioral trait (speaker labeling). Could mention file format support, but for a two-parameter tool this is sufficient.

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 coverage is 0%, so the description's Args section fully compensates. It clarifies file_path as an absolute path and explains model_size with default and rationale (large-v3 for best Chinese support), providing meaning entirely missing from the schema.

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 'Transcribe an audio/video file to text using local Whisper' with a specific verb and resource, and then explains automatic speaker detection and voiceprint matching. This clearly distinguishes it from sibling tools like search_knowledge or generate_image.

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?

An explicit 'Use this when...' section lists concrete use cases such as meeting minutes and interview transcription. While it doesn't explicitly name alternative tools or exclusions, the clear context suffices given the sibling set.

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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