Kubernetes
The kube-mcp server provides AI assistants with direct access to Kubernetes cluster operations through a set of specialized tools built with Python using the FastMCP framework. It enables capabilities like listing, creating, and managing pods, deployments, and services, as well as retrieving logs and cleaning up resources. The implementation includes predefined container templates for common images (Ubuntu, Nginx, Busybox, Alpine) with appropriate resource limits and health checks. It's particularly valuable for DevOps workflows requiring Kubernetes management within AI assistant conversations, allowing users to monitor cluster state, deploy test containers, and troubleshoot issues without switching to separate command-line tools.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- cloud-infra
- Source
- PulseMCP
- Repository
- github.com/lochgeo/kube-mcp
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Kubernetes from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.