Smart Clip

AI-powered smart video clipping MCP server. Input a long video + editing intent, output highlight short clips.

Not another FFmpeg wrapper — it's the "editing brain". Uses subtitle semantic analysis + LLM-driven decision making to identify highlight moments, with mcp-video as the execution layer (FFmpeg fallback built-in).

Features

• 🧠 LLM-driven highlight detection — analyzes subtitles to identify the most engaging moments
• 🎬 5 MCP tools — smart_clip, repurpose, highlight_reel, analyze_content, get_edit_plan
• 🎯 Platform-adaptive — auto-resize and format for TikTok, YouTube Shorts, Instagram Reels
• 📝 Auto subtitles — Whisper transcription + burn-in with platform-specific styling
• 🔊 Audio analysis — energy peaks and silence detection for precise cut points
• 👀 Human-in-the-loop — preview edit plans before execution
• 💰 Low cost — ¥0.8-1.16 per hour of video (50x cheaper than cloud alternatives)

Quick Start

Prerequisites

• Python 3.11+
• FFmpeg installed and on PATH
• mcp-video (auto-installed as dependency)
• Whisper model (auto-downloaded on first use)

Install

pip install smart-clip-mcp

Configure MCP Client

Claude Code:

claude mcp add smart-clip -- uvx --from smart-clip-mcp smart-clip-mcp

Claude Desktop / Cursor:

{
  "mcpServers": {
    "smart-clip": {
      "command": "uvx",
      "args": ["--from", "smart-clip-mcp", "smart-clip-mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Usage

Ask your AI agent:

"Extract 5 highlight clips from this 1-hour podcast video"

"Turn this interview into 3 TikTok-ready shorts"

"Analyze this video and tell me the most engaging moments"

MCP Tools

Tool	Description
smart_clip	Auto-detect highlights and clip them from a long video
repurpose	Convert long video to platform-specific short clips
highlight_reel	Compile highlights from multiple videos into a reel
analyze_content	Analyze video content without clipping (preview mode)
get_edit_plan	Generate an edit plan for human review before execution

Architecture

Video → [Analyzer] → [Planner] → [Executor] → Clips
          │              │            │
          │ Whisper       │ LLM        │ mcp-video
          │ librosa       │ Prompts    │ FFmpeg
          │ PySceneDetect │ Strategy   │

• Analyzer — Content understanding: Whisper transcription, audio energy analysis, scene detection
• Planner — Decision making: LLM highlight detection, template matching, strategy engine
• Executor — Clip generation: trim, merge, subtitles, platform adaptation via mcp-video

Configuration

Create ~/.smart-clip/config.yaml:

analyzer:
  whisper:
    mode: local          # local | api
    model: large-v3
    language: zh
  audio:
    energy_percentile: 90
    silence_threshold: 0.3

planner:
  llm:
    model: gpt-4o-mini
    temperature: 0
  strategy:
    min_score: 6.0
    min_gap: 10

executor:
  output:
    format: mp4
    quality: high

Development

# Clone
git clone [email protected]:Ambrose1/Smart-Clip-MCP.git
cd Smart-Clip-MCP

# Create venv
python -m venv .venv
source .venv/bin/activate

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run MCP server locally (stdio mode)
smart-clip-mcp

# Run MCP server with SSE transport (HTTP)
smart-clip-mcp --transport sse --port 8000

Docker

Build & Run

# Build image
docker build -t smart-clip-mcp .

# Run with SSE transport (accessible via HTTP)
docker run -d \
  -p 8000:8000 \
  -e OPENAI_API_KEY=sk-... \
  -v $(pwd)/videos:/workspace/videos \
  -v $(pwd)/output:/workspace/output \
  smart-clip-mcp

Docker Compose (recommended)

# Set your API key
export OPENAI_API_KEY=sk-...

# Start
docker compose up -d

# View logs
docker compose logs -f

# Stop
docker compose down

Test with MCP Inspector

Once the server is running in SSE mode:

# Install MCP Inspector
npx @modelcontextprotocol/inspector

# Connect to http://localhost:8000/sse

Or test with curl:

# List available tools
curl -X POST http://localhost:8000/messages \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0.1.0"}}}'

License

Apache 2.0 — see LICENSE.