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yamaru-eu

Yamaru Hardware Probe

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by yamaru-eu
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
```bash
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`):

```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

```bash
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.

TDQS

A3.5/5.0

Scored across 11 tools

Disambiguation3/5

Several tools overlap in purpose: analyze_ram_pressure, analyze_performance, and monitor_system_health all deal with CPU/memory metrics, causing potential confusion. Additionally, system specs are split across analyze_local_system and analyze_inference_config, adding ambiguity.

Naming Consistency3/5

Tool names mostly follow a verb_noun pattern but use a mix of verbs (analyze, check, get, diagnose, monitor) without uniform style. Some compound nouns are inconsistent in format, making the set moderately predictable.

Tool Count5/5

With 11 tools, the count is well within the 3-15 optimal range. Each tool serves a distinct purpose related to hardware probing and LLM inference, without being excessive.

Completeness3/5

The set covers hardware specs, storage health, thermal, performance, and LLM inference utilities, but lacks basic metrics like disk usage and network info. The inclusion of antivirus impact is niche, while GPU info is only available in inference context.

Maintenance

ActivityInactive
ResponsivenessNo issues