"author:ACRT-EVAL" matching MCP servers:

Enables connection to a Kubernetes cluster to list and manage pods, services, deployments, and nodes, create/delete/describe pods, inspect namespaces, and retrieve logs for debugging.

Connects to a Kubernetes cluster using your current kubectl context, letting an assistant list pods, services, deployments, nodes, and namespaces, create, delete, and describe pods, and pull logs for debugging. It turns everyday cluster management into natural-language tool calls.

Enables connecting to a Kubernetes cluster through kubectl context to list and manage pods, services, deployments, nodes, and namespaces, create or delete pods, describe pods, and retrieve logs for debugging.

Enables connection to a Kubernetes cluster to list and manage pods, services, deployments, nodes, and namespaces, including creating, deleting, describing pods and retrieving logs for debugging.

Enables AI coding agents to run a specification-first workflow through MCP tools: Socratic interviews, immutable seed specifications, execution, staged evaluation, evolutionary loops, and PM/PRD generation.

Enables AI coding agents to run a specification-first workflow with Socratic interviews, immutable seed specs, staged evaluation gates, and persistent evolutionary loops across multiple runtimes.

Connects to a Kubernetes cluster through the current kubectl context and enables listing, creating, deleting, and describing pods, services, deployments, nodes, and namespaces, plus retrieving logs for debugging.

Enables AI coding agents to conduct Socratic interviews, crystallize answers into immutable specifications, execute staged development, and run automated evaluation and evolutionary loops until convergence.

Enables connecting to a Kubernetes cluster to manage it, including listing pods, services, deployments, nodes, and namespaces, creating and deleting pods, describing pods, and retrieving logs for debugging.

Enables AI coding agents to run a specification-first workflow through MCP tools: Socratic interview, immutable seed specification, execution, staged evaluation, and evolutionary loops. This helps turn vague ideas into verified, working codebases across multiple runtimes.