video-transcript

name

video-transcript

description

Transcribe online videos (Bilibili/B站, YouTube, Vimeo, Twitch, and any yt-dlp supported platform) to text so agents can learn from video content. Use whenever the user provides a video link (bilibili.com, b23.tv, youtube.com, youtu.be, vimeo.com, twitch.tv, etc.), asks to transcribe a video, or wants to extract/summarize content from a video. Supports existing subtitles (fast) and AI-generated transcription via local Whisper (slower but always available).

Video Transcription

Transcribes online videos to text using the video-toolkit_* native tools (backed by the video-toolkit MCP server configured in opencode.jsonc). Works with any platform supported by yt-dlp — Bilibili (B站), YouTube, Vimeo, Twitch, and hundreds more.

Workflow

Step 1: Resolve short links

Short links like b23.tv/XXXXX or youtu.be/XXXXX must be resolved first:

curl -sI "https://b23.tv/XXXXX" | grep -i location

Step 2: Try existing subtitles first

Call the native tool video-toolkit_get-transcript:

video-toolkit_get-transcript
Args: url="<video URL>", lang="zh"

• If the video has subtitles → instant result
• If no subtitles → fails, proceed to Step 3

Step 3: Generate subtitles with Whisper

Call the native tool video-toolkit_generate-subtitles:

video-toolkit_generate-subtitles
Args: url="<video URL>", language="zh"

Downloads audio, converts to 16kHz mono, transcribes with local Whisper (small model).

Timing (CPU, ~4 cores):

Video length	Transcription time
5 min	~1 min
15 min	~3 min
30 min	~6.5 min
1 hour	~13 min

Step 4: Use the transcript

Read the returned text and answer the user's question. Timestamps included — cite specific moments if needed.

Step 5: Check available languages

video-toolkit_list-transcript-languages
Args: url="<video URL>"

Supported Platforms

Anything yt-dlp supports:

Bilibili (bilibili.com, b23.tv) · YouTube (youtube.com, youtu.be) · Vimeo · Twitch · Podcast feeds · Direct media URLs · And hundreds more

Configuration

The video-toolkit MCP server is configured in opencode.jsonc and its tools are exposed as native video-toolkit_* tools (no skill_mcp invocation needed):

"video-toolkit": {
  "type": "local",
  "command": ["node", "/path/to/video-toolkit-mcp/dist/index.js"],
  "enabled": true,
  "timeout": 600000,
  "environment": {
    "YT_DLP_PATH": "/home/ubuntu/.local/bin/yt-dlp",
    "FFMPEG_PATH": "/home/ubuntu/.local/bin/ffmpeg",
    "FFPROBE_PATH": "/home/ubuntu/.local/bin/ffprobe",
    "TRANSCRIPT_MCP_WHISPER_ENGINE": "local",
    "WHISPER_BINARY_PATH": "/home/ubuntu/.local/bin/whisper",
    "WHISPER_MODEL_PATH": "small"
  }
}

The local whisper wrapper is bundled at scripts/whisper. Copy to ~/.local/bin/whisper and chmod +x.

Dependencies

• yt-dlp — downloads video/audio
• ffmpeg — audio format conversion
• faster-whisper (pip install faster-whisper) — local transcription
• whisper wrapper script at ~/.local/bin/whisper

Limitations

• Some platforms require authentication for subtitles (e.g. B站). generate-subtitles with local Whisper always works as fallback.
• Transcription speed: CPU-bound, ~4-5x realtime with small model.
• Accuracy: Good for clear speech; noise or heavy accents reduce quality.
• Long videos (>1h) may exceed context windows. Process in chunks.