mcp-video 1.5.0

Guardrailed video editing MCP server for AI agents. FFmpeg, PUSHING CREATION planning, Hyperframes, repurposing tools, Python client, and CLI. Local, fast, free.

mcp-video

Guardrailed video editing MCP server for AI agents.
Structured tools for FFmpeg video editing, cinematic prompt planning, media analysis, subtitles, audio, effects, Hyperframes video creation, local repurposing packages, and preflight validation that helps prevent silent bad media output.

Install • Quick Start • Agent Workflows • Tools • Tool Reference • AI Discovery • Agent Skill • llms.txt • MCP Registry

---

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, local repurposing packages, and guardrails for risky edit parameters 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, preflight guardrails, 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+ and a resolvable Hyperframes CLI. Install/pin Hyperframes in the active Node package layout, add hyperframes to PATH, or set MCP_VIDEO_HYPERFRAMES_COMMAND.

Quick Start

Try the receipt-backed proof first

From a clone of this repo, run the smallest confidence workflow before wiring an agent host:

uv run --no-project --with mcp-video python workflows/05-confidence-baseline/workflow.py
uv run --no-project --with mcp-video python workflows/benchmarks/run_confidence_benchmark.py

The workflow generates a tiny source clip, creates a checked vertical video, runs quality/release checkpoint steps, and writes workflows/05-confidence-baseline/output/video_receipt.json.

Proof notes live in docs/proofs/.

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.

Agent Skill

mcp-video includes a public agent skill at skills/mcp-video/SKILL.md. Use $mcp-video in compatible agent hosts when you want the agent to choose between the MCP server, CLI, and Python client while preserving the inspect, edit, verify, and human-review workflow.

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

Workflow	Example prompt
Social clips	"Turn this landscape recording into a captioned TikTok and YouTube Short."
Podcast production	"Find the strongest segment, trim it, normalize audio, add chapters, and export."
Product demos	"Create a short launch video from screenshots, title cards, and voiceover."
Cinematic planning	"Create a style pack and storyboard, then render shot prompts for generation."
Quality review	"Compare these two exports, make thumbnails, and flag visual or audio problems."
Batch automation	"Convert this folder of clips to web-ready MP4 with consistent loudness."
Code-created video	"Scaffold a Hyperframes composition, inspect it, render it, then add subtitles and a watermark."
Local repurposing	"Turn this master clip into Shorts, Reels, TikTok, and YouTube assets with thumbnails and a manifest."

MCP Tools

mcp-video currently registers 119 MCP tools. The table below summarizes the documented core categories; search_tools lets agents discover the exact operation they need without loading every tool description into context.

Category	Count	Highlights
Core video editing	32	trim, merge, resize, crop, rotate, convert, overlays, subtitles, export, cleanup, templates, merge-compatibility guardrails
Cinematic creation	4	project scaffold, style-pack parsing, storyboard parsing, shot prompt expansion
AI-assisted media	11	transcription, scene detection, upscaling, stem separation, silence removal, color grading
Hyperframes	18	init, preview, render, snapshots, inspect, catalog, website capture, local TTS, transcription, background removal, diagnostics, benchmark, post-process
Repurposing	2	dry-run manifests, platform-ready variants, thumbnails, storyboards, release checkpoints
Procedural audio	7	synthesize, compose, presets, effects, sequences, generated audio, spatial audio, mix-parameter guardrails
Visual effects	8	vignette, glow, noise, scanlines, chromatic aberration, luma key, mask, shape mask, bounded filter parameters
Transitions	3	glitch, morph, pixelate
Layout and motion	6	grid, picture-in-picture, split-screen, animated text, counters, progress bars, auto-chapters, layout mismatch warnings
Analysis	8	scene detection, thumbnail, preview, storyboard, quality compare, metadata, waveform, release checkpoint
Image analysis	3	extract colors, generate palettes, analyze product images
Discovery	1	search_tools

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.
• High-risk edit paths now run preflight guardrails before FFmpeg execution: filter bounds, merge compatibility, audio mix volume/timing, overlay/watermark/chroma opacity and similarity, animated text timing/overflow, and grid/split-screen mismatch warnings.
• 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.

---

Part of KyaniteLabs

More from KyaniteLabs. Related projects:

• Epoch — time-estimation MCP server (PERT) for AI agents
• DialectOS — Spanish dialect localization MCP server & CLI
• checkyourself — local-first production-readiness checks for AI-built code

→ More at kyanitelabs.tech