Enables AI agents to plan, submit, monitor, and manage Kubeflow training jobs through natural language, without needing to learn Kubernetes or the Kubeflow SDK.
Enables AI agents to manage GPU training end-to-end through natural language, including submitting and scheduling jobs, monitoring logs and metrics, diagnosing failures, comparing runs, and recommending the best checkpoints.
An MCP server that enables AI assistants to interact with Kubernetes clusters by translating natural language into kubectl and Helm operations. It allows users to query, manage, and diagnose Kubernetes resources and cluster states through a seamless integration.
Enables Kubernetes-native management of agent/model workloads via MCP tools, including fleet status, workload lifecycle, and boot orchestration for AI workflows.
A production-grade MCP server providing a secure, natural-language interface to Kubernetes for developers and AI agents, with multi-cluster routing, OIDC authentication, RBAC, and audit logging.