vibops-mcp
The MCP server for VibOps — The AI Infrastructure Engine. From code to GPU in one conversation.
The problem
Getting an AI app from code to production on GPUs requires stitching together 9+ tools — git, Docker, CI/CD, Helm, kubectl, GPU monitoring, cost management, compliance, alerting. Each with its own API, dashboard, and cost model. No single interface spans the full pipeline.
The solution
vibops-mcp connects your AI assistant to VibOps — the engine that clones, builds, deploys, scales, monitors, fixes, and bills your apps and agents on any GPU, any cluster, any cloud. One pip install, 117 tools, one conversation.
- Ship — clone repos, build containers, deploy models, run Helm/kubectl, trigger pipelines, submit Slurm jobs
- Operate — scale deployments, manage VMs (Proxmox/XO/vSphere/HPE VME), detect and remediate GPU anomalies
- Observe — GPU utilisation, workload breakdown, MTTR, cost estimates, live K8s deployments
- Govern — AI Act compliance, SOC 2/RGPD reports, immutable audit chain, policy management
- FinOps — per-agent cost tracking, budget enforcement, chargeback, spend trends, waste analysis
Every operation goes through your VibOps instance and is recorded in the immutable audit log.
Installation
pip install git+https://github.com/VibOpsai/vibops-mcp.git
Released versions are also on PyPI, if you prefer a pinned one:
pip install vibops-mcp
Configuration
You need two environment variables:
Connecting your client
vibops-mcp speaks the Model Context Protocol, so the same server entry works
in any MCP-compatible client. Add it to your client's MCP configuration:
{
"mcpServers": {
"vibops": {
"command": "vibops-mcp",
"env": {
"VIBOPS_URL": "https://vibops.example.com",
"VIBOPS_TOKEN": "your-token-here"
}
}
}
}
Where that file lives depends on the client:
Other MCP clients read the same block from their own configuration file; see your client's documentation for its location. Some ship a command that writes the entry for you instead of editing the file by hand.
Available tools
Observation (16 tools — read-only)
Actions (18 tools — write)
HPE VME / Morpheus (13 tools)
Hypervisors — Proxmox, vSphere, Xen Orchestra (30 tools)
Documented last and listed here in full: these were registered and served but absent from this README, so a reader counting the sections found 87 of the 117 the first line promises. They now add up.
Proxmox VE (5 tools)
VMware vSphere (9 tools)
Xen Orchestra / XCP-ng (15 tools)
Cross-hypervisor
Configuration (3 tools)
Agent Infrastructure Control Plane (12 tools)
The missing layer between your AI agents and your GPU fleet. Works with any framework (n8n, LangChain, CrewAI, Dify) — just point to the VibOps LLM Proxy.
Governance & Compliance (21 tools)
GPU FinOps (4 tools)
LLM Inference Proxy
VibOps includes a transparent OpenAI-compatible proxy (port 8004) that sits between your AI agents and LLM inference servers (vLLM, Ollama, TGI). Every inference request is logged with agent attribution for FinOps.
Your agents point to the proxy instead of the LLM directly:
# Before
OPENAI_BASE_URL=http://vllm:8000/v1
# After
OPENAI_BASE_URL=http://vibops-proxy:8004/v1
Add a X-VibOps-Agent-Id header to attribute costs per agent:
curl -X POST http://vibops-proxy:8004/v1/chat/completions \
-H "X-VibOps-Agent-Id: pricing-agent-v2" \
-H "X-VibOps-Team: supply-chain" \
-d '{"model": "mistral:7b", "messages": [...]}'
The proxy captures: agent ID, team, model, tokens, latency, GPU cost — visible in the console FinOps dashboard and queryable via get_agent_usage.
Example prompts
"Clone my repo and deploy it on the GPU cluster."
"Deploy llama3:8b on vibops-dev with 2 replicas."
"Scale the inference deployment to 4 replicas on prod-cluster."
"What's our GPU utilisation trend over the last 7 days?"
"Show me the cost breakdown per cluster this week."
"Which clusters have open critical GPU alerts?"
"Are there any open GPU anomalies right now?"
"Scan my infrastructure and show discovered services."
"What's our AI Act compliance score and which controls are non-compliant?"
"Generate a SOC 2 report for Q1 2026."
"Verify the audit chain hasn't been tampered with."
"Which agent costs the most in GPU this month?"
"Show me the inference cost breakdown for the pricing agent."
"Which agents share the same model, and what does each one cost?"
"Create a machine identity for the pricing-agent with a 1-year expiry."
"Show me the spend trend for the last 7 days and flag any waste."
Contributing
See CONTRIBUTING.md. All contributions require a DCO sign-off (git commit -s).
License
MIT — free to use, modify, and distribute. See LICENSE.
