What can you do with this server?

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 — 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 to smart_clip. Underneath, the server uses LLM-driven semantic analysis, Whisper-based subtitle transcription, and audio energy/silence detection to identify precise cut points.

Which integrations are available for this server?

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.

How do I use Smart Clip MCP?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Smart Clip MCP Extract 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

by Ambrose1

Overview Schema Source Related Servers Discussions Admin

Python

Local

Smart Clip MCP

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)

Related MCP server: short-form-video-editor-mcp

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 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 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.

Available Tools

5 tools

analyze_content_toolA

分析视频内容，输出结构化报告，不执行剪辑。用于预览和决策。

Inputs: video_path (required), focus. Returns: object

get_edit_plan_toolA

生成剪辑方案但不执行，返回方案供人工审核。审核通过后调用 smart_clip 执行。

Inputs: video_path (required), intent, clip_count, clip_duration_min, +1 more. Returns: object

highlight_reel_toolB

从多个视频中提取精彩片段合成集锦视频。支持跨视频主题聚合。

Inputs: video_paths (required), theme (required), target_duration. Returns: object

repurpose_toolA

将长视频自动重制为适配目标平台的短视频。自动识别精彩内容、裁切、加字幕、调比例。

Inputs: video_path (required), platform, clip_count, style. Returns: object

smart_clip_toolA

从长视频中自动识别精彩片段并裁切输出。适用于口播、播客、直播回放等语音驱动内容。

Inputs: video_path (required), intent, clip_count, clip_duration_min, +5 more. Returns: object

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored 2026-06-21 05:45 across 5 tools

Disambiguation4/5

Each tool has a distinct role: analysis, planning, single-video clipping, multi-video compilation, and platform-specific repurposing. There is slight overlap between smart_clip_tool and repurpose_tool in basic clipping, but their descriptions clarify different use cases.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern with the _tool suffix. Most use a verb_noun structure (analyze_content, get_edit_plan, highlight_reel, repurpose), but 'smart_clip_tool' uses an adjective-noun combination, breaking the verb pattern.

Tool Count5/5

With 5 tools, the server covers the essential functions of a video clipping assistant without being overwhelming. The count is well-scoped for the intended domain of automated editing and repurposing.

Completeness4/5

The tool set provides a complete workflow from analysis to planning to execution, plus specialized features like highlight reels and repurposing. Missing direct manual editing or plan modification, but these are not core to the automated focus.

Resources

Related MCP Connectors

keptbits
com.keptbits
Turns long videos into captioned vertical clips, cut at the moments that stand on their own.
Image & Video Processing Multimedia Processing
1
https://keptbits.com/api/mcp
OpusClip
io.github.opus-pro
Turn long videos into AI-curated short clips: caption, reframe, thumbnail, schedule, and publish.
Multimedia Processing Image & Video Processing Social Media
29
https://mcp.opus.pro/mcp
FrameOS
studio.frameos
Turn long videos into captioned, reframed short clips, thumbnails, and social posts.
Multimedia Processing Image & Video Processing
1
https://frameos.studio/mcp
Renderster
com.renderster
Long video in, captioned vertical clips out: clip, dub, transcribe and auto edit videos
Multimedia Processing Image & Video Processing Speech Processing
1
https://mcp.renderster.com/mcp

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Smart Clip MCP

Features

Quick Start

Prerequisites

Install

Configure MCP Client

Usage

MCP Tools

Architecture

Configuration

Development

Docker

Build & Run

Docker Compose (recommended)

Test with MCP Inspector

License

Available Tools

Tool Schema Changelog

TDQS

Resources

Related MCP Connectors

Related MCP Servers

mcp-lumiclipofficial

short-form-video-editor-mcp

videoscan-mcp

Oh My Cassette