GPU Server

mcp-name: io.github.moezdil/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 controller busy % 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. The nvidia-smi query does not include the CUDA version, so cuda is null on that path.

Install

pip install gpu-server

The package moved from mcp-gpu-server to gpu-server on PyPI in 0.2.1. The old name is frozen at 0.1.3, which does not start with mcp 2.x. The mcp-gpu-server command still works, and since 0.2.2 gpu-server does too.

Listed on the official MCP Registry as io.github.moezdil/mcp-gpu-server.

Connect to your MCP client

Add this to your MCP client config file:

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

Or without installing anything, using uv:

{ "mcpServers": { "gpu": { "command": "uvx", "args": ["gpu-server"] } } }

Run tests

uv run pytest tests/test_server.py # no GPU needed uv run python tests/test_gpu.py # on a GPU host

Tested on real hardware

Release 0.2.2 was installed from PyPI into a clean virtual environment on a Linux host and tested against a real GPU on 2026-10-21.

Install the published package and run the GPU integration test:

python3 -m venv venv venv/bin/pip install gpu-server==0.2.2 git clone --depth 1 https://github.com/moezdil/mcp-gpu-server.git repo venv/bin/python repo/tests/test_gpu.py

Result: all 37 checks passed. They exercise the NVML path; the nvidia-smi fallback is only checked for presence, not parsed.

Then call every tool through a real MCP client over stdio:

import anyio, json, sys from mcp import Client, StdioServerParameters

async def main(): async with Client(StdioServerParameters(command=sys.argv[1])) as c: print("protocol:", c.protocol_version) for t in (await c.list_tools()).tools: r = await c.call_tool(t.name, {}) print(t.name, t.annotations.read_only_hint, json.dumps(r.structured_content))

anyio.run(main)

Saved as e2e.py and run with venv/bin/python e2e.py venv/bin/gpu-server. Result: the client and server negotiated protocol 2026-07-28, all five tools are listed as read-only, and every call returned structured content without errors:

gpu_stats {"count": 1, "gpus": [{"index": 0, "name": "NVIDIA GeForce RTX 3090", "driver": "580.178.04", "cuda": "13.0", "temp_c": 42, "gpu_pct": 0, "mem_pct": 0, "vram": {"total_mib": 24576, "used_mib": 451, "free_mib": 24124, "pct": 1.8}}]}

Requirements

Python 3.10 or higher, MCP Python SDK 2.x. NVIDIA GPU with drivers installed on the host machine.

Links

• PyPI: https://pypi.org/project/gpu-server/
• MCP Registry entry: https://registry.modelcontextprotocol.io/v0/servers?search=io.github.moezdil/mcp-gpu-server
• Model Context Protocol: https://modelcontextprotocol.io
• MCP Python SDK: https://github.com/modelcontextprotocol/python-sdk
• uv: https://docs.astral.sh/uv/
• NVML (nvidia-ml-py): https://pypi.org/project/nvidia-ml-py/