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gpu-mcp-server

mcp
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SUMMARY

MCP server for NVIDIA GPU metrics give AI agents real-time access to GPU utilization, memory, temperature, and power

README.md

gpu-mcp-server

An MCP server that exposes NVIDIA GPU metrics as tools.
Any MCP-compatible AI agent (Claude, Goose, Cursor, etc.) can query real-time GPU
utilization, memory, temperature, power, PCIe and NVLink throughput no Prometheus
or dcgm-exporter required.

Built on the official Go MCP SDK
and NVIDIA go-nvml.

Tools

Tool Description
list_gpus List all GPUs with utilization and memory info
get_gpu_metrics Detailed metrics for a GPU by index or UUID
gpu_summary Aggregate stats across all devices

All tools support MIG (Multi-Instance GPU) — MIG instances appear as separate
devices with their parent GPU's shared metrics (temperature, power, PCIe).

Quick start

# build (requires CGO + NVML headers on Linux)
make build

# run the server communicates over stdio
./gpu-mcp-server

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "gpu": {
      "command": "/path/to/gpu-mcp-server"
    }
  }
}

Goose

extensions:
  gpu-metrics:
    type: stdio
    cmd: /path/to/gpu-mcp-server

Build

Requires Go 1.23+, CGO, and NVIDIA drivers on the target machine.

make build       # compile binary
make test        # run tests (no GPU needed uses mock)
make lint        # golangci-lint
make docker      # container image

Tests use a mock collector, so they run anywhere no GPU hardware required.

Architecture

Agent (Claude/Goose) ─── MCP (stdio) ──→ gpu-mcp-server ──→ NVML ──→ GPU
                                              │
                                         Tools:
                                         • list_gpus
                                         • get_gpu_metrics
                                         • gpu_summary

The server runs as a local process alongside the agent. It calls NVML directly
through cgo — no sidecar, no network hops, no metric pipeline to configure.

Project info

  • License: Apache 2.0
  • Language: Go
  • AAIF project alignment: MCP
  • Related: keda-gpu-scaler (GPU autoscaling for Kubernetes)

Roadmap

See ROADMAP.md for the 12-month public roadmap.

Contributing

See CONTRIBUTING.md for how to get involved.

Governance

This project follows Linux Foundation Minimum Viable Governance.

Documentation

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