MCP System Monitor

The MCP System Monitor is a lightweight, cross‑platform server that exposes live operating‑system metrics through the Model Context Protocol. By turning routine system statistics into MCP tools, it gives large‑language models instant visibility into the environment they run in—CPU load, memory pressure, disk health, network traffic, and even per‑process details. This capability is especially valuable for AI assistants that need to reason about performance, diagnose issues, or adapt their behavior based on resource availability.

At its core, the server offers a collection of high‑level tools that map directly to common monitoring tasks. A single call can retrieve CPU usage and core counts, while another fetches memory consumption or swap activity. Disk tools expose partition layout and I/O statistics; network tools list interfaces, connections, and traffic counters. Host information supplies uptime, boot time, and logged‑in users, and a process tool provides sorted listings with fine‑grained control over the number of entries or a specific PID. Each tool is designed to return concise, JSON‑serializable data that an LLM can ingest without additional parsing logic.

Developers integrating this server into AI workflows benefit from a unified, protocol‑driven interface that eliminates the need for custom shell scripts or third‑party monitoring agents. Because MCP is stateless and works over standard input/output, the monitor can run in containerized environments, on edge devices, or as a local daemon behind an LLM client. An assistant can simply invoke the appropriate tool, receive real‑time metrics, and use them to adjust response strategies—for example, throttling compute‑heavy operations when CPU usage is high or alerting users about disk space shortages.

Real‑world scenarios include automated system health checks in dev‑ops pipelines, self‑healing AI agents that pause or offload tasks when resources dwindle, and educational tools that demonstrate system internals to learners. The monitor’s ability to sort processes by CPU or memory also makes it useful for troubleshooting performance bottlenecks directly from the assistant’s interface.

What sets this MCP server apart is its minimal footprint and broad feature set. It requires no external dependencies beyond the Go runtime, runs out of the box on any platform that supports Go binaries, and adheres strictly to MCP standards. This makes it a plug‑and‑play component for any AI system that needs reliable, real‑time insight into the host environment.