Local Process Intelligence Server

This is a local, intelligent monitoring server built for the Model Context Protocol (MCP). It provides deep insight into your system and running processes, enabling a local LLM agent to analyze the machine’s state, perform diagnostics, suggest optimizations, and offer real-time solutions.

[!NOTE]

⚙️ Compatibility

This project was tested on the latest versions of macOS. While most features work reliably on this version, compatibility with other operating systems (Linux/Windows) is not always guaranteed.

What It Can Do

This project provides tools to:

• 🔧 Retrieve a full system summary (OS, CPU, RAM, Disk, Boot Time)
• 📊 Monitor CPU usage in real time
• 💾 Track RAM and swap memory usage
• 💽 Check disk usage (total, used, free, % used)
• 🧩 Analyze top processes sorted by CPU load
• 🔎 Search for running processes by name
• 🌲 Explore the process tree of a given PID
• ⚠️ Detect anomalies like:
• High memory + low CPU (e.g., memory leaks)
• Zombie/defunct processes
• Resource usage spikes above a threshold
• 🧠 Deliver actionable warnings and insights

This allows a local agent to not only observe, but also understand what’s happening on your machine — and provide real-time, specific solutions based on the actual system state.

🚀 Quick Start

📦 Installation

It is recommended using uv, so make sure you have uv installed. Generally, for more information regarding MCP checkout MCP Python SDK.

Clone this repo then:

uv sync

▶️ Running the Server / Adding to Claude Desktop Config

With uv:

uv run mcp install main.py

Or directly:

mcp install main.py

To test in development mode with logging:

mcp dev main.py

[!NOTE] 🧠 Note: When using Claude Desktop, ensure the app is properly restarted after installing a new server.

Find more information on MCP Quickstart

🚧 Future Work

The project lays the foundation for a powerful local system intelligence layer. In future versions, one could:

• Expand full cross-platform support
• Add background process logging
• Integrate energy usage and thermal monitoring
• Allow custom alert thresholds and scheduled diagnostics
• Let agents make automated corrective actions