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hardware-probe

mcp
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SUMMARY

Expert hardware probe for AI, Gaming, and HPC. Analyzes GPU, VRAM, and Memory Bandwidth to optimize local LLM inference (Ollama) and provide actionable hardware upgrade recommendations.

README.md

Yamaru Hardware Probe (MCP)

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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 dependencies
npm run build      # Compile TypeScript → dist/
npm run test       # Run test suite
npm run inspector  # Test tools in the MCP Inspector

License

Apache 2.0 - Part of the Yamaru Project.

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