Video Context

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Let your AI assistant summarize videos from YouTube, Bilibili, and Xiaohongshu.

Send a video link to get a summary, take notes, or ask questions about the video. Xiaohongshu support is experimental.

Video Context connects your assistant to video subtitles and local speech transcription through MCP and an Agent Skill. Your assistant writes the summary; Video Context provides the video content with timestamps.

Get started · Usage guide · Downloads

See an example

An illustrated walkthrough based on a real YouTube subtitle extraction, not an app recording. The summary was prepared by an AI assistant for this example. Source and timestamp links.

Related MCP server: Open CLAW Knowledge Distiller

What you can do

• Summarize a video: ask for the main points and jump back to the relevant timestamps.

• Ask follow-up questions: find an explanation or turn a tutorial into study notes.

• Save the transcript: export Markdown or JSON and reuse cached results offline.

After setup, try sending your assistant a video link with:

Summarize this video in three points and include timestamps.

Supported platforms

Video Context uses subtitles and audio. It does not read diagrams, code, or actions that appear only on screen.

Get started

Version 0.1.0a3 is a development preview, primarily tested on Windows. Initial setup requires an internet connection and a few dependencies.

1. Install uv. Python 3.11 or 3.12 is required; uv can provision a compatible version. For automatic Codex setup, make sure the Codex CLI is available. Install Node.js for YouTube extraction.

2. Download video-context-0.1.0a3-source.zip from the release page and extract it to a folder you plan to keep.

3. Double-click install.cmd. It installs dependencies, downloads the small speech model (about 500 MB), and registers the MCP server and Skill with Codex.

4. Reopen Codex and send a video link with your request.

The installer is for Windows. Other MCP clients need manual configuration; see the usage guide. macOS and Linux have not yet been tested on real machines. The package is not published on PyPI.

Use from the terminal

Run these commands in the project folder:

uv sync --frozen --no-editable --extra asr --extra mcp
uv run --no-sync video-context setup --download-model
uv run --no-sync video-context "VIDEO_URL"

Results are saved under output/. For subtitles only, skip model setup and add --asr never. Use --offline to read an existing transcript from the cache.

Privacy

Video extraction connects to the source platform. Installation also connects to dependency and model download services. Speech recognition runs locally; Video Context does not upload audio to a transcription service. When connected to an AI assistant, the resulting text is passed to that assistant.

Requests are anonymous by default. Browser sessions are read only when explicitly configured. Do not publish cookies, signed share links, or private transcripts.

Current limits

• Platform access can change; not every link will work.

• Speech recognition can miss names, technical terms, and accents. Check important quotes against the video.

• Defaults: two hours per video, 256 MiB per download, and one speech transcription job at a time.

• HLS/DASH segmented media and automatic translation are not supported.

• Designed for trusted local use. Do not expose it as a public service accepting arbitrary URLs; see Security.

Contributing

See Contributing for development commands and Validation for what has actually been tested. Documentation uses English as the primary language, with separate translations linked at the top of each page.

License

MIT. You may use, modify, and distribute the project, including commercially, while retaining the copyright and license notice. Third-party dependencies, models, and video content retain their own licenses and rights.