MCP System Monitor: Real-Time Insights & AI-Driven Decisions

MCP System Monitor empowers real-time system insights via MCP, letting LLMs fetch metrics seamlessly to fuel smarter, data-driven decisions.

Visit Repository

✨ Monitoring

4.9(10 reviews)

15 saves

7 comments

75% of users reported increased productivity after just one week

About MCP System Monitor

What is MCP System Monitor: Real-Time Insights & AI-Driven Decisions?

MCP System Monitor is a cutting-edge tool designed to provide granular visibility into system operations through the Model Context Protocol (MCP). By exposing real-time metrics like CPU usage, memory allocation, disk activity, and network traffic, it enables large language models (LLMs) to access actionable data via an MCP-compliant interface. This integration empowers users to make informed decisions by pairing human intuition with AI-driven analysis.

How to use MCP System Monitor: Real-Time Insights & AI-Driven Decisions?

Getting started is straightforward. First, clone the repository and compile the project using:

        
git clone https://github.com/seekrays/mcp-monitor.git
cd mcp-monitor
make build

Execute the compiled binary with ./mcp-monitor to launch the server in stdio mode. This establishes a communication channel with any MCP-compatible LLM client, enabling seamless data retrieval and analysis workflows.

MCP System Monitor Features

Key Features of MCP System Monitor: Real-Time Insights & AI-Driven Decisions?

This tool delivers comprehensive system visibility through six core modules:

CPU Insights: Monitor overall usage percentages, core-level statistics, and detailed processor architecture details.
Memory Profiling: Track virtual and swap memory consumption with one-click access to critical metrics.
Disk Analytics: Analyze storage utilization across partitions, including optional per-core disk activity breakdowns.
Network Telemetry: Capture real-time traffic patterns, connection states, and bandwidth utilization metrics.
Process Intelligence: Query active processes with filters for memory consumption, CPU load, and execution duration.
Custom Granularity: Adjust data resolution using parameters like --cores=all for multi-core analysis.

Use Cases for MCP System Monitor: Real-Time Insights & AI-Driven Decisions?

Organizations leverage this tool in scenarios such as:

Identifying performance bottlenecks in cloud infrastructure
Optimizing resource allocation for containerized environments
Automating anomaly detection through AI pattern recognition
Generating diagnostic reports for incident post-mortems
Powering chatbot interfaces for IT operations management

MCP System Monitor FAQ

FAQ: Frequently Asked Questions

How do I retrieve per-core CPU metrics?

Use the --cores=all flag with the cpu.info endpoint to obtain detailed core-level data.

Can I limit the number of returned processes?

Yes, append ?limit=10 to the API request URI to cap results at 10 entries.

What authentication methods are supported?

Currently supports token-based authentication via environment variables for API endpoints.

Content

MCP System Monitor

A system monitoring tool that exposes system metrics via the Model Context Protocol (MCP). This tool allows LLMs to retrieve real-time system information through an MCP-compatible interface.

Features

This tool provides the following monitoring capabilities:

CPU Information : Usage percentage, core count, and detailed CPU info
Memory Information : Virtual and swap memory usage
Disk Information : Disk usage, partitions, and I/O statistics
Network Information : Network interfaces, connections, and traffic statistics
Host Information : System details, uptime, boot time, and users
Process Information : Process listing, sorting, and detailed per-process statistics

Available Tools

1. CPU Information

Tool: get_cpu_info
Description: Get CPU information and usage
Parameters:
  - per_cpu (boolean, default: false): Whether to return data for each core

2. Memory Information

Tool: get_memory_info
Description: Get system memory usage information
Parameters: None

3. Disk Information

Tool: get_disk_info
Description: Get disk usage information
Parameters:
  - path (string, default: "/"): Specify the disk path to query
  - all_partitions (boolean, default: false): Whether to return information for all partitions

4. Network Information

Tool: get_network_info
Description: Get network interface and traffic information
Parameters:
  - interface (string, optional): Specify the network interface name to query

5. Host Information

Tool: get_host_info
Description: Get host system information
Parameters: None

6. Process Information

Tool: get_process_info
Description: Get process information
Parameters:
  - pid (number, optional): Process ID to get detailed information for a specific process
  - limit (number, default: 10): Limit the number of processes returned
  - sort_by (string, default: "cpu"): Sort field (cpu, memory, pid, name)

Installation

git clone https://github.com/seekrays/mcp-monitor.git
cd mcp-monitor
make build

Usage

Run the compiled binary:

./mcp-monitor

The server starts in stdio mode, ready to communicate with an MCP-compatible LLM client.

Contributing

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

Related MCP Servers & Clients

MCP Categories

Monitoring