mcp-video

Public Discovery

mcp-video is a free, open-source Model Context Protocol (MCP) server, Python library, and CLI that gives AI agents a real video-editing surface. It wraps FFmpeg, PUSHING CREATION-style planning, media analysis, quality checks, subtitles, audio generation, effects, Hyperframes 0.5 rendering, and local repurposing packages behind structured tool schemas.

Best-fit searches:

• video editing MCP server
• AI agent video editing
• FFmpeg MCP tools
• Claude Code video editing
• Cursor MCP video tools
• Python video editing library
• subtitle automation
• reels and shorts automation
• agentic media pipeline
• local AI video workflow
• Hyperframes video creation
• YouTube Shorts repurposing

Why It Exists

AI agents can write FFmpeg commands, but they should not have to guess flags, parse brittle stderr, or silently publish broken media. mcp-video gives agents typed operations, inspectable tool metadata, structured results, and quality checkpoints so a video workflow can be automated and reviewed without turning into shell-command roulette.

Use it when you want an AI assistant to:

• trim, merge, resize, crop, rotate, transcode, or export video;
• add text, subtitles, watermarks, overlays, filters, fades, effects, and transitions;
• extract audio, normalize audio, synthesize audio, add generated audio, or create waveforms;
• detect scenes, make thumbnails, generate storyboards, compare quality, and create release checkpoints;
• scaffold cinematic projects, read STYLE_/NEG_ blocks, parse storyboard tables, and expand shot prompts;
• create new Hyperframes projects, inspect rendered layouts, capture websites, generate local speech, remove backgrounds, and post-process the result with FFmpeg tools;
• repurpose one source video into vertical, horizontal, and square local delivery packages with manifests and review artifacts;
• drive repeatable media workflows from Claude Code, Cursor, Codex-style clients, scripts, or CI.

Installation

Prerequisite: FFmpeg must be installed and available on PATH.

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg

Run without a global install:

uvx --from mcp-video mcp-video doctor

Or install with pip:

pip install mcp-video
mcp-video doctor

Hyperframes tools additionally need Node.js 22+ because they call the Hyperframes CLI through npx.

Quick Start

Claude Code

claude mcp add mcp-video -- uvx --from mcp-video mcp-video

Claude Desktop

{
  "mcpServers": {
    "mcp-video": {
      "command": "uvx",
      "args": ["--from", "mcp-video", "mcp-video"]
    }
  }
}

Cursor

{
  "mcpServers": {
    "mcp-video": {
      "command": "uvx",
      "args": ["--from", "mcp-video", "mcp-video"]
    }
  }
}

Then ask your agent:

Trim this interview into a 45-second vertical clip, add burned captions, normalize the audio, make a thumbnail, and create a release checkpoint before export.

Python Client

from mcp_video import Client

editor = Client()

clip = editor.trim("interview.mp4", start="00:02:15", duration="00:00:45")
caption_file = "captions.srt"
editor.ai_transcribe(clip.output_path, output_srt=caption_file)
captioned = editor.subtitles(clip.output_path, subtitle_file=caption_file)
vertical = editor.resize(captioned.output_path, aspect_ratio="9:16")
checkpoint = editor.release_checkpoint(vertical.output_path)

print(checkpoint["thumbnail"])
print(checkpoint["storyboard"])

CLI

mcp-video info interview.mp4
mcp-video trim interview.mp4 -s 00:02:15 -d 45
mcp-video video-ai-transcribe clip.mp4 --output captions.srt
mcp-video subtitles clip.mp4 captions.srt
mcp-video resize clip.mp4 --aspect-ratio 9:16
mcp-video video-quality-check clip.mp4
mcp-video repurpose clip.mp4 --platforms youtube-shorts instagram-reel tiktok

What Agents Can Do

MCP Tools

mcp-video registers a broad MCP tool surface, including a search_tools discovery tool so agents can find the right operation without loading every tool description into context.

from mcp_video import Client

editor = Client()
matches = editor.search_tools("subtitle")
print(matches["tools"])

Full reference: docs/TOOLS.md

Agent-Safe Workflow

For autonomous agents, the intended path is inspect, edit, verify, then ask a human to review release artifacts:

from mcp_video import Client

client = Client()

print(client.inspect("trim"))

result = client.pipeline(
    [
        {"op": "trim", "input": "source.mp4", "start": "00:01:00", "duration": "00:00:45"},
        {"op": "add_text", "text": "Launch clip", "position": "top-center"},
        {"op": "normalize_audio"},
        {"op": "resize", "aspect_ratio": "9:16"},
        {"op": "export", "quality": "high"},
        {"op": "release_checkpoint"},
    ],
    output_path="final-short.mp4",
)

Safety contract:

• Media-producing calls return structured results with output paths.
• Analysis and discovery calls return structured JSON reports.
• Tool discovery is available through search_tools() and Client.inspect().
• Unexpected keyword errors are converted into actionable MCPVideoError guidance.
• Do not publish agent-generated video without video_quality_check, video_release_checkpoint, and human visual/audio inspection.

Documentation

• Tool reference
• Python client reference
• CLI reference
• AI agent discovery guide
• FAQ
• llms.txt

Testing

Development verification lives in docs/TESTING.md. Keep public-surface, media workflow, and security checks current when changing tool behavior.

Development

git clone https://github.com/KyaniteLabs/mcp-video.git
cd mcp-video
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest tests/ -v -m "not slow and not hyperframes"

Community

• Contributing
• Code of Conduct
• Governance
• Maintainers
• Security
• Support
• Roadmap
• Changelog
• GitHub Discussions

License

Apache 2.0. See LICENSE.

Built with FFmpeg, Hyperframes, and the Model Context Protocol.