Gpu Mcp Server

gpu-mcp-server

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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 | | get_gpu_processes | PID-level GPU process attribution | | 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).

Sample output

Each tool returns structured JSON. The examples below show the shape of the data an agent receives from a node with two NVIDIA A100 GPUs.

list_gpus:

{
  "count": 2,
  "devices": [
    {
      "index": 0,
      "uuid": "GPU-aaaa-1111",
      "name": "NVIDIA A100-SXM4-80GB",
      "gpu_utilization_percent": 85,
      "memory_used_mib": 57344,
      "memory_total_mib": 81920
    },
    {
      "index": 1,
      "uuid": "GPU-bbbb-2222",
      "name": "NVIDIA A100-SXM4-80GB",
      "gpu_utilization_percent": 20,
      "memory_used_mib": 12288,
      "memory_total_mib": 81920
    }
  ]
}

get_gpu_metrics (with {"index": 0} or {"uuid": "GPU-aaaa-1111"}):

{
  "index": 0,
  "uuid": "GPU-aaaa-1111",
  "name": "NVIDIA A100-SXM4-80GB",
  "gpu_utilization_percent": 85,
  "memory_utilization_percent": 70,
  "memory_used_mib": 57344,
  "memory_total_mib": 81920,
  "temperature_celsius": 72,
  "power_draw_watts": 300,
  "power_limit_watts": 400,
  "pcie_tx_kbps": 0,
  "pcie_rx_kbps": 0,
  "nvlink_tx_mbps": 0,
  "nvlink_rx_mbps": 0
}

gpu_summary:

{
  "device_count": 2,
  "avg_gpu_utilization": 52.5,
  "avg_memory_utilization": 42.5,
  "total_memory_used_mib": 69632,
  "total_memory_total_mib": 163840,
  "max_temperature_celsius": 72,
  "total_power_draw_watts": 375
}

MIG instances add is_mig, parent_gpu, and mig_profile fields to the get_gpu_metrics and list_gpus payloads.

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

Cursor

Add to .cursor/mcp.json for a project, or ~/.cursor/mcp.json for all projects:

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

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "gpu": {
      "command": "/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](ROADMAP.md) for the 12-month public roadmap.

Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md) for how to get involved.

Contributors

Thanks to all our [contributors](CONTRIBUTORS.md)! Add yourself via PR.

Governance

This project follows [Linux Foundation Minimum Viable Governance](GOVERNANCE.md).

Documentation

• [ROADMAP.md](ROADMAP.md) - public roadmap
• [GOVERNANCE.md](GOVERNANCE.md) - decision-making process
• [DEPENDENCIES.md](DEPENDENCIES.md) - external dependencies and licenses
• [SECURITY.md](SECURITY.md) - vulnerability reporting
• [AGENTS.md](AGENTS.md) - instructions for AI agents working on this repo
• [CODEOFCONDUCT.md](CODEOFCONDUCT.md) - community standards

Star History

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: pmady
• Source: pmady/gpu-mcp-server
• License: Apache-2.0
• Homepage: https://dev.to/pavan_madduri/give-your-ai-agent-eyes-on-gpus-introducing-gpu-mcp-server-54d4

Install and usage instructions live in the source repository linked above.