MCP Kubernetes
Provides comprehensive tools for managing Kubernetes clusters, including pod and deployment management, node monitoring, log retrieval, scaling operations, and multi-context support for advanced cluster operations.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
MCP Kubernetes
Model Context Protocol server for Kubernetes operations.
Description
This project implements an MCP (Model Context Protocol) server for advanced management of Kubernetes clusters. It provides tools to query, manage, and monitor resources such as pods, deployments, nodes, and logs, with support for multiple contexts and namespaces.
Related MCP server: Kubernetes MCP Server
Features
π’ Pod Management:
π¦ List pods by namespace and context.
π Retrieve complete pod details, including events, containers, volumes, and status.
π Deployment Management:
π¦ List deployments by namespace and context.
π Scale replicas and rollout (restart) deployments.
π Query detailed deployment status.
π₯οΈ Node Management:
ποΈ List nodes with capacity, status, roles, and cluster summary.
π Pod Logs:
π Retrieve logs from specific pods and containers, with options for previous logs and line count.
π Multi-context Support:
π Query and switch Kubernetes contexts.
βοΈ Set default context.
βοΈ Configuration and Logging:
π οΈ Utilities for loading Kubernetes configuration and structured logging.
Installation
pip install -r requirements.txtUsage
Run the MCP server:
python src/mcp_kubernetes/main.pyAPI Examples
The tools are exposed as MCP functions and can be invoked from compatible clients:
Get pods:
get_pods(context="my-context", namespace="default")Pod details:
get_pod_details(environment="prod", pod_name="nginx-123", namespace="default", context="my-context")Get deployments:
get_deployments(context="my-context", namespace="default")Scale deployment:
scale_deployment(namespace="default", deployment_name="web", replicas=5)Rollout deployment:
rollout_deployment(namespace="default", deployment_name="web")Get nodes:
get_nodes(context="my-context")Get logs:
get_logs(context="my-context", environment="prod", pod_name="nginx-123", namespace="default", container="nginx")Available contexts:
get_available_contexts()Change context:
set_default_context(context="other-context")
Project Structure
src/mcp_kubernetes/main.py: Main entry point for the MCP server.src/mcp_kubernetes/config.py: Configuration and logging utilities.src/mcp_kubernetes/tools/: Kubernetes tools modules:deployments.py: Deployment management.pods.py: Pod management and details.nodes.py: Node information and summary.logs.py: Pod log retrieval.
Contributing
Contributions are welcome! Please open an issue or pull request for suggestions and improvements.
License
MIT License. See the LICENSE file
This server cannot be deployed
Maintenance
Related MCP Connectors
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costsβall without needing to parse text output or use complex kubectl commands.
Manage Rackspace Spot Kubernetes Cloudspaces, node pools, and VMs from your AI assistant.
Manage Kubernetes clusters, deployments, databases, secrets and observability on Mengi Cloud.
Deploy, monitor, and manage your OpenClaw AI assistants via natural language.
Related MCP Servers
- AlicenseBqualityDmaintenanceEnables managing Kubernetes clusters through natural language by providing tools to list resources, view logs, port-forward services, scale deployments, and execute kubectl operations via AI assistants.81MIT
- FlicenseNot gradedqualityDmaintenanceEnables interactive Kubernetes cluster monitoring and troubleshooting through natural language queries. Users can diagnose pod issues, check service status, and investigate cluster problems using conversational AI.-
- AlicenseAqualityCmaintenanceEnables AI assistants to interact with and manage Kubernetes clusters, supporting operations on pods, deployments, services, configmaps, secrets, namespaces, metrics, and events with built-in safety features for destructive actions.917 npm1MIT
- AlicenseBqualityDmaintenanceEnables AI assistants to interact with Kubernetes clusters through 50 specialized tools for comprehensive cluster management. Supports both local kubectl and remote SSH-based execution for managing pods, deployments, services, and other Kubernetes resources.49MIT