mcp-name: io.github.mesutoezdil/mcp-gpu-server

mcp-gpu-server

An MCP server that exposes NVIDIA GPU metrics as tools. Once connected, any MCP-compatible client can query your GPU status in real time directly from a conversation.

What it does

Instead of running nvidia-smi manually, you ask your AI assistant and it calls these tools automatically:

gpu_info GPU name, driver version, CUDA version gpu_utilization core utilization % and memory bandwidth % gpu_vram total, used, free VRAM in MiB and usage % gpu_temperature GPU core temperature in Celsius gpu_stats everything above in one call

Example response from gpu_stats:

{ "count": 1, "gpus": [{ "index": 0, "name": "NVIDIA L40S", "driver": "580.126.09", "cuda": "13.0", "temp_c": 29, "gpu_pct": 0, "mem_pct": 0, "vram": { "total_mib": 46068, "used_mib": 610, "free_mib": 45457, "pct": 1.3 } }] }

How it works

Queries NVML (pynvml) directly when available. Falls back to nvidia-smi subprocess if NVML is not accessible. Returns clean JSON in both cases.

Install

pip install mcp-gpu-server

Connect to your MCP client

Add this to your MCP client config file:

{ "mcpServers": { "gpu": { "command": "mcp-gpu-server" } } }

Run tests

python tests/test_gpu.py

Requirements

Python 3.10 or higher. NVIDIA GPU with drivers installed on the host machine.