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MCP Video Parser

by michaelbaker-dev
GitHub
Python
MIT License
2
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MCP Video Parser

A powerful video analysis system that uses the Model Context Protocol (MCP) to process, analyze, and query video content using AI vision models.

🎬 Features

  • AI-Powered Video Analysis: Automatically extracts and analyzes frames using vision LLMs (Llava)

  • Natural Language Queries: Search videos using conversational queries

  • Time-Based Search: Query videos by relative time ("last week") or specific dates

  • Location-Based Organization: Organize videos by location (shed, garage, etc.)

  • Audio Transcription: Extract and search through video transcripts

  • Chat Integration: Natural conversations with Mistral/Llama while maintaining video context

  • Scene Detection: Intelligent frame extraction based on visual changes

  • MCP Protocol: Standards-based integration with Claude and other MCP clients

🚀 Quick Start

Prerequisites

  • Python 3.10+

  • Ollama installed and running

  • ffmpeg (for video processing)

Installation

  1. Clone the repository:

git clone https://github.com/michaelbaker-dev/mcpVideoParser.git cd mcpVideoParser

  1. Install dependencies:

pip install -r requirements.txt

  1. Pull required Ollama models:

ollama pull llava:latest # For vision analysis ollama pull mistral:latest # For chat interactions

  1. Start the MCP server:

python mcp_video_server.py --http --host localhost --port 8000

Basic Usage

  1. Process a video:

python process_new_video.py /path/to/video.mp4 --location garage

  1. Start the chat client:

python standalone_client/mcp_http_client.py --chat-llm mistral:latest

  1. Example queries:

  • "Show me the latest videos"

  • "What happened at the garage yesterday?"

  • "Find videos with cars"

  • "Give me a summary of all videos from last week"

🏗️ Architecture

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Video Files │────▶│ Video Processor │────▶│ Frame Analysis │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ │ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ MCP Server │◀────│ Storage Manager │◀────│ Ollama LLM │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ ▼ ┌─────────────────┐ │ HTTP Client │ └─────────────────┘

🛠️ Configuration

Edit config/default_config.json to customize:

  • Frame extraction rate: How many frames to analyze

  • Scene detection sensitivity: When to capture scene changes

  • Storage settings: Where to store videos and data

  • LLM models: Which models to use for vision and chat

See Configuration Guide for details.

🔧 MCP Tools

The server exposes these MCP tools:

  • process_video - Process and analyze a video file

  • query_location_time - Query videos by location and time

  • search_videos - Search video content and transcripts

  • get_video_summary - Get AI-generated summary of a video

  • ask_video - Ask questions about specific videos

  • analyze_moment - Analyze specific timestamp in a video

  • get_video_stats - Get system statistics

  • get_video_guide - Get usage instructions

🛠️ Utility Scripts

Video Cleanup

Clean all videos from the system and reset to a fresh state:

# Dry run to see what would be deleted python clean_videos.py --dry-run # Clean processed files and database (keeps originals) python clean_videos.py # Clean everything including original video files python clean_videos.py --clean-originals # Skip confirmation and backup python clean_videos.py --yes --no-backup

This script will:

  • Remove all video entries from the database

  • Delete all processed frames and transcripts

  • Delete all videos from the location-based structure

  • Optionally delete original video files

  • Create a backup of the database before cleaning (unless --no-backup)

Video Processing

Process individual videos:

# Process a video with automatic location detection python process_new_video.py /path/to/video.mp4 # Process with specific location python process_new_video.py /path/to/video.mp4 --location garage

📖 Documentation

🚦 Development

Running Tests

# All tests python -m pytest tests/ -v # Unit tests only python -m pytest tests/unit/ -v # Integration tests (requires Ollama) python -m pytest tests/integration/ -v

Project Structure

mcp-video-server/ ├── src/ │ ├── llm/ # LLM client implementations │ ├── processors/ # Video processing logic │ ├── storage/ # Database and file management │ ├── tools/ # MCP tool definitions │ └── utils/ # Utilities and helpers ├── standalone_client/ # HTTP client implementation ├── config/ # Configuration files ├── tests/ # Test suite └── video_data/ # Video storage (git-ignored)

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

📝 Roadmap

  • ✅ Basic video processing and analysis

  • ✅ MCP server implementation

  • ✅ Natural language queries

  • ✅ Chat integration with context

  • 🚧 Enhanced time parsing (see INTELLIGENT_QUERY_PLAN.md)

  • 🚧 Multi-camera support

  • 🚧 Real-time processing

  • 🚧 Web interface

🐛 Troubleshooting

Common Issues

  1. Ollama not running:

ollama serve # Start Ollama

  1. Missing models:

ollama pull llava:latest ollama pull mistral:latest

  1. Port already in use:

# Change port in command python mcp_video_server.py --http --port 8001

📄 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

  • Built on FastMCP framework

  • Uses Ollama for local LLM inference

  • Inspired by the Model Context Protocol specification

💬 Support

Version: 0.1.1
Author: Michael Baker
Status: Beta - Breaking changes possible

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

A video analysis system that uses AI vision models to process, analyze, and query video content through natural language, enabling users to search videos by time, location, and content.

  1. 🎬 Features
    1. 🚀 Quick Start
      1. Prerequisites
      2. Installation
      3. Basic Usage
    2. 🏗️ Architecture
      1. 🛠️ Configuration
        1. 🔧 MCP Tools
          1. 🛠️ Utility Scripts
            1. Video Cleanup
            2. Video Processing
          2. 📖 Documentation
            1. 🚦 Development
              1. Running Tests
              2. Project Structure
            2. 🤝 Contributing
              1. 📝 Roadmap
                1. 🐛 Troubleshooting
                  1. Common Issues
                2. 📄 License
                  1. 🙏 Acknowledgments
                    1. 💬 Support

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