What is Devops Monitoring Mcp

DevOps-Monitoring-MCP is the Auto-Remediation Monitoring & Control Platform (ARM-CP), an intelligent DevOps solution designed to monitor, diagnose, and automatically resolve issues in containerized applications.

Use cases

Use cases include monitoring the health of microservices, automatically resolving incidents in production environments, and providing insights through dashboards for better decision-making.

How to use

To use DevOps-Monitoring-MCP, clone the repository, set up the environment by configuring the .env file with your DeepSeek API key, and then build and launch the application using Docker Compose.

Key features

Key features include real-time container monitoring, AI-powered incident diagnosis, automated remediation workflows, and isolated logging pipelines.

Where to use

DevOps-Monitoring-MCP can be used in various fields such as software development, IT operations, and any environment utilizing containerized applications for efficient monitoring and issue resolution.

Auto-Remediation Monitoring & Control Platform

🚀 Features

• Real-time container monitoring
• AI-powered incident diagnosis
• Automated remediation workflows
• Isolated logging pipelines

📦 Prerequisites

# Required Tools
- Docker Desktop 4.15+
- Python 3.9+
- Node.js 14.x (for App2)

🛠️ Installation

# Clone the repository
git clone https://github.com/VikasDz/DevOps-Monitoring-MCP.git
cd DevOps-Monitoring-MCP

# Set up environment
cp .env.example .env
nano .env  # Add your DeepSeek API key

# Build and launch
docker-compose up -d --build

🌐 Service Endpoints

🐳 Docker Commands Cheatsheet

# View running containers
docker-compose ps

# Check MCP logs
docker-compose logs -f mcp

# Force rebuild
docker-compose up -d --force-recreate

# Full cleanup
docker-compose down -v && docker system prune -a

📝 Sample prometheus.yml

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'app1'
    metrics_path: '/metrics'
    static_configs:
      - targets: ['app1:5000']
  
  - job_name: 'app2'
    metrics_path: '/metrics'
    static_configs:
      - targets: ['app2:3000']

🤖 Testing Auto-Remediation

# Trigger test failure (50% error rate)
import requests
for _ in range(5):
    requests.get('http://localhost:5000/error')
    
# Verify remediation
docker-compose logs mcp | grep "Attempting remediation"

🗂️ Project Structure

ARM-CP/
├── apps/
│   ├── app1/               # Flask app
│   │   ├── app.py
│   │   └── requirements.txt
│   └── app2/               # Node.js app
│       ├── app.js
│       └── package.json
├── monitoring/
│   ├── prometheus.yml
│   ├── grafana/
│   └── logstash/
│       ├── app1.conf
│       └── app2.conf
├── mcp-server/
│   ├── app.py              # Control plane
│   └── requirements.txt
└── docker-compose.yml

💡 How the AI Integration Works

def analyze_incident(self, error_log):
    prompt = f"""
    Analyze this Docker container error:
    {error_log}
    
    Suggest remediation steps in JSON format with:
    - root_cause
    - steps[]
    - confidence_score
    """
    
    response = requests.post(
        self.deepseek_url,
        headers={"Authorization": f"Bearer {self.api_key}"},
        json={"model": "deepseek-chat", "messages": [{"role": "user", "content": prompt}]}
    )
    return response.json()

🆘 Support

For issues, please:

1. Check container logs

docker-compose logs --tail=100

2. Open a GitHub Issue
3. Include your docker-compose version output