Dot AI (Kubernetes Deployment)
Automates Kubernetes deployment workflows with intelligent resource discovery, intent-based recommendations, manifest generation, and deployment execution while capturing organizational patterns through vector search for codifying deployment knowledge and providing deployment guidance.
About
This MCP server provides AI-powered Kubernetes deployment automation with intelligent resource discovery and organizational pattern management, built by Viktor Farcic using TypeScript with Anthropic Claude for AI analysis, Qdrant for vector storage, and the Kubernetes client library. The implementation offers deployment workflow tools including intent-based recommendations, solution selection, manifest generation, and automated deployment with readiness checking, plus organizational data management for capturing deployment patterns and cluster resource capabilities. Features include semantic search across organizational patterns and cluster resources, documentation testing workflows, session-based pattern creation, and Docker/Kubernetes deployment support with both MCP and CLI interfaces, making it valuable for platform engineers who need to codify deployment knowledge, automate complex Kubernetes workflows, and provide AI-assisted deployment guidance to development teams.
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