MCP System Monitor Server

A cross-platform server for real-time monitoring of CPU, GPU, memory, disk, network, and process information.

A cross-platform MCP (Model Context Protocol) server that provides comprehensive real-time system monitoring capabilities for LLMs. Built with FastMCP for easy integration with Claude Desktop and other MCP-compatible clients.

- CPU Monitoring: Real-time usage, per-core statistics, frequency, temperature, detailed processor information (model, vendor, architecture, cache sizes)
- GPU Monitoring: Multi-vendor GPU support (NVIDIA with full metrics, Apple Silicon with comprehensive support including unified memory and core count, AMD/Intel with basic info)
- Memory Monitoring: RAM and swap usage, availability statistics
- Disk Monitoring: Space usage, filesystem information for all mounted drives
- Network Statistics: Interface-level traffic and error counters
- Process Monitoring: Top processes by CPU/memory usage
- System Information: OS details, hostname, uptime, architecture

- I/O Performance: Detailed disk I/O metrics, read/write rates, per-disk statistics, busy time analysis
- System Load: Load averages (1m, 5m, 15m), context switches, interrupts, running/blocked processes
- Enhanced Memory: Detailed memory statistics including buffers, cache, active/inactive memory, page faults, swap activity
- Enhanced Network: Network performance metrics with transfer rates, errors, drops, interface speed and MTU

- get_current_datetime: Get the current local datetime in ISO format
- get_cpu_info: Get current CPU usage and statistics
- get_gpu_info: Get GPU information for all detected GPUs
- get_memory_info: Get RAM and swap usage
- get_disk_info: Get disk usage for all mounted drives
- get_system_snapshot: Get complete system state in one call
- monitor_cpu_usage: Monitor CPU usage over a specified duration
- get_top_processes: Get top processes by CPU or memory usage
- get_network_stats: Get network interface statistics

Phase 1 Performance Monitoring (6 tools):

- get_io_performance: Get detailed I/O performance metrics and rates
- get_system_load: Get system load averages and process statistics
- get_enhanced_memory_info: Get detailed memory statistics with caches/buffers
- get_enhanced_network_stats: Get enhanced network performance metrics
- get_performance_snapshot: Get complete performance monitoring snapshot
- monitor_io_performance: Monitor I/O performance over specified duration with trend analysis

- system://live/cpu: Live CPU usage data
- system://live/memory: Live memory usage data
- system://config: System configuration and hardware information

Phase 1 Performance Resources (3 resources):

- system://performance/io: Live I/O performance data
- system://performance/load: Live system load data
- system://performance/network: Live network performance data

- Full metrics: usage percentage, memory (used/total), temperature, power consumption
- Supports multiple NVIDIA GPUs
- Requires NVIDIA drivers and NVML libraries

- Comprehensive support for M1, M2, and M3 chips
- GPU core count detection
- Unified memory reporting (shares system RAM)
- Metal API support detection
- Temperature monitoring (when available)

- Basic detection and identification
- Limited metrics depending on platform and drivers

- Python 3.10+
- Windows, macOS, or Linux
- GPU (optional): NVIDIA GPUs for full metrics, Apple Silicon GPUs fully supported on macOS

git clone https://github.com/huhabla/mcp-system-monitor.git cd mcp-system-monitor

Install dependencies using uv (recommended):

uv run mcp dev mcp_system_monitor_server.py

uv run mcp install mcp_system_monitor_server.py --name "System Monitor"

Modify the following JSON template to set the path to the MCP server in your MCP client for Windows:

{ "mcpServers": { "mpc-system-monitor": { "command": "cmd", "args": [ "/c", "C:/Users/Sören Gebbert/Documents/GitHub/mcp-system-monitor/start_mpc_system_monitor.bat" ] } } }

Modify the following JSON template to set the path to the MCP server in your MCP client for MacOS:

{ "mcpServers": { "mpc-system-monitor": { "command": "/bin/zsh", "args": [ "/Users/holistech/Documents/GitHub/mcp-system-monitor/start_mcp_system_monitor.sh" ] } } }

Once connected to Claude Desktop or another MCP client, you can use natural language to interact with the system monitor:

- "Show me the current CPU usage"
- "What's my GPU temperature?"
- "How many GPU cores does my Apple M1 Max have?"
- "Show me GPU memory usage and whether it's unified memory"
- "How much disk space is available?"
- "Monitor CPU usage for the next 10 seconds"
- "Show me the top 5 processes by memory usage"
- "Get a complete system snapshot"

- "Show me detailed I/O performance metrics"
- "What's the current system load average?"
- "Monitor I/O performance for the next 30 seconds"
- "Show me enhanced memory statistics with cache information"
- "Get detailed network performance metrics"
- "Give me a complete performance snapshot"

The server uses a modular collector-based architecture:

- BaseCollector: Abstract base class providing caching and async data collection
- Specialized Collectors: CPU, GPU, Memory, Disk, Network, Process, and System collectors
- Phase 1 Performance Collectors: IOPerformance, SystemLoad, EnhancedMemory, and EnhancedNetwork collectors
- Pydantic Models: Type-safe data models for all system information
- FastMCP Integration: Simple decorators for exposing tools and resources

All collectors implement intelligent caching to:

- Reduce system overhead from frequent polling
- Provide consistent data within time windows
- Allow configurable cache expiration

The project includes a comprehensive test suite with 100% coverage of all MCP tools, resources, and collectors:

- test_mcp_system_monitor_server.py- Original basic collector tests
- test_mcp_system_monitor_server_comprehensive.py- Comprehensive MCP tools/resources tests
- test_mcp_server_integration.py- Integration tests for MCP server protocol compliance
- test_architecture_agnostic.py- Cross-platform tests focusing on data contracts
- conftest.py- Test configuration, fixtures, and mocking utilities

pytest -m unit # Fast unit tests only pytest -m integration # Integration tests only pytest -m agnostic # Architecture/OS agnostic tests pytest -m "not slow" # Exclude slow tests pytest -m "unit and not slow" # Fast unit tests for CI

pytest tests/test_mcp_system_monitor_server_comprehensive.py # All MCP endpoints pytest tests/test_mcp_server_integration.py # Integration tests pytest tests/test_architecture_agnostic.py # Cross-platform tests

pytest --cov=mcp_system_monitor_server --cov-report=html

- 15 MCP Tools(9 basic + 6 Phase 1 performance)
- 6 MCP Resources(3 basic + 3 Phase 1 performance)
- 11 Collectors(7 basic + 4 Phase 1 performance)
- Cross-platform compatibilitytesting
- Performance benchmarkingand stress testing
- Error handlingand edge case validation

- System snapshot collection: < 5 seconds
- Individual tool calls: < 1 second each
- Concurrent operations: 20 parallel calls < 10 seconds

⚠️ = Limited support, depends on hardware/drivers

- Ensure NVIDIA drivers are installed
- Check ifnvidia-smicommand works
- The server will gracefully handle missing GPU libraries

- Supported on macOS with M1, M2, and M3 chips
- Provides comprehensive information including unified memory and GPU core count
- Usessystem_profilercommand (available by default on macOS)

- Some system information may require elevated privileges
- The server handles permission errors gracefully and skips inaccessible resources

- Adjust the monitoring frequency by modifying collector update intervals
- Use cached data methods to reduce system calls
- Default cache expiration is 2 seconds for most collectors
- Consider increasingmax_ageparameter inget_cached_data()calls for less frequent updates

- The server uses intelligent caching to minimize system calls
- Each collector maintains its own cache with configurable expiration
- Continuous monitoring tools (likemonitor_cpu_usage) bypass caching for real-time data
- For high-frequency polling, consider using the resource endpoints which leverage caching

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.

- Built withFastMCP
- System monitoring viapsutil
- NVIDIA GPU support vianvidia-ml-py

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