A Go-based MCP server that exposes real-time system metrics (CPU, memory, disk, network, host & process data) to LLM clients.
https://github.com/seekrays/mcp-monitorStop switching between terminal windows, system monitors, and your AI chat when troubleshooting performance issues. MCP System Monitor bridges that gap by giving your LLM direct access to live system metrics.
You're deep in a debugging session. Your app is sluggish, but instead of asking your LLM to "help me troubleshoot performance" and then manually copying htop output, you can simply ask: "What processes are consuming the most CPU right now?" Your LLM gets real data, not outdated screenshots or manual copy-paste.
This isn't another monitoring dashboard – it's system observability integrated directly into your AI workflow.
Comprehensive System Access: CPU usage (per-core or aggregate), memory stats, disk I/O, network traffic, process listings, and host information. All queryable through natural language via your LLM.
Real-Time Data: No stale metrics or cached responses. When you ask about current system state, you get current system state.
Zero Configuration Overhead: Built in Go using gopsutil for cross-platform compatibility. No agents, no cloud dependencies, no complex setup. Clone, build, run.
Performance Debugging: "Show me the top 5 memory-consuming processes" or "What's the current CPU usage breakdown?" while staying in your AI conversation context.
Development Environment Monitoring: Track resource usage during builds, tests, or local development without leaving your coding flow.
Infrastructure Troubleshooting: Get instant system snapshots when investigating issues instead of switching between monitoring tools and AI assistance.
Automated Health Checks: Your LLM can proactively monitor system health and alert you to anomalies based on real metrics.
The server runs via stdio for direct MCP communication or HTTP mode for remote access. Six core tools handle everything from basic CPU stats to detailed process information with flexible sorting and filtering.
Want per-core CPU data? Set per_cpu: true. Need process info sorted by memory usage? Use sort_by: "memory". The API adapts to what you're investigating.
# Get it running in under a minute
git clone https://github.com/seekrays/mcp-monitor.git
cd mcp-monitor && make build
./mcp-monitor
Your LLM now has direct system access. Ask about performance bottlenecks, resource usage, or process behavior and get real answers backed by live data.
This transforms system monitoring from a manual, context-switching task into a natural part of your AI-assisted development workflow. Your debugging conversations become more productive because your LLM has the same system visibility you do.