Smart Clip MCP
An AI-powered video editing server that transforms long videos into highlight clips with intelligent editing and platform adaptation. It provides 5 tools:
smart_clip: Automatically detect and extract the most engaging moments from a long video, with controls for clip count, duration range, platform target, subtitle burn-in, and background music.repurpose: Convert long-form video into platform-optimized short clips for TikTok, YouTube Shorts, or Instagram Reels, with style preferences (informative, entertaining, emotional) and automatic aspect ratio adjustment.highlight_reel: Aggregate highlight clips from multiple videos into a single themed compilation reel with a configurable target duration.analyze_content: Get a structured analysis report (highlights, structure, sentiment) without performing any actual clipping â useful for previewing content before editing.get_edit_plan: Generate a detailed clipping plan for human review and approval before any actual edits are executed; the approved plan can then be passed tosmart_clip.
Underneath, the server uses LLM-driven semantic analysis, Whisper-based subtitle transcription, and audio energy/silence detection to identify precise cut points.
Repurpose long videos into platform-optimized short clips for Instagram Reels, with auto-resize and formatting.
Uses OpenAI language models (e.g., GPT-4o-mini) for LLM-driven highlight detection, semantic analysis, and editing decisions.
Repurpose long videos into platform-optimized short clips for TikTok, with auto-resize and formatting.
Repurpose long videos into platform-optimized short clips for YouTube Shorts, with auto-resize and formatting.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Smart Clip MCPExtract 5 highlight clips from this 1-hour podcast video"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Smart Clip MCP
AI-powered smart video clipping MCP server. Input a long video + editing intent, output highlight short clips.
Not another FFmpeg wrapper â 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
đ§ LLM-driven highlight detection â analyzes subtitles to identify the most engaging moments
đŹ 5 MCP tools â smart_clip, repurpose, highlight_reel, analyze_content, get_edit_plan
đŻ Platform-adaptive â auto-resize and format for TikTok, YouTube Shorts, Instagram Reels
đ Auto subtitles â Whisper transcription + burn-in with platform-specific styling
đ Audio analysis â energy peaks and silence detection for precise cut points
đ Human-in-the-loop â preview edit plans before execution
đ° Low cost â ÂĽ0.8-1.16 per hour of video (50x cheaper than cloud alternatives)
Related MCP server: podcli
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-mcpConfigure MCP Client
Claude Code:
claude mcp add smart-clip -- uvx --from smart-clip-mcp smart-clip-mcpClaude 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 |
| Auto-detect highlights and clip them from a long video |
| Convert long video to platform-specific short clips |
| Compile highlights from multiple videos into a reel |
| Analyze video content without clipping (preview mode) |
| Generate an edit plan for human review before execution |
Architecture
Video â [Analyzer] â [Planner] â [Executor] â Clips
â â â
â Whisper â LLM â mcp-video
â librosa â Prompts â FFmpeg
â PySceneDetect â Strategy âAnalyzer â Content understanding: Whisper transcription, audio energy analysis, scene detection
Planner â Decision making: LLM highlight detection, template matching, strategy engine
Executor â 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: highDevelopment
# Clone
git clone git@github.com: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 8000Docker
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-mcpDocker 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 downTest with MCP Inspector
Once the server is running in SSE mode:
# Install MCP Inspector
npx @modelcontextprotocol/inspector
# Connect to http://localhost:8000/sseOr 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 â see LICENSE.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Ambrose1/Smart-Clip-MCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server