About Vibops MCP
Vibops MCP is an MCP server in the Cloud category: the AI Infrastructure Operating System for enterprise and Cloud Service Provider GPU environments.
Install
git clone https://github.com/VibOpsai/vibops-mcpThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Tools (16)
get_cluster_deploymentsLive K8s deployment status for a cluster
Parameters not published.
get_cluster_rateGet configured GPU cost rate for a cluster
Parameters not published.
get_cost_estimateEstimated GPU spend
Parameters not published.
get_gpu_metricsHourly GPU utilisation time-series
Parameters not published.
get_jobGet job details and result
Parameters not published.
get_job_metricsJob success rate, latency P50/P95/P99, error breakdown
Parameters not published.
get_mttrMean Time To Resolve GPU alerts
Parameters not published.
get_workload_breakdownJob count by workload type
Parameters not published.
list_alertsList GPU alerts (open or resolved)
Parameters not published.
list_clustersList clusters and GPU utilisation
Parameters not published.
Show all 16 tools
list_gatewaysList registered gateways and status
Parameters not published.
list_jobsList recent jobs with optional filters
Parameters not published.
list_kubectl_contextsList available kubectl contexts
Parameters not published.
list_pipelinesList automation pipelines
Parameters not published.
list_providersList configured AI/GPU cloud providers
Parameters not published.
list_secretsList secrets (names only, never values)
Parameters not published.
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README
vibops-mcp
The provider-agnostic MCP server for GPU infrastructure — one interface for any cloud, any cluster, any provider.
The problem
Large enterprises and CSPs managing GPU infrastructure deal with fragmentation — AWS, GCP, Azure, on-prem, neoclouds, each with their own API, dashboard, and cost model. Correlating utilisation, cost, and workload type across providers requires jumping between 5 tools.
The solution
vibops-mcp is a single MCP server that abstracts this complexity. One pip install, 26 tools, and your AI assistant can observe, operate, and optimize your entire GPU fleet — regardless of where it runs.
- 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 git+https://github.com/VibOpsai/vibops-mcp.git
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?"
Roadmap
Wave 1 — Current (v0.1)
- ✅ Kubernetes (generic — on-prem, Kind, kubeadm)
- ✅ Any VibOps-connected cluster via gateway
Wave 2 — Q2 2026
- 🔜 NVIDIA DGX Cloud (NGC API)
- 🔜 AWS (EC2 GPU + EKS)
- 🔜 GCP (A3/A2 + GKE)
- 🔜 Azure (ND/NC series + AKS)
Wave 3 — Q3 2026
- 🔜 CoreWeave, Lambda Labs, RunPod
- 🔜 OVHcloud, Scaleway
- 🔜 Dell APEX, HPE GreenLake
Want a provider prioritised? Open an issue.
Contributing
See CONTRIBUTING.md. All contributions require a DCO sign-off (git commit -s).
License
MIT — free to use, modify, and distribute. See LICENSE.
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.