What is Mcp Kubernetes Server

mcp-kubernetes-server is a lightweight MCP server that provides natural language processing and API access to Kubernetes clusters, integrating kubectl commands and the Kubernetes Python client.

Use cases

Use cases include listing pods and deployments, creating and deleting namespaces, inspecting cluster resources, modifying configurations, and scaling deployments using natural language commands.

How to use

To use mcp-kubernetes-server, clone the repository, set up a virtual environment, activate it, and install the necessary dependencies. You can then interact with the server using natural language queries or through the API endpoints.

Key features

Key features include a natural language interface for converting English queries to kubectl commands, full CRUD operations for managing Kubernetes resources, dual execution mode with kubectl and the Kubernetes Python client, and advanced capabilities like namespace validation and resource management.

Where to use

mcp-kubernetes-server can be used in DevOps environments, cloud-native application development, and any scenario where Kubernetes management is required through simplified command interfaces.

Kubernetes MCP Server

A lightweight MCP server that provides natural language processing and API access to Kubernetes clusters, combining both kubectl commands and Kubernetes Python client.

https://github.com/user-attachments/assets/48e061cd-3e85-40ff-ab04-a1a2b9bbd152

✨ Features

• Natural Language Interface: Convert plain English queries to kubectl commands
  • List pods and deployments across all namespaces
  • Fallback to general resource listing for unsupported queries
• Full CRUD Operations:
  • 🆕 Create/Delete namespaces, pods, and deployments via API endpoints
  • 🔍 Inspect cluster resources
  • ✏️ Modify labels, annotations, and deployment configurations
  • 🗑️ Graceful deletion
  • 📊 Scale deployments
• Dual Execution Mode:
  • kubectl command integration
  • Kubernetes Python client (official SDK)
• Advanced Capabilities:
  • Namespace validation (DNS-1123 compliant)
  • Label filtering
  • Grace period control
  • Automatic command fallback
  • Resource management (CPU, memory)
  • Environment variable configuration

📦 Installation

Prerequisites

• Python 3.11+
• Kubernetes cluster access
• kubectl configured locally
• UV installed

# Clone repository
git clone https://github.com/ductnn/mcp-kubernetes-server.git 
cd mcp-kubernetes-server

# Create virtual environment
uv venv .venv

# Activate (Unix)
source .venv/bin/activate

# Install dependencies
uv pip install -r requirements.txt

🚀 Usage

Natural Language Processing

The server supports basic natural language queries for listing resources:

# List all pods
result = nl_processor.process("Show me all pods")

# List all deployments
result = nl_processor.process("Show me all deployments")

# Query with namespace
result = nl_processor.process("Show me all resources", "kube-system")

For more complex operations, use the dedicated API endpoints:

# Create a pod
pod_service.create_pod(
    name="my-pod",
    namespace="default",
    image="nginx:latest",
    labels={"app": "my-app"}
)

# Create a deployment
deployment_service.create_deployment(
    name="my-deployment",
    namespace="default",
    image="nginx:latest",
    replicas=3
)

# Delete a namespace
namespace_service.delete("my-namespace", force=True)

API Endpoints

The server provides RESTful endpoints for all operations:

• /api/pods - Pod operations
• /api/deployments - Deployment operations
• /api/namespaces - Namespace operations
• /api/cluster - Cluster operations
• /api/nlp - Natural language processing

🤖 Usage with AI Assistants

Claude Desktop

• Open your Claude Desktop and choose Settings -> choose mode Developer -> Edit config and open file claude_desktop_config.json and edit:

• Then, restart your Claude Desktop and play :)

🧪 Testing

Run the test suite:

# Run all tests
pytest

# Run specific test file
pytest tests/unit/test_pod_service.py

# Run with coverage
pytest --cov=.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.