Manages GPU-accelerated Docker containers for AI workloads, including NVIDIA Container Toolkit integration and multi-architecture container support.
Supports deployment of lightweight Kubernetes clusters for edge orchestration and distributed AI workloads.
Provides edge deployment support for orchestrating AI workloads across Jetson devices.
Manages Linux system administration tasks including package management, updates, service control, and networking.
Manages NVIDIA Jetson hardware including power modes, CUDA toolkit, JetPack SDK, and thermal monitoring.
Supports installation and integration of OpenCV for computer vision applications on Jetson devices.
Manages installation and optimization of PyTorch for AI model deployment on Jetson hardware.
Handles TensorFlow installation, optimization, and model deployment for edge AI computing.
Supports deployment and optimization of YOLO models for object detection on Jetson Nano devices.
JetsonMCP
An MCP server that connects AI assistants to NVIDIA Jetson Nano Super systems for comprehensive edge computing management, AI workload optimization, and system administration.
JetsonMCP enables AI assistants like Claude to help configure and manage Jetson Nano systems through SSH connections. From AI model deployment to system optimization - ask questions in natural language instead of learning complex CUDA and Linux commands.
๐ What Makes JetsonMCP Special
Built for Edge AI Computing
CUDA toolkit management and optimization
JetPack SDK integration and updates
AI framework installation (TensorFlow, PyTorch, TensorRT)
Model deployment and inference optimization
Hardware-Specific Optimizations
GPU memory management and monitoring
Power mode configuration (10W/5W modes)
Temperature monitoring with thermal throttling
Fan curve management and cooling optimization
Container & Orchestration
Docker container management for AI workloads
NVIDIA Container Toolkit integration
Kubernetes edge deployment support
Multi-architecture container support (ARM64)
๐ฏ How It Works
Natural language requests are translated through Claude Desktop via MCP protocol to optimized commands executed on your Jetson Nano.
Examples:
AI & ML Operations:
"Deploy YOLOv5 model for object detection" - Downloads, optimizes, and runs inference
"Check CUDA memory usage" - Monitors GPU utilization and memory allocation
"Switch to 5W power mode" - Optimizes power consumption for battery operation
"Install TensorRT optimization" - Sets up high-performance inference engine
System Management:
"Monitor GPU temperature while running inference" - Real-time thermal monitoring
"Update JetPack to latest version" - Manages NVIDIA software stack updates
"Optimize Docker for AI workloads" - Configures runtime for GPU acceleration
Edge Computing:
"Deploy lightweight Kubernetes cluster" - Sets up K3s for edge orchestration
"Configure remote model serving" - Sets up inference endpoints
"Monitor system resources during AI tasks" - Performance profiling and optimization
๐ง Key Features
Edge AI Management
CUDA Toolkit Integration - Automatic CUDA environment setup and management
JetPack Management - SDK updates, component installation, and version control
AI Framework Support - TensorFlow, PyTorch, OpenCV, TensorRT optimization
Model Deployment - Automated model conversion, optimization, and serving
Hardware Optimization
Power Management - Dynamic power mode switching (10W/5W/MAXN)
Thermal Management - Temperature monitoring with automatic throttling
GPU Monitoring - Memory usage, utilization, and performance metrics
Fan Control - Custom fan curves and cooling optimization
Container Orchestration
NVIDIA Docker - GPU-accelerated container runtime management
Edge Kubernetes - K3s deployment for distributed AI workloads
Multi-arch Support - ARM64 container management and deployment
Registry Management - Private registry setup for edge deployments
System Administration
Remote Management - SSH-based secure system administration
Package Management - APT and snap package installation/updates
Service Management - Systemd service control and monitoring
Backup & Recovery - System state management and restoration
๐ Prerequisites
Jetson Nano Setup
Fresh JetPack Installation (4.6+ recommended)
SSH Access Enabled
Adequate Power Supply (5V/4A recommended for full performance)
MicroSD Card (64GB+ recommended) or NVMe SSD
Internet Connectivity for package installation
Network Configuration
Static IP recommended for consistent access
Firewall configured to allow SSH (port 22)
Optional: VPN setup for remote access
โก Quick Start
1. Prepare Your Jetson Nano
2. Install JetsonMCP
3. Configure Connection
Required .env
settings:
4. Claude Desktop Integration
Add to your Claude Desktop configuration:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json
Restart Claude Desktop to load the server.
๐ ๏ธ Available Tools
AI & ML Management
manage_ai_workloads
- Model deployment, inference optimization, CUDA managementmanage_jetpack
- JetPack SDK installation, updates, component managementmanage_frameworks
- TensorFlow, PyTorch, OpenCV, TensorRT installation
Hardware Control
manage_hardware
- Power modes, temperature monitoring, fan control, GPIOmanage_performance
- CPU/GPU governors, frequency scaling, thermal managementmanage_storage
- SSD optimization, swap configuration, disk management
Container Operations
manage_containers
- Docker management, NVIDIA runtime, GPU accelerationmanage_orchestration
- Kubernetes/K3s deployment, edge computing setupmanage_registry
- Private registry setup, multi-arch image management
System Administration
manage_system
- Package management, updates, service control, networkingmanage_security
- Firewall, SSH keys, user management, system hardeningmanage_monitoring
- System metrics, logging, alerting, remote monitoring
๐ง Advanced Configuration
Power Management
CUDA Environment
Docker GPU Support
๐งช Testing & Development
Run Tests
Development Setup
๐ Monitoring & Observability
System Metrics
CPU/GPU Utilization - Real-time performance monitoring
Memory Usage - RAM and GPU memory tracking
Temperature Sensors - Thermal monitoring and alerts
Power Consumption - Current power mode and usage metrics
AI Workload Metrics
Inference Latency - Model performance benchmarking
Throughput - Requests per second for deployed models
Resource Utilization - GPU memory and compute efficiency
Model Accuracy - Performance validation and monitoring
๐ Security Features
SSH Security
Host key verification and rotation
Connection timeouts and retry logic
Credential management and cleanup
Audit logging for all operations
Container Security
Image vulnerability scanning
Runtime security policies
Network isolation and segmentation
Secrets management for AI models
System Hardening
Firewall configuration management
User privilege separation
System update automation
Security patch monitoring
๐ Use Cases
Edge AI Development
Rapid prototyping of AI applications
Model optimization and benchmarking
Distributed inference deployment
Real-time computer vision applications
IoT & Sensor Networks
Sensor data processing and analysis
Edge computing orchestration
Remote device management
Predictive maintenance systems
Industrial Applications
Quality control and inspection
Predictive analytics
Autonomous systems development
Industrial IoT integration
๐ค Contributing
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
Development Priorities
AI Framework Integration - Support for additional ML frameworks
Edge Orchestration - Advanced Kubernetes edge deployment
Hardware Abstraction - Support for other Jetson platforms (AGX, Xavier)
Monitoring Enhancement - Advanced telemetry and observability
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Connects AI assistants to NVIDIA Jetson Nano systems for edge computing management, enabling natural language control of AI workloads, hardware optimization, and system administration tasks.
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