Hardware Probe

Yamaru Hardware Probe (MCP)

[NPM Version] [NPM Downloads] [MCP Registry] [Awesome MCP] [PRs Welcome] [License]

Expert system hardware probe and performance diagnostic engine for AI, Gaming, and High-Performance workflows. This is a Model Context Protocol (MCP) server that provides deep system insights beyond simple specifications.

Key Features

• 🔍 Deep Hardware Inventory: Comprehensive analysis of CPU, RAM, GPU (VRAM/Bandwidth), Storage, and OS topology.
• ⚡ Real-time Performance Monitoring: Live tracking of system load and identification of resource-hogging processes.
• 🧊 Thermal & Power Diagnostics: Detects thermal throttling and frequency clipping to resolve unexpected slowness.
• 🤖 AI/LLM Optimization: Specialized tools for predicting LLM performance, calculating quantization fit, and optimizing runtimes (Ollama, CUDA, Metal).
• 🛡️ Privacy-First: Automatic anonymization of unique hardware identifiers before any remote transmission.

Installation

For Gemini CLI Users

gemini extension install @yamaru-eu/hardware-probe

For Manual MCP Setup

Add this to your MCP settings file (e.g., npx-config.json or claude_desktop_config.json):

{  "mcpServers": {    "yamaru-probe": {      "command": "npx",      "args": ["-y", "@yamaru-eu/hardware-probe"]    }  }}

Available Tools

• analyze_local_system: Full hardware inventory.
• analyze_performance: Real-time performance metrics and top processes.
• analyze_ram_pressure: Detailed memory pressure and RSS analysis for deep RAM troubleshooting.
• check_storage_health: Disk SMART health, firmware, and I/O bottleneck analysis.
• thermal_profile: Real-time CPU/GPU thermal states, fan speeds, and frequency throttling detection.
• diagnose_antivirus_impact: Detects EDR/Antivirus conflicts and exclusion coverage on dev paths.
• monitor_system_health: Statistical health report (CPU load, RAM usage, temperature) with min/max/avg over a configurable time window (up to 10 minutes).
• check_llm_compatibility (BETA): Predicts performance for a specific LLM model via remote API.
• get_llm_recommendations (BETA): Recommends the best local models via remote API.
• analyze_inference_config: Deep-dive into AI runtimes and environment variables.

Skills Integration

When used with Gemini CLI, this extension provides the following expert skills:

• hardware-performance-expert: Global protocol for system health and troubleshooting.
• local-inference-optimizer: Specialized logic for fine-tuning local LLM runs.

Development

npm install        # Install dependenciesnpm run build      # Compile TypeScript → dist/npm run test       # Run test suitenpm run inspector  # Test tools in the MCP Inspector

License

Apache 2.0 - Part of the Yamaru Project.