Kubernetes MCP Server

The Most Comprehensive Kubernetes MCP Server – Manage your entire Kubernetes ecosystem through AI assistants like Claude, ChatGPT, and more!

---

📺 Live Demo

![K8s Assistant in action] AI automatically validating YAML, deploying to 'test' namespace, and opening a tunnel.

https://github.com/user-attachments/assets/708c91e1-6312-4b11-bbd5-4bd8663295fd

✨ Comprehensive Coverage (65+ Tools!)

Our project provides a complete set of Kubernetes management tools, including advanced workloads, configuration management, observability, and security.

🎯 Production-Grade Features

• Complete Workload Management: Deployments, StatefulSets, DaemonSets, Jobs, CronJobs
• Advanced Scheduling: Node taints management, resource quotas, limit ranges
• Security & RBAC: Webhook configurations, ClusterRole listings, Secrets management
• Observability: Event tracking, pod logs, HPA metrics, node resource utilization
• Multi-Cluster Support: Register and manage multiple Kubernetes clusters

---

📋 Features Overview

🚀 Advanced Deployment & Automation

• Universal Apply: Apply ANY Kubernetes resource (Namespace, Pod, Deployment, etc.) using a single tool.
• Server-Side Apply (SSA): Optimized resource management using ApplyPatch for safe, conflict-free updates.
• Dry-Run Validation: Validate YAML manifests against the K8s API without creating resources (dry_run: true).
• Smart Field Management: Track changes with custom field_manager identifiers (e.g., ai-provisioner).

🌐 Networking & Connectivity

• Real-time Port Forwarding: Establish secure tunnels from localhost to any Pod port instantly.
• Session Management: Full control to Start and Stop/Terminate active port-forwarding tunnels via AI.
• Service Discovery: List and manage Services and Ingress controllers across all namespaces.

📊 Monitoring & Debugging

• Intelligent Logging: Real-time log streaming with Automated Log Zipping for large data exports.
• Resource Metrics: Monitor Node and Pod resource utilization (CPU, Memory).
• Event Filtering: Track cluster-wide events with advanced filtering by object type and namespace.

🔧 Core Workload Operations

• Workload Management: Full CRUD operations for Pods, Deployments, StatefulSets, and DaemonSets.
• Batch Processing: Trigger, suspend, and retrieve logs from Jobs and CronJobs.
• Scaling: Dynamic scaling of replicas for Deployments and StatefulSets.

⚙️ Configuration & Security

• Config & Secrets: Secure management of ConfigMaps and Secrets.
• RBAC & Policies: List and audit ClusterRoles, ResourceQuotas, and LimitRanges.
• Advanced Scheduling: Manage Node Taints and Webhook configurations (Mutating/Validating).

🖥️ Multi-Cluster Management

• Dynamic Registration: Register multiple clusters on-the-fly using local Kubeconfig paths or raw data.
• Context Switching: Seamlessly interact with different cluster IDs in a single session.

---

🚀 Getting Started

Quick Installation

# Clone the repository
git clone [https://github.com/yourusername/k8s-mcp-server.git](https://github.com/yourusername/k8s-mcp-server.git)
cd k8s-mcp-server

# Build the server
go build -o k8s-mcp-server

Run with Default Configuration

./k8s-mcp-server

Configuration Example

This configuration is typically used in the client (AI assistant) to point to your server instance.

{
  "servers": {
    "k8s": {
      "command": "path/to/k8s-mcp-server",
      "env": {
        "KUBECONFIG_PATH": "/path/to/kubeconfig"
      }
    }
  }
}

---

📖 Usage Examples

Ask your AI Assistant (e.g., Claude, ChatGPT) to manage your cluster:

• "List all deployments in the production namespace"
• "Scale my-api deployment to 5 replicas"
• "Show me recent events in the default namespace"
• "Get logs from the failing payment-service pod"
• "Create a new Job to run a database migration"
• "Check HPA status for the frontend service"

🤖 AI Assistant Integration

This server is compatible with any client supporting the Model Context Protocol (MCP):

• Claude Desktop
• Cursor AI
• Windsurf
• Any MCP-compatible client

🏆 Developer Benefits

Feature	Description
✅ Developer Experience	Intuitive Tool Names: Consistent k8s__ naming, Detailed Descriptions, Smart Defaults for namespace and other parameters, Comprehensive error handling.
✅ Production Ready	Multi-Cluster: Manage dev, staging, and production clusters; Security First: Never expose secret values, only metadata; Audit Trail: Event logging and monitoring tools; Resource Control: Quotas and limits.
✅ Extensible Architecture	Easy to add new tools using the defined Domain-Use Case-Delivery structure.

🔄 Roadmap

Version	Features
v1.1	CRD support, Helm management tools, Network Policies, Pod Disruption Budgets, Vertical Pod Autoscaling (VPA)
v1.2	Multi-tenant namespace management, Cost optimization recommendations, Security scanning integration, Backup & Restore operations, GitOps synchronization

🤝 Contributing

We welcome contributions! Here's how you can help:

• Add New Tools: See our guide for adding Kubernetes resource types
• Improve Documentation: Help make our README better
• Report Issues: Found a bug? Let us know
• Feature Requests: Suggest new tools

Check out CONTRIBUTING.md for detailed developer guidelines.

📚 Learn More

• Model Context Protocol
• Kubernetes API Documentation
• Go MCP SDK

🛡️ License

Apache 2.0 License - see LICENSE file for details.

⭐ Show Your Support

If this project helps you manage Kubernetes more effectively, please give it a star! It helps others discover the tool and motivates continued development.

Join the revolution in Kubernetes management through AI!