ops-mcp-server
ops-mcp-server: an AI-driven IT operations platform that fuses LLMs and MCP architecture to enable intelligent monitoring, anomaly detection, and natural human-infrastructure interaction with enterprise-grade security and scalability.
📖 Table of Contents
🚀 Project Overview
ops-mcp-server is an IT operations management solution for the AI era. It achieves intelligent IT operations through the seamless integration of the Model Context Protocol (MCP) and Large Language Models (LLMs). By leveraging the power of LLMs and MCP's distributed architecture, it transforms traditional IT operations into an AI-driven experience, enabling automated server monitoring, intelligent anomaly detection, and context-aware troubleshooting. The system acts as a bridge between human operators and complex IT infrastructure, providing natural language interaction for tasks ranging from routine maintenance to complex problem diagnosis, while maintaining enterprise-grade security and scalability.
🌟 Key Features
🖥️ Server Monitoring
- Real-time CPU, memory, disk inspections.
- System load and process monitoring.
- Service and network interface checks.
- Log analysis and configuration backup.
- Security vulnerability scans (SSH login, firewall status).
- Detailed OS information retrieval.
📦 Container Management (Docker)
- Container, image, and volume management.
- Container resource usage monitoring.
- Log retrieval and health checks.
🌐 Network Device Management
- Multi-vendor support (Cisco, Huawei, H3C).
- Switch port, VLAN, and router route checks.
- ACL security configuration analysis.
- Optical module and device performance monitoring.
➕ Additional Capabilities
- Extensible plugin architecture.
- Batch operations across multiple devices.
- Tool listing and descriptive commands.
🎬 Demo Videos
📌 Project Demo
On Cherry Studio
!Demo Animation
📌 Interactive Client Demo
On Terminal
!Client Demo Animation
⚙️ Installation
Ensure you have Python 3.10+ installed. This project uses uv for dependency and environment management.
1. Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
2. Set Up Virtual Environment
uv venv .venv
# Activate the environment
source .venv/bin/activate # Linux/macOS
.\.venv\Scripts\activate # Windows
3. Install Dependencies
uv pip install -r requirements.txt
Dependencies are managed via pyproject.toml.
🚧 Deployment
📡 SSE Remote Deployment (UV)
cd server_monitor_sse
# Install dependencies
pip install -r requirements.txt
# Start service
cd ..
uv run server_monitor_sse --transport sse --port 8000
🐳 SSE Remote Deployment (Docker Compose)
Ensure Docker and Docker Compose are installed.
cd server_monitor_sse
docker compose up -d
# Check status
docker compose ps
# Logs monitoring
docker compose logs -f
🛠️ Local MCP Server Configuration (Stdio)
Add this configuration to your MCP settings:
{
"ops-mcp-server": {
"command": "uv",
"args": [
"--directory", "YOUR_PROJECT_PATH_HERE",
"run", "server_monitor.py"
],
"env": {},
"disabled": true,
"autoApprove": ["list_available_tools"]
},
"network_tools": {
"command": "uv",
"args": [
"--directory", "YOUR_PROJECT_PATH_HERE",
"run", "network_tools.py"
],
"env": {},
"disabled": false,
"autoApprove": []
},
}
Note: Replace YOUR_PROJECT_PATH_HERE with your project's actual path.
💬 Interactive Client Usage
An interactive client (client.py) allows you to interact with MCP services using natural language.
1. Install Client Dependencies
uv pip install openai rich
2. Configure Client
Edit these configurations within client.py:
# Initialize OpenAI client
self.client = AsyncOpenAI(
base_url="https://your-api-endpoint",
api_key="YOUR_API_KEY"
)
# Set model
self.model = "your-preferred-model"
3. Run the Client
uv run client.py [path/to/server.py]
Example:
uv run client.py ./server_monitor.py
Client Commands
help - Display help.quit - Exit client.clear - Clear conversation history.model - Switch models.
📄 License
This project is licensed under the MIT License.
📌 Notes
- Ensure remote SSH access is properly configured.
- Adjust tool parameters based on actual deployment conditions.
- This project is under active development; feedback and contributions are welcome.
