Skip to main content
Glama

transcribe_audio

Transcribe audio or video files locally using mlx-whisper on Apple Silicon. Automatically splits long files into 5-minute chunks, returns timestamped segments, and saves an SRT file for DaVinci Resolve import.

Instructions

Transcribe an audio/video file locally using mlx-whisper (Apple Silicon). Long files are automatically split into 5-minute chunks so it never times out.

Returns ALL segments with timestamps inline (compact format) plus saves an SRT file next to the source for Resolve import.

Parameters:

  • file_path: Absolute path to audio/video file (mp3, wav, m4a, mp4, mov, etc.)

  • model: "tiny" (fastest), "base", "small", "medium", "large" (most accurate), "turbo" (best speed/quality, default). Or a full HuggingFace repo path.

  • language: Language code (e.g. "en", "fr", "de", "ja"). None = auto-detect.

  • word_timestamps: Include word-level timestamps in output.

  • initial_prompt: Optional text to guide the model's vocabulary/style.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
modelNoturbo
languageNo
word_timestampsNo
initial_promptNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Given no annotations, the description provides good transparency: it explains local processing, chunking behavior, return format (segments with inline timestamps), and side effect of saving an SRT file. It could be slightly improved by explicitly stating it's a read-only operation on the timeline.

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 concise and well-structured: a brief purpose statement, a notable behavioral note (chunking), a summary of output format, and a bullet list of parameters. Every sentence provides necessary information without redundancy.

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 5 parameters, no annotations, but with an output schema, the description covers core functionality, parameters, and key output details (inline segments and SRT file). It is sufficiently complete, though it could explicitly mention that the output schema provides detailed return structure.

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?

The description compensates for 0% schema coverage by providing clear, detailed explanations for all five parameters: file_path (supported formats), model (options with descriptions), language (auto-detect vs. code), word_timestamps (boolean), and initial_prompt (optional guidance). This adds significant value beyond the schema's basic type and title info.

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

Purpose4/5

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

The description clearly states the tool transcribes audio/video files using mlx-whisper, specifying the action and resource. However, it does not explicitly differentiate from sibling tools like transcribe_and_add_subtitles or create_subtitles_from_audio, which perform related tasks.

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 mentions that long files are split into 5-minute chunks to avoid timeouts, implying usage for large files. However, it lacks explicit guidance on when to use this tool versus alternatives such as transcribe_and_add_subtitles or export_srt.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/barckley75/resolve-claude-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server