MCP AI Monitor

by MedusaSH

Integrations

  • Sends detailed system monitoring reports to Discord channels through configurable webhooks, with separate reports for hardware metrics and network analysis, formatted as visually optimized embeds

  • Uses pandas for data manipulation of collected system metrics before analysis and reporting

  • Built on Python 3.8+ with a CLI interface for running system monitoring, anomaly detection, and reporting commands

MCP_AI_Monitor

🔍 Overview

MCP_AI_Monitor is a comprehensive system monitoring solution that uses unsupervised machine learning algorithms to detect abnormal behavior in resource usage. Designed to provide deep visibility into your system's performance in real time, it combines data collection, predictive analysis, and detailed reporting.

✨ Main Features

  • 🤖 AI Anomaly Detection - Uses Isolation Forest to identify unusual system behaviors
  • 📊 Real-time Analysis - Continuous monitoring of CPU, RAM and network metrics
  • 🧠 Adaptive Learning - Adjusts to your system's normal behavior to reduce false positives
  • 📱 Instant Notifications - System alerts when anomalies are detected
  • 📈 Detailed Visualizations - Resource usage graphs with trend identification
  • ⚙️ Process Analysis - Identification of resource-intensive applications
  • 🌐 Network Monitoring - Analyze active connections and network performance
  • 📡 Discord Integration - Detailed reports automatically sent to your Discord channels
  • 🎨 Modern CLI Interface - Colorful and intuitive display in the terminal

🚀 Available orders

OrderDescription
python mcp.py collectSystem data collection (CPU, RAM)
python mcp.py trainTrains AI model for anomaly detection
python mcp.py monitorLaunches real-time monitoring with anomaly detection
python mcp.py statsGenerates usage graphs and statistics
python mcp.py discordSends detailed reports to Discord
python mcp.py networkAnalyzes the network and sends a dedicated report
python mcp.py allPerforms the complete sequence (collection, training, monitoring)

🛠️ Architecture

MCP_AI_Monitor is composed of several add-ons:

  1. Data collection module ( collect_data.py )
    • Records system metrics at regular intervals
    • Stores data in CSV format for later analysis
  2. AI Training Module ( train_model.py )
    • Pre-processes the collected data
    • Train an Isolation Forest model for anomaly detection
    • Save the model for real-time use
  3. Monitoring module ( monitor_ai.py )
    • Uses the trained model to detect anomalies in real time
    • Implements a learning phase to adapt to normal behavior
    • Distinguishes application launches from real anomalies
  4. Discord Integration
    • Sends separate reports for hardware and network
    • Uses configurable webhooks for each data category
    • Optimized visual format with thematic embeds

📊 Discord Reports

MCP_AI_Monitor generates detailed reports and sends them to Discord via dedicated webhooks:

Hardware reports

  • System Information - Details about CPU, RAM, OS
  • Usage Graphs - Visualize CPU/RAM Trends
  • Active Processes - List of the most power-hungry applications

Network reports

  • Network activity - Upload/download speeds, data volumes
  • Network Interfaces - Details of active interfaces and their IP addresses
  • Active Connections - Tracking established connections and associated processes

📋 Prerequisites

  • Python 3.8+
  • Python dependencies (installable via pip install -r requirements.txt ):
    • psutil - System Data Collection
    • scikit-learn - Machine learning algorithms
    • pandas - Data Manipulation
    • matplotlib - Graph generation
    • colorama - Colorful display in the terminal
    • discord-webhook - Integration with Discord

🔧 Installation

  1. Clone this repository:
git clone https://github.com/MedusaSH/MCP_AI_Monitor.git cd MCP_AI_Monitor
  1. Install the dependencies:
pip install -r requirements.txt
  1. Configure your Discord webhooks (optional):
    • Change the webhook URLs in the mcp.py file
    • Ability to use separate webhooks for hardware and network reports

📖 User guide

Quick Start

For a first full use:

# Collecte de données (60 secondes par défaut) python mcp.py collect # Entraînement du modèle IA python mcp.py train # Surveillance en temps réel python mcp.py monitor

Automated workflow

To run the entire process in one command:

# Exécute la séquence complète et envoie un rapport sur Discord python mcp.py all --duration 120 --report

🔍 Anomaly detection

The system uses an Isolation Forest algorithm to detect abnormal behavior:

  1. Learning Phase - Collecting data to establish a baseline
  2. Dynamic adaptation - Adjusting thresholds based on normal behavior
  3. Smart Filtering - Detect app launches to reduce false positives
  4. Anomaly Scoring - Classifying Events by Level of Abnormality

🌱 Contribution

Contributions are welcome! To contribute:

  1. Fork the project
  2. Create a branch for your feature ( git checkout -b feature/amazing-feature )
  3. Commit your changes ( git commit -m 'Add some amazing feature' )
  4. Push to the branch ( git push origin feature/amazing-feature )
  5. Open a Pull Request

📜 License

This project is licensed under the MIT License. See the LICENSE file for more information.

👥 Authors

  • MedusaSH - Initial Development - Github

🙏 Acknowledgments

  • Forest insulation by scikit-learn
  • psutil for accessing system metrics
  • discord-webhook library for Discord integration

-
security - not tested
F
license - not found
-
quality - not tested

An advanced system monitoring solution that uses unsupervised machine learning algorithms to detect abnormal resource usage patterns in real-time, with features including anomaly detection, process analysis, and Discord integration.

  1. 🔍 Overview
    1. ✨ Main Features
      1. 🚀 Available orders
        1. 🛠️ Architecture
          1. 📊 Discord Reports
            1. Hardware reports
            2. Network reports
          2. 📋 Prerequisites
            1. 🔧 Installation
              1. 📖 User guide
                1. Quick Start
                2. Automated workflow
              2. 🔍 Anomaly detection
                1. 🌱 Contribution
                  1. 📜 License
                    1. 👥 Authors
                      1. 🙏 Acknowledgments

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