Video Extract

Name: Video Extract
Author: yanlingLabs

by yanlingLabsversion 0.16.2i★ –iRuns on your computer

Registry namecom.yanlinglabs/video-extract-mcp

Install

Overview

Description from the maker (original English)

Download any video from a URL, or get its transcript and key frames. All local, no API keys.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiVery new↓ 209/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: VIDEO_EXTRACT_COOKIES_FROM_BROWSER, VIDEO_EXTRACT_MODELS_DIR, VIDEO_EXTRACT_MAX_CONCURRENCY

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "video-extract": {
      "command": "npx",
      "args": [
        "-y",
        "@yanlinglabs/video-extract-mcp"
      ],
      "env": {
        "VIDEO_EXTRACT_COOKIES_FROM_BROWSER": "your-value",
        "VIDEO_EXTRACT_MODELS_DIR": "your-value",
        "VIDEO_EXTRACT_MAX_CONCURRENCY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add video-extract --env VIDEO_EXTRACT_COOKIES_FROM_BROWSER=your-value --env VIDEO_EXTRACT_MODELS_DIR=your-value --env VIDEO_EXTRACT_MAX_CONCURRENCY=your-value -- npx -y @yanlinglabs/video-extract-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "video-extract": {
      "command": "npx",
      "args": [
        "-y",
        "@yanlinglabs/video-extract-mcp"
      ],
      "env": {
        "VIDEO_EXTRACT_COOKIES_FROM_BROWSER": "your-value",
        "VIDEO_EXTRACT_MODELS_DIR": "your-value",
        "VIDEO_EXTRACT_MAX_CONCURRENCY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "video-extract": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@yanlinglabs/video-extract-mcp"
      ],
      "env": {
        "VIDEO_EXTRACT_COOKIES_FROM_BROWSER": "your-value",
        "VIDEO_EXTRACT_MODELS_DIR": "your-value",
        "VIDEO_EXTRACT_MAX_CONCURRENCY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

VIDEO_EXTRACT_COOKIES_FROM_BROWSER env
Set to 'auto' to borrow browser cookies only when a platform refuses a download (age-restricted, members-only, or bot-checked media).
VIDEO_EXTRACT_MODELS_DIR env
Where local speech models live. Defaults to ~/.cache/video-extract-mcp/models; they are fetched on demand.
VIDEO_EXTRACT_MAX_CONCURRENCY env
How many analyses may run at once. Default 2; each peaks at about 2 GB when Whisper transcribes, 1.1 GB with SenseVoice.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 9, 2026

✓ Pass
Maker matches the repo
The registry name was verified against the domain yanlinglabs.com (the registry checks domain ownership)
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Design, images & audioi

More stars than 0% of the other 2809 servers in this category
Published to the registry more recently than 37% of the other 2809 servers in this category
Basic checks equal to or better than 100% of the other 2809 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Video Extract safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.16.2) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

video-extract-mcp

Give an AI agent any video link: download the file, read the transcript, or get just the frames that matter — all on your own machine.

Two jobs, and you can use either on its own:

Get the video. A YouTube link, a TikTok, a WeChat Channels share URL, a raw .mp4, or a page from a site nobody has heard of — it resolves and downloads it, whole or just the section you asked for. If that is all you need, stop there; nothing forces you to analyse anything.

Or read it. A transcript (real captions when the platform has them, local speech recognition when it does not) and a small set of *important* keyframes — deduplicated, scene-aware, and scored — instead of a thousand near-identical stills.

Built for AI agents. Three MCP tools, no cloud, no API keys, no Python.

claude mcp add --scope user video-extract -- npx -y @yanlinglabs/video-extract-mcp@latest

codex mcp add video-extract -- npx -y @yanlinglabs/video-extract-mcp@latest

What it can do

Get the video, from almost anywhere. Paste a link and it resolves: YouTube, TikTok, Instagram, X, Facebook and Reels, Twitch, Vimeo, Reddit, WeChat Channels, a bare .mp4 or .m3u8, or a page on a site nobody has heard of. Local files work too. Anything unsupported comes back as a clear failure rather than a crash.

From there you choose how much work to pay for:

Just look it up. Title, creator, duration, chapter list, description, comment count — without downloading a byte. On a long video this is how you find the one section worth analysing.
Download the file. The whole thing, or just seconds 300–420 of it. Where the platform supports ranged fetching, only that section is transferred rather than the full video.
Get a transcript. Real captions when the platform has them, in any language it publishes; local speech recognition when it has none. The result says which you got. A captions-only request skips the download entirely — seconds instead of minutes.
Get the frames that matter. Not every Nth frame: scene changes, on-screen text appearing, genuinely new visuals — deduplicated and scored, typically a few dozen for an hour of video. Or uniform sampling, or one exact frame at one exact second, if that is what you need.
Do several at once. Pass a list of videos and get a result per video; one failing does not sink the rest.
Reach private or rate-limited content. Point it at your browser's cookies and it can fetch age-restricted, members-only or followers-only media, and shrug off the bot checks that block anonymous downloads.
Run long jobs in the background. A full analysis of an hour-long video takes minutes; it can run as a background task, report progress over a local HTTP endpoint, and hand back the result even if your client gave up waiting.

Everything happens on your machine. No API key, no upload, no third-party service.

Why this exists

Two problems, really. Getting the video at all — every platform hides its media behind a different mechanism, and none of them want a script fetching it. And then reading it: an LLM cannot watch a video, and the usual workaround — dump every Nth frame into the context window — burns enormous amounts of context on frames that are 98% identical to the one before, while still missing the slide that changed when nothing else moved.

video-extract-mcp handles both:

Fetching, from almost anywhere. One resolver chain covers the big platforms, direct media URLs, and generic sites, with ranged fetches where the platform allows them and cookie support for anything that needs a login. Downloading is a complete use of this tool, not a step on the way to something else.

Transcript, honestly sourced. The platform's own captions are used whenever the video has any — human-written first, otherwise the platform's automatic ones. Audio is transcribed locally (Whisper or SenseVoice) only when the video has no captions, or when the captions it has could not be retrieved. The result tells you which you got via transcript.source, and for a local transcript transcript.asrReason says why (no_captions or captions_failed; a failed retrieval is also listed in warnings).
Keyframes chosen, not sampled. Scene-boundary detection, blur/quality filtering, on-screen-text novelty (subtitle-aware, so burned-in captions don't preserve redundant frames), and image-embedding similarity feed an iterative diversity-aware selector.
Output goes to disk, not into your context. The tool reply is a compact summary plus file paths. A 35-frame manifest and a full transcript don't belong in a conversation where the agent needs three numbers from them.
Everything runs on your machine. No third-party API, no upload, no key. Long analyses can run as MCP background tasks — the tool returns a handle immediately and pushes progress; see Background tasks below.

Quick start

Install the system programs first; they can't come from npm. You need ffmpeg (with ffprobe), yt-dlp, Deno (yt-dlp needs it for YouTube), tesseract with Chinese language data, and Node ≥ 22.12.

macOS (the tested platform):

brew install node ffmpeg yt-dlp tesseract tesseract-lang    # Homebrew's yt-dlp brings Deno along

Windows (not yet run there; these come from the package manifests), in PowerShell:

winget install OpenJS.NodeJS.LTS

winget install yt-dlp.yt-dlp # also installs Deno and an ffmpeg build with ffprobe winget install tesseract-ocr.tesseract

Then open a new terminal. Tesseract's installer doesn't add itself to PATH, and winget doesn't add Chinese language data; INSTALL.md has both steps.

Linux: your distribution's yt-dlp is usually too old for YouTube, and Deno isn't packaged. INSTALL.md has the Debian/Ubuntu and Fedora steps.

Restart your agent after installing, so the server it launches sees the new programs.

Then point your MCP client at the package. There are two ways, and they differ in ways worth thirty seconds of your time.

Option A — `npx`, nothing installed. Simplest, and it picks up new releases on its own.

Claude Code:

claude mcp add --scope user video-extract -- npx -y @yanlinglabs/video-extract-mcp@latest

Codex:

codex mcp add video-extract -- npx -y @yanlinglabs/video-extract-mcp@latest

Another agent? Point it at [INSTALL.md](https://github.com/yanlingLabs/video-extract-mcp/blob/main/INSTALL.md) and it can install itself. It covers Gemini CLI, Grok, opencode, Hermes Agent, OpenClaw, Pi, Kilo Code, Cline and DeepSeek Harness, says how each recipe was checked, and lists the tool-call timeout to raise in each: a full analysis takes minutes, and several stop waiting after one.

Keep the `@latest` — without it npx pins to the first version it cached and never updates.

Option B — installed globally. Starts faster and gives you the video-extract status CLI as a real command.

npm install -g @yanlinglabs/video-extract-mcp

Then register it — Claude Code:

claude mcp add --scope user video-extract -- video-extract-mcp

Codex:

codex mcp add video-extract -- video-extract-mcp

	`npx` (A)	global install (B)
Updates	automatic only with `@latest` in the spec — a bare `npx -y @yanlinglabs/video-extract-mcp` pins to the first version it cached and never updates	manual: `npm update -g @yanlinglabs/video-extract-mcp`. You stay on the installed version until you run it
Startup	~0.9s (npm resolution on every launch)	~0.1s
`video-extract status` in your shell	not on `PATH` — needs `npx -y -p @yanlinglabs/video-extract-mcp video-extract status`	works directly
Working directory	must not be this package's own checkout (see below)	irrelevant

Neither affects what agents can do: an agent checks on background work over HTTP using the statusUrl handed to it in the reply, never a shell command. The CLI is for humans.

Or in any MCP client's config — "command": "npx", "args": ["-y", "@yanlinglabs/video-extract-mcp@latest"] for A, or "command": "video-extract-mcp" with no args for B:

"mcpServers": { "video-extract": { "command": "npx", "args": ["-y", "@yanlinglabs/video-extract-mcp@latest"] } } }

One gotcha with `npx`, and it only bites contributors. Run inside this package's own git checkout, npx @yanlinglabs/video-extract-mcp fails with command not found — npx sees the local package.json claiming that name, looks for the binary in a local node_modules/.bin that was never populated, and gives up. Since MCP clients launch servers with the working directory set to your project, option A cannot work *in this repo*. Working on the tool itself? Point that one project at your build — claude mcp add --scope local video-extract -- node "$PWD/dist/mcp.js" — which also means a npm run build takes effect immediately, with no publish round-trip. Everywhere else, npx is fine.

That is enough for any video that has captions — which, thanks to the caption-first transcript policy, is most of them. The vision model downloads itself on first use.

Speech models are only needed for videos with no captions at all, and they are fetched automatically the first time one is. Only the engine that video needs is downloaded — 233 MB for the Chinese/Japanese/Korean model, 1.3 GB for Whisper — into ~/.cache/video-extract-mcp/models. Set VIDEO_EXTRACT_AUTO_FETCH_MODELS=0 to keep it manual, or pre-fetch them yourself:

npx -y -p @yanlinglabs/video-extract-mcp@latest video-extract cookies   # installs the package, then exits

curl -fsSL https://raw.githubusercontent.com/yanlingLabs/video-extract-mcp/main/scripts/fetch-models.sh \ | bash -s -- ~/.cache/video-extract-mcp/models

~/.cache/video-extract-mcp/models is where the tool looks by default. Override with VIDEO_EXTRACT_MODELS_DIR. If the fetch is disabled or fails, an uncaptioned video still returns frames and records a warning explaining why the transcript is missing — it degrades rather than fails.

From source (contributors)

git clone https://github.com/yanlingLabs/video-extract-mcp.git

cd video-extract-mcp npm install && npm run build ./scripts/fetch-models.sh # into ./models, which takes precedence when present npm run preflight # verifies ffmpeg / ffprobe / yt-dlp / tesseract

Environment variables

Variable	Purpose
`VIDEO_EXTRACT_MODELS_DIR`	Where speech models live. Defaults to `./models` when that exists, else `~/.cache/video-extract-mcp/models`.
`VIDEO_EXTRACT_AUTO_FETCH_MODELS`	Set `0` to stop the speech models being downloaded on demand. They are then your job (`scripts/fetch-models.sh`), and an uncaptioned video degrades with a warning saying so.

| VIDEO_EXTRACT_COOKIES_FILE | Path to a Netscape-format cookie jar, used for every yt-dlp source at once — YouTube, Instagram, Facebook, X, TikTok, Twitch and the rest. See [Authenti

Only the beginning is shown; read the rest in the repo

Video Extract

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

video-extract-mcp

What it can do

Why this exists

Quick start

From source (contributors)

Environment variables