"Kubernetes" matching MCP servers:

Enables natural-language management of Kubernetes clusters via kubectl-ai and Gemini, providing commands like listing pods, scaling deployments, and retrieving logs through a FastAPI backend.

AI-powered MCP server for enterprise OpenShift/Kubernetes cluster management, providing diagnostic tools, RAG knowledge retrieval, and autonomous remediation recommendations.

Professional AI-powered architecture diagram generator with multi-cloud support and MCP server integration. Generates beautiful, accurate diagrams with provider-specific icons for AWS, Azure, GCP, Kubernetes, and more.

Enables AI agents to plan, submit, monitor, and manage Kubeflow training jobs through natural language, without needing to learn Kubernetes or the Kubeflow SDK.

Provides AI assistants with direct access to Red Hat OpenShift AI observability data, enabling querying of Prometheus metrics, Alertmanager alerts, Loki logs, Grafana dashboards, and Kubernetes cluster state to troubleshoot vLLM inference workloads.

Enables users to create, inspect, list, and delete per-user JupyterLab servers on the UChicago ATLAS Analysis Facility Kubernetes cluster, with GPU availability and supported image listing tools.

Enables Kubernetes-native management of agent/model workloads via MCP tools, including fleet status, workload lifecycle, and boot orchestration for AI workflows.

AI-powered interface for Kubeflow Training via MCP, enabling AI assistants to manage distributed training jobs, fine-tune LLMs, and monitor workloads on Kubernetes through natural language.

A Kubernetes diagnostic agent that provides on-demand root cause analysis and human-in-the-loop remediation via Slack, using LLM reasoning with OPA-bounded security controls.

Enables MCP clients to query a local Kubernetes/Docker knowledge base through semantic search, ask DevOps questions via a RAG pipeline, and search a troubleshooting journal by symptom.

PrismSRE is an AI-powered Kubernetes troubleshooting agent that uses Google's Gemini models and the Model Context Protocol to provide autonomous diagnostics and real-time insights for cluster issues. It features a glassmorphism dashboard and secure read-only access to pods, logs, and deployments.

Enables MCP-compatible AI clients to retrieve FinOps-aligned knowledge and runbooks on demand, covering cloud cost optimization across AWS, Azure, GCP, and OCI, AI inference economics, Kubernetes, data platforms, allocation, chargeback, anomaly management, and waste detection.