MCP Monitor, developed by Seekrays for the mcp platform, functions as an MCP server. Built primarily with Go, this free tool enables Large Language Models to access real-time system information. It connects to the underlying operating system to expose vital metrics through the Model Context Protocol. Distribution occurs mainly via its GitHub repository, with Docker images also suggested as a method. MCP Monitor aims to bridge the gap between AI agents and live system data.

What does MCP Monitor connect to?

MCP Monitor is a system monitoring tool that establishes connections to the underlying operating system. Its primary function is to expose system metrics using the Model Context Protocol (MCP). Categorized as an MCP server, it is specifically engineered to allow Large Language Models (LLMs) to retrieve current system information. This is achieved through an MCP-compatible interface, making system data accessible for AI agent interactions and analysis.

Core features and tools

MCP Monitor exposes several callable tools to retrieve system data. These include: get_cpu_info for CPU Information, get_memory_info for Memory Information, get_disk_info for Disk Information, get_network_info for Network Information, get_host_info for Host Information, and get_process_info for Process Information. Each tool provides specific real-time insights into different aspects of the system, enabling detailed monitoring and data retrieval.

Who is it for?

MCP Monitor serves LLMs requiring real-time system information, as well as developers and authorized security testers reviewing AI-agent integrations. SOC teams also benefit during triage of AI-agent integrations. It is designed for compatibility with any MCP-compatible LLM client, including those generally discussed in broader MCP contexts like Claude Desktop and VS Code. Skip this if you do not need to expose system metrics to an MCP-compatible LLM client.

How to get started

No credentials are required. MCP Monitor supports the stdio transport protocol, starting in stdio mode to communicate with an MCP-compatible LLM client.

Use cases

• LLMs querying for system metrics such as CPU, memory, disk, network, host, and process information.
• Developers and security testers observing and troubleshooting Model Context Protocol sessions, including initialization, tool discovery, tool calls, responses, and errors.
• SOC teams using it during triage of an AI-agent integration to understand advertised tools, calls made, and identify errors or suspicious data flow.

MCP Monitor provides a direct bridge for LLMs and other stakeholders to access real-time system metrics via the Model Context Protocol. Its Go-based architecture and stdio transport support make it a focused solution for system monitoring within AI-agent ecosystems. It offers specific tools for CPU, memory, disk, network, host, and process data.