vibops-mcp 0.1.0

VibOps MCP server — control your GPU infrastructure from Claude Desktop, Cursor, or any MCP client

vibops-mcp

Control your GPU infrastructure with Claude, Cursor, or any MCP-compatible AI assistant.

What it does

vibops-mcp exposes 26 tools that let an AI assistant:

• Observe — GPU utilisation, workload breakdown, MTTR, cost estimates, live K8s deployments
• Act — deploy models, scale clusters, run Helm/kubectl, trigger pipelines
• Configure — set cost rates, manage gateways, store secrets

All operations go through your VibOps instance and are recorded in the audit log.

Installation

pip install vibops-mcp

Configuration

You need two environment variables:

Variable	Description
VIBOPS_URL	Base URL of your VibOps instance, e.g. https://vibops.example.com
VIBOPS_TOKEN	API token — create one in VibOps → Settings → API Tokens

Claude Desktop

Add to ~/.config/claude/claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "vibops": {
      "command": "vibops-mcp",
      "env": {
        "VIBOPS_URL": "https://vibops.example.com",
        "VIBOPS_TOKEN": "your-token-here"
      }
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project root, or to the global config:

{
  "mcpServers": {
    "vibops": {
      "command": "vibops-mcp",
      "env": {
        "VIBOPS_URL": "https://vibops.example.com",
        "VIBOPS_TOKEN": "your-token-here"
      }
    }
  }
}

Claude Code (CLI)

claude mcp add vibops vibops-mcp \
  -e VIBOPS_URL=https://vibops.example.com \
  -e VIBOPS_TOKEN=your-token-here

Available tools

Observation (read-only)

Tool	Description
list_clusters	List clusters and GPU utilisation
list_kubectl_contexts	List available kubectl contexts
get_cluster_deployments	Live K8s deployment status for a cluster
get_cluster_rate	Get configured GPU cost rate for a cluster
list_jobs	List recent jobs with optional filters
get_job	Get job details and result
get_gpu_metrics	Hourly GPU utilisation time-series
get_workload_breakdown	Job count by workload type
get_mttr	Mean Time To Resolve GPU alerts
get_cost_estimate	Estimated GPU spend
list_gateways	List registered gateways and status
list_alerts	List GPU alerts (open or resolved)
list_secrets	List secrets (names only, never values)
list_providers	List configured AI/GPU cloud providers
list_pipelines	List automation pipelines

Actions (write)

Tool	Description
scale_cluster	Scale a K8s deployment or node pool
deploy_model	Deploy an AI model onto a GPU cluster
helm_upgrade	Run helm upgrade --install
helm_uninstall	Uninstall a Helm release
run_kubectl	Run an arbitrary kubectl command
git_clone	Clone a git repository
create_secret	Store an encrypted secret
trigger_pipeline	Manually trigger an automation pipeline

Configuration

Tool	Description
set_cluster_rate	Set GPU cost rate for a cluster (admin only)
register_gateway	Register a new gateway (returns one-time token)
delete_gateway	Revoke a gateway

Example prompts

"What's our GPU utilisation trend over the last 7 days?"
"Show me the cost breakdown per cluster this week."
"Deploy llama3:8b on vibops-dev with 2 replicas."
"Which clusters have open critical GPU alerts?"
"Scale the inference namespace to 4 replicas on prod-cluster."
"What's our MTTR for critical alerts?"

License

MIT — free to use, modify, and distribute. See LICENSE.

Built on FastMCP and the VibOps platform.