🎬 Enhanced Multimedia Analysis MCP

A Model Context Protocol (MCP) server for professional multimedia content analysis and AI video generation prompt engineering

🌟 Overview

The Enhanced Multimedia Analysis MCP Server is a production-ready Model Context Protocol implementation that provides AI agents with sophisticated tools for analyzing visual content (images and videos) and generating optimized prompts for AI video generation systems.

Core Capabilities

• 🔍 Systematic multi-dimensional content analysis via hotkey framework

• 🎨 Professional prompt generation for AI video/image generators

• 📱 Platform-specific optimization (TikTok, Instagram, YouTube, Cinema)

• 👥 Character consistency tracking across scenes

• 📊 Four analysis depth levels (Quick, Standard, Deep, Comprehensive)

• ⚡ Quick activation via /aiv slash command

Key Benefits

✅ Reduces prompt engineering time from hours to minutes ✅ Improves prompt quality through systematic analysis ✅ Enables consistency across multiple generations ✅ Optimizes for platforms automatically ✅ Empowers AI agents with 100+ analysis dimensions

Related MCP server: videoseek-mcp

🚀 Quick Start

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/enhanced-multimedia-analysis-mcp.git
   cd enhanced-multimedia-analysis-mcp

2. Install dependencies:

   pip install -r requirements.txt

3. Install the /aiv command:

   ./scripts/install_aiv.sh

4. Configure Claude Desktop:

   Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

   {
     "mcpServers": {
       "video-analysis": {
         "command": "python3",
         "args": ["/path/to/enhanced-multimedia-analysis-mcp/video_analysis_mcp.py"]
       }
     }
   }

5. Restart Claude Desktop and test:

   /aiv sunset over mountains with dramatic clouds

💡 Usage Examples

Basic Analysis

/aiv A majestic eagle soaring over mountains at sunset

With Platform Optimization

/aiv 30-second product video --platform Instagram --depth deep

Character-Focused Analysis

/aiv Detective noir scene --focus character consistency, cinematography

With Custom Hotkeys

/aiv Epic battle scene --hotkeys A1,C1,L1,E1 --format json

Available Options

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                     Claude Desktop / MCP Client                  │
│                    Slash Commands: /aiv                          │
└────────────────────────────┬────────────────────────────────────┘
                             │ JSON-RPC 2.0 over stdio
┌────────────────────────────▼────────────────────────────────────┐
│              Video Analysis MCP Server                           │
│              (video_analysis_mcp.py)                            │
│                                                                  │
│  ┌────────────────────────────────────────────────────────┐   │
│  │              4 MCP Tools                                │   │
│  │  • video_analysis_analyze_image                        │   │
│  │  • video_analysis_analyze_video                        │   │
│  │  • video_analysis_analyze_multimedia                   │   │
│  │  • video_analysis_get_hotkeys                          │   │
│  └─────────────────────┬──────────────────────────────────┘   │
│                        │                                        │
│  ┌─────────────────────▼──────────────────────────────────┐   │
│  │         Analysis Engine (Hotkey-Based)                  │   │
│  └─────────────────────┬──────────────────────────────────┘   │
│                        │                                        │
│  ┌─────────────────────▼──────────────────────────────────┐   │
│  │         Prompt Generator                                │   │
│  └─────────────────────┬──────────────────────────────────┘   │
│                        │                                        │
│  ┌─────────────────────▼──────────────────────────────────┐   │
│  │         Output Formatter (Markdown/JSON)                │   │
│  └─────────────────────────────────────────────────────────┘   │
└────────────────────────────────────────────────────────────────┘

Technical Stack

• Framework: MCP Python SDK (FastMCP)

• Validation: Pydantic v2 models

• Python Version: 3.10+

• Design Pattern: Tool-oriented, stateless

• Communication: JSON-RPC 2.0 over stdio

📚 Documentation

• Master Specification - Complete system documentation

• /aiv Command Guide - Slash command usage and examples

• Installation Script - Located in scripts/install_aiv.sh

🔧 Configuration

Environment Variables

Configure the MCP server behavior using environment variables:

# Output character limit
export VIDEO_ANALYSIS_CHAR_LIMIT=25000

# Enable debug logging
export VIDEO_ANALYSIS_DEBUG=false

# Enable caching (improves performance)
export VIDEO_ANALYSIS_CACHE_ENABLED=true
export VIDEO_ANALYSIS_CACHE_DIR=/tmp/video_analysis_cache
export VIDEO_ANALYSIS_CACHE_TTL=3600

# Set default analysis depth
export VIDEO_ANALYSIS_DEFAULT_DEPTH=standard

Claude Desktop Configuration

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "video-analysis": {
      "command": "python3",
      "args": ["/path/to/video_analysis_mcp.py"],
      "env": {
        "VIDEO_ANALYSIS_CHAR_LIMIT": "25000",
        "VIDEO_ANALYSIS_CACHE_ENABLED": "true",
        "VIDEO_ANALYSIS_CACHE_DIR": "/tmp/video_analysis_cache"
      }
    }
  }
}

🎯 Features

Analysis Framework

The system uses a comprehensive hotkey-based analysis framework with 100+ dimensions organized into categories:

• A-Series: Aesthetic & Visual Style (A1-A13)

• S-Series: Story & Narrative (S1-S12)

• C-Series: Character & Subject (C1-C12)

• K-Series: Cinematography (K1-K13)

• P-Series: Platform Optimization (P1-P10)

• E-Series: Execution & Technical (E1-E12)

Analysis Depths

Platform Optimizations

• TikTok: Vertical format, hook-first, trending sounds

• Instagram: Aesthetic-first, grid-aware, story integration

• YouTube: Thumbnail optimization, retention focus, SEO

• Cinema: Cinematic language, aspect ratios, theatrical quality

🚢 Deployment

Docker

docker build -t video-analysis-mcp:1.1.0 .
docker run -d --name video-analysis-mcp video-analysis-mcp:1.1.0

Systemd Service

See docs/MASTER_SPECIFICATION.md for complete deployment instructions including:

• Systemd service configuration

• Kubernetes deployment

• Docker Compose setup

• Monitoring & observability

🧪 Testing

Run comprehensive tests:

python3 -m pytest tests/

Test individual tools:

# Test image analysis
python3 -c "from video_analysis_mcp import test_image_analysis; test_image_analysis()"

# Test video analysis
python3 -c "from video_analysis_mcp import test_video_analysis; test_video_analysis()"

📈 Performance

With Caching Enabled

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

1. Clone the repository

2. Install development dependencies: pip install -r requirements-dev.txt

3. Run tests: pytest tests/

4. Follow the code style guide (PEP 8)

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built on the Model Context Protocol by Anthropic

• Uses FastMCP for MCP server implementation

• Inspired by professional video production workflows

📞 Support

• Issues: GitHub Issues

• Documentation: docs/MASTER_SPECIFICATION.md

• Discussions: GitHub Discussions

🗺️ Roadmap

• Real-time video file analysis

• Integration with popular AI video generators

• Web interface for prompt generation

• Batch processing capabilities

• Advanced caching strategies

• Multi-language support

Made with ❤️ for the AI video generation community

Version 1.1.0 | Changelog | Documentation