What makes this different from asking an LLM directly?
Instead of a generic “it depends on your configuration” response, the server lets the LLM read your actual CPU, RAM, VRAM, thermals, and storage health and give you a real, data-driven answer.
@yamaru-eu
Basic information
Yamaru Hardware Probe gives Claude Desktop and other MCP clients real visibility into your machine. 10 expert tools: full hardware inventory, real-time performance & thermal monitoring, RAM pressure analysis, SMART storage health, antivirus impact diagnostics, and LLM compatibili
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"yamaru-probe": {
"command": "npx",
"args": [
"-y",
"@yamaru-eu/hardware-probe"
]
}
}
}Reads the hardware specifications of the local machine: CPU, RAM, motherboard and OS. Returns a structured JSON object.
Provides real-time system performance: CPU load, memory usage, and top processes.
Performs a deep scan for LLM inference optimization: GPU details, real memory bandwidth benchmark, ML runtimes (Ollama, Docker, WSL), and environment variables.
Returns the expert instructions and rules for optimizing LLM inference on this specific hardware. Call this to learn HOW to interpret analyze_inference_config results.
(BETA) Checks if a specific LLM model can run on this machine. Returns optimal quantization and estimated tokens per second. Requires remote API connection.
(BETA) Recommends the best LLM models that can run locally on this machine. Requires remote API connection.
Reports current memory pressure: total/used/free/available memory, swap usage, and top processes by RSS.
Reports per-disk health: type (NVMe/SSD/HDD), vendor, temperature, SMART status, and firmware.
Reports current CPU and GPU thermal and frequency state: temperature, utilization, and fan speed.
Detects running antivirus/EDR products and reads their exclusion rules. Checks dev hot paths coverage.
Monitors system metrics (CPU load, RAM usage, CPU temperature) over a configurable duration (up to 10 minutes) and returns min/max/avg statistics. Use duration_seconds to set the observation window and interval_seconds to control sampling granularity.
Open-source MCP server that gives LLM clients real hardware diagnostics from the machine they run on. It exposes 10 expert tools covering CPU, RAM, VRAM, thermals, storage health, and more — so AI assistants provide grounded, data-driven answers instead of generic responses. Built by the Yamaru project.
Add the server configuration to your claude_desktop_config.json with the command npx -y @yamaru-eu/hardware-probe and restart Claude Desktop. The server then exposes 10 diagnostic tools queryable through any MCP client.
Instead of a generic “it depends on your configuration” response, the server lets the LLM read your actual CPU, RAM, VRAM, thermals, and storage health and give you a real, data-driven answer.
The server runs via npx with `@yamaru-eu/hardware-probe`. It is built on TypeScript, the MCP SDK, and the systeminformation library.
The server is privacy-first — it automatically anonymizes unique hardware IDs before any remote call.
The LLM compatibility prediction tools (`check_llm_compatibility`, `get_llm_recommendations`, `analyze_inference_config`) are marked as BETA.
The project is licensed under Apache 2.0.
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