SystemSage

SystemSage is a versatile Model Context Protocol (MCP) server designed to give AI assistants instant, cross‑platform access to a machine’s full system stack. Rather than requiring the assistant to rely on generic text prompts or external APIs, SystemSage exposes a rich set of tools that let the model query live resource metrics, manipulate services, and perform diagnostic checks—all while maintaining a consistent MCP interface. This eliminates the need for developers to write custom adapters or shell scripts for each operating system, allowing a single prompt to work on Windows, macOS, and Linux with identical semantics.

At its core, SystemSage solves the problem of “black‑box” system insight. Developers can ask an AI, “Which services are consuming the most CPU?” or “Can you stop ?”, and receive a direct, actionable response. The server’s toolset covers the entire lifecycle of system health: from real‑time monitoring of CPU, memory, and disk usage to deeper diagnostics such as SMART status checks, security scans, and temporary file cleanup. By bundling these capabilities into a single MCP endpoint, SystemSage enables rapid troubleshooting and automated maintenance without exposing raw command‑line access to the user.

Key features include:

• Real‑time resource tracking: Continuously poll CPU, memory, disk, and network metrics with configurable duration windows.
• Process & service management: Locate processes by name or PID, start/stop/restart services, and toggle boot‑time settings across platforms.
• Container orchestration support: Manage Docker containers and, via optional cloud extras, Kubernetes clusters through unified commands.
• Security & health checks: Run comprehensive system audits that report on login attempts, open ports, and file‑system integrity.
• Environment introspection: Retrieve environment variables, startup programs, and network interface details for contextual decision‑making.

In practice, SystemSage shines in scenarios where an AI assistant must act as a system administrator: automating routine clean‑ups, monitoring production servers for anomalies, or guiding users through complex service diagnostics. Because the server is MCP‑compliant, it plugs seamlessly into any AI workflow that already consumes MCP tools—whether through Cursor Desktop, an LLM‑powered chatbot, or a custom orchestration layer. Its cross‑platform consistency and breadth of monitoring functions give developers a single, reliable interface to the underlying operating system, dramatically reducing boilerplate and accelerating time‑to‑value for AI‑augmented operations.