Skip to main content
Glama

generate_subtitles

Generate SRT or WebVTT subtitle files from audio or video with automatic language detection and optional English translation.

Instructions

Generate subtitle files for an audio or video file using whisper.cpp. Set language='auto' to detect the spoken language automatically. Set translate_to_english=true to also generate an English translation subtitle file. Supports SRT and WebVTT (VTT) output formats. When both native and translation are requested, two files are saved: one in the original language and one English translation. Load SRT in VLC via Subtitle → Add Subtitle File. VTT works in web players and HTML5 video. Supports all standard formats plus .3gp and .ts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesAbsolute Windows path to the file.
languageNoLanguage code (e.g. ja, es, fr, de) or 'auto' to detect automatically. Defaults to en.en
output_formatNosrt = SubRip subtitle (default, widest compatibility), vtt = WebVTT (web and HTML5 video).srt
translate_to_englishNoAlso generate an English translation subtitle file alongside the native language file. Only applies when language is not 'en'. Not available in background mode.
backgroundNoRun as a detached background job — recommended for files over 10 minutes. Returns a job ID to use with check_progress. translate_to_english is not available in background mode.
threadsNoCPU threads. Defaults to 2 of 2.
temperatureNoSampling temperature 0.0–1.0. Default 0.0.
promptNoPrior context string for domain-specific vocabulary or speaker names.
beam_sizeNoBeam search width. Higher = more accurate, slower. Default 5.
best_ofNoCandidate sequences evaluated. Default 5.
diarizeNoStereo speaker diarization. Requires stereo audio.
vad_modelNoPath to Silero VAD model .bin. Strips silence before transcription.
gpu_deviceNoGPU/Vulkan device index for multi-GPU systems. Overrides the WHISPER_GPU_DEVICE env default. Check whisper-cli's startup log for the index that lists your target card.
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses behavioral traits: uses whisper.cpp, supports SRT/VTT, saves two files for translation, background mode returns job ID, limitations of translate_to_english in background mode. Could mention file naming conventions.

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?

Description is front-loaded with purpose and key features, but contains several sentences of usage tips that could be streamlined. Still, every sentence adds value and the structure is logical.

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?

Given 13 parameters and no output schema, the description covers main use cases, output formats, background mode, and compatibility. Missing details on return values (e.g., saved file paths or job ID structure) but sufficient for typical usage.

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

Parameters3/5

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

Schema coverage is 100% with detailed parameter descriptions. Description adds minor usage tips (e.g., loading SRT in VLC) but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.

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 clearly states 'Generate subtitle files for an audio or video file using whisper.cpp', with specific verb and resource. It distinguishes from siblings like transcribe_audio and analyze_media by focusing on subtitle generation rather than transcription or media analysis.

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?

Provides explicit guidance on when to use (generating subtitles) with features like auto language detection, translation, and background mode. Lacks direct comparison with alternatives but context signals provide sibling names; slight omission of when not to use.

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/eviscerations/whisper-windows-mcp'

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