"kubernetes" matching MCP servers:

A version-aware Kubernetes documentation assistant that connects LLMs to trusted, real-time Kubernetes docs to reduce hallucinations and ensure accurate, version-specific responses.

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

Enables running durable, traceable AI agents via LangGraph through a universal MCP interface, integrating with Hatchet for orchestration, logging, and retries. Provides tools for knowledge management (ingestion, RAG) and Kubernetes operations (diagnosis, auto-fix).

Enables natural language management of Hammerspace storage clusters with automated file ingestion, tagging, tier management, and vector embedding generation. Supports real-time file monitoring, multi-format document processing, and Kubernetes-based ingestion workflows with Milvus integration.

Enables AI agents to securely retrieve pre-compiled bounded context and memory state over a zero-network Unix Domain Socket, ensuring tenant isolation and sub-millisecond rehydration in Kubernetes environments.

Enables intelligent Kubernetes deployments, automated documentation testing, organizational pattern management, and shared prompt libraries. Provides AI-powered recommendations based on cluster capabilities and automates DevOps workflows through conversational interfaces.

Enables querying Confluence or Kubernetes documentation through hybrid search and an agentic RAG pipeline, returning structured answers with citations.

Enables an event-driven operations agent to query internal SQL, GitHub, Slack, Jira, documentation, and Kubernetes telemetry through 28 RBAC-gated MCP tools, with shared memory for playbook retrieval and incident workflows.

A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.

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.