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Manage AI agents, GPU infrastructure, costs, and compliance from one control plane.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "vibops-mcp" yet — see the docs or source repo.
Using VibOps MCP, inventory all current GPU resources, group them by cloud provider and hardware type, flag instances with utilization below 30%, and provide actionable scale-down recommendations.
A GPU inventory, a list of underutilized nodes, and prioritized scale-down recommendations.
Use VibOps MCP to set GPU cost budgets and model policies for different AI agents: support agents have a $500 monthly cap and may only use approved models; research agents have a $3,000 monthly cap, with alerts and automatic task suspension when over budget.
Returns configured budgets and policies for each agent, plus the alerting and task suspension rules.
Use VibOps MCP to summarize key AI infrastructure operations from the last 30 days, generate an audit summary suitable for SOC 2 and AI Act review, and indicate whether an immutable HMAC audit chain is present.
An audit summary with key changes, accountability details, and audit chain status.
Build, deploy, manage hosted apps, and chat with each app’s persistent AI agent.
Manage GPU inventory, VM lifecycle, billing, SSH keys, and setup recipes.
Discover GPU nodes, reserve them with signed handles, and dispatch inference tasks through MCP.
Enforce policy checks on AI tool calls without exposing system credentials.
Build, deploy, and operate secure, observable AI agent MCP infrastructure.
Aggregate multiple MCP servers into a few fixed tools for AI apps.