kubectl-mcp-server

A Model Context Protocol (MCP) server for Kubernetes that enables AI assistants like Claude, Cursor, and others to interact with Kubernetes clusters through natural language.

• Connect to a Kubernetes cluster
• List and manage pods, services, deployments, and nodes
• Create, delete, and describe pods and other resources
• Get pod logs and Kubernetes events
• Support for Helm v3 operations (installation, upgrades, uninstallation)
• kubectl explain and api-resources support
• Choose namespace for next commands (memory persistence)
• Port forward to pods
• Scale deployments and statefulsets
• Execute commands in containers
• Manage ConfigMaps and Secrets
• Rollback deployments to previous versions
• Ingress and NetworkPolicy management
• Context switching between clusters

• Process natural language queries for kubectl operations
• Context-aware commands with memory of previous operations
• Human-friendly explanations of Kubernetes concepts
• Intelligent command construction from intent
• Fallback to kubectl when specialized tools aren't available
• Mock data support for offline/testing scenarios
• Namespace-aware query handling

• Cluster health monitoring
• Resource utilization tracking
• Pod status and health checks
• Event monitoring and alerting
• Node capacity and allocation analysis
• Historical performance tracking
• Resource usage statistics via kubectl top
• Container readiness and liveness tracking

• RBAC validation and verification
• Security context auditing
• Secure connections to Kubernetes API
• Credentials management
• Network policy assessment
• Container security scanning
• Security best practices enforcement
• Role and ClusterRole management
• ServiceAccount creation and binding
• PodSecurityPolicy analysis
• RBAC permissions auditing
• Security context validation

• Cluster diagnostics and troubleshooting
• Configuration validation
• Error analysis and recovery suggestions
• Connection status monitoring
• Log analysis and pattern detection
• Resource constraint identification
• Pod health check diagnostics
• Common error pattern identification
• Resource validation for misconfigurations
• Detailed liveness and readiness probe validation

• Multiple transport protocols support (stdio, SSE)
• Integration with multiple AI assistants
• Extensible tool framework
• Custom resource definition support
• Cross-namespace operations
• Batch operations on multiple resources
• Intelligent resource relationship mapping
• Error explanation with recovery suggestions
• Volume management and identification

The Kubectl MCP Tool implements the Model Context Protocol (MCP), enabling AI assistants to interact with Kubernetes clusters through a standardized interface. The architecture consists of:

1. MCP Server: A compliant server that handles requests from MCP clients (AI assistants)
2. Tools Registry: Registers Kubernetes operations as MCP tools with schemas
3. Transport Layer: Supports stdio, SSE, and HTTP transport methods
4. Core Operations: Translates tool calls to Kubernetes API operations
5. Response Formatter: Converts Kubernetes responses to MCP-compliant responses

The tool operates in two modes:

1. CLI Mode: Direct command-line interface for executing Kubernetes operations
2. Server Mode: Running as an MCP server to handle requests from AI assistants

For detailed installation instructions, please see the Installation Guide.

You can install kubectl-mcp-tool directly from PyPI:

pip install kubectl-mcp-tool

For a specific version:

pip install kubectl-mcp-tool==1.1.1

The package is available on PyPI: https://pypi.org/project/kubectl-mcp-tool/1.1.1/

• Python 3.9+
• kubectl CLI installed and configured
• Access to a Kubernetes cluster
• pip (Python package manager)

# Install latest version from PyPI
pip install kubectl-mcp-tool

# Or install development version from GitHub
pip install git+https://github.com/rohitg00/kubectl-mcp-server.git

# Clone the repository
git clone https://github.com/rohitg00/kubectl-mcp-server.git
cd kubectl-mcp-server

# Install in development mode
pip install -e .

After installation, verify the tool is working correctly:

# Check CLI mode
kubectl-mcp --help

Note: This tool is designed to work as an MCP server that AI assistants connect to, not as a direct kubectl replacement. The primary command available is kubectl-mcp serve which starts the MCP server.

The minimal wrapper (kubectl_mcp_tool.minimal_wrapper) is a simplified MCP server implementation that provides better compatibility across different AI assistants and addresses common issues:

1. Direct Configuration

   {
     "mcpServers": {
       "kubernetes": {
         "command": "python",
         "args": ["-m", "kubectl_mcp_tool.minimal_wrapper"],
         "env": {
           "KUBECONFIG": "/path/to/your/.kube/config",
           "PATH": "/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin",
           "MCP_LOG_FILE": "/path/to/logs/debug.log",
           "MCP_DEBUG": "1"
         }
       }
     }
   }

2. Key Environment Variables

   • MCP_LOG_FILE: Path to log file (recommended to avoid stdout pollution)
   • MCP_DEBUG: Set to "1" for verbose logging
   • MCP_TEST_MOCK_MODE: Set to "1" to use mock data instead of real cluster
   • KUBECONFIG: Path to your Kubernetes config file
   • KUBECTL_MCP_LOG_LEVEL: Set to "DEBUG", "INFO", "WARNING", or "ERROR"

3. Testing the Minimal Wrapper You can test if the wrapper is working correctly with:

   python -m kubectl_mcp_tool.simple_ping

   This will attempt to connect to the server and execute a ping command.

Add the following to your Claude Desktop configuration at ~/.config/claude/mcp.json (Windows: %APPDATA%\Claude\mcp.json):

{
  "mcpServers": {
    "kubernetes": {
      "command": "python",
      "args": ["-m", "kubectl_mcp_tool.minimal_wrapper"],
      "env": {
        "KUBECONFIG": "/path/to/your/.kube/config"
      }
    }
  }
}

Add the following to your Cursor AI settings under MCP by adding a new global MCP server:

{
  "mcpServers": {
    "kubernetes": {
      "command": "python",
      "args": ["-m", "kubectl_mcp_tool.minimal_wrapper"],
      "env": {
        "KUBECONFIG": "/path/to/your/.kube/config",
        "PATH": "/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/opt/homebrew/bin"
      }
    }
  }
}

Save this configuration to ~/.cursor/mcp.json for global settings.

Note: Replace /path/to/your/.kube/config with the actual path to your kubeconfig file. On most systems, this is ~/.kube/config.

Add the following to your Windsurf configuration at ~/.config/windsurf/mcp.json (Windows: %APPDATA%\WindSurf\mcp.json):

{
  "mcpServers": {
    "kubernetes": {
      "command": "python",
      "args": ["-m", "kubectl_mcp_tool.minimal_wrapper"],
      "env": {
        "KUBECONFIG": "/path/to/your/.kube/config"
      }
    }
  }
}

For automatic configuration of all supported AI assistants, run the provided installation script:

bash install.sh

This script will:

1. Install the required dependencies
2. Create configuration files for Claude, Cursor, and WindSurf
3. Set up the correct paths and environment variables
4. Test your Kubernetes connection

1. kubectl installed and in your PATH
2. A valid kubeconfig file
3. Access to a Kubernetes cluster
4. Helm v3 (optional, for Helm operations)

List all pods in the default namespace

Create a deployment named nginx-test with 3 replicas using the nginx:latest image

Get logs from the nginx-test pod

Forward local port 8080 to port 80 on the nginx-test pod

# Clone the repository
git clone https://github.com/rohitg00/kubectl-mcp-server.git
cd kubectl-mcp-server

# Install dependencies
pip install -r requirements.txt

# Install in development mode
pip install -e .

# Run tests
python -m python_tests.test_all_features

├── kubectl_mcp_tool/         # Main package
│   ├── __init__.py           # Package initialization
│   ├── cli.py                # CLI entry point
│   ├── mcp_server.py         # MCP server implementation
│   ├── mcp_kubectl_tool.py   # Main kubectl MCP tool implementation
│   ├── natural_language.py   # Natural language processing
│   ├── diagnostics.py        # Diagnostics functionality
│   ├── core/                 # Core functionality 
│   ├── security/             # Security operations
│   ├── monitoring/           # Monitoring functionality
│   ├── utils/                # Utility functions
│   └── cli/                  # CLI functionality components
├── python_tests/             # Test suite
│   ├── run_mcp_tests.py      # Test runner script
│   ├── mcp_client_simulator.py # MCP client simulator for mock testing
│   ├── test_utils.py         # Test utilities
│   ├── test_mcp_core.py      # Core MCP tests
│   ├── test_mcp_security.py  # Security tests
│   ├── test_mcp_monitoring.py # Monitoring tests
│   ├── test_mcp_nlp.py       # Natural language tests
│   ├── test_mcp_diagnostics.py # Diagnostics tests
│   └── mcp_test_strategy.md  # Test strategy documentation
├── docs/                     # Documentation
│   ├── README.md             # Documentation overview
│   ├── INSTALLATION.md       # Installation guide
│   ├── integration_guide.md  # Integration guide
│   ├── cursor/               # Cursor integration docs
│   ├── windsurf/             # Windsurf integration docs
│   └── claude/               # Claude integration docs
├── compatible_servers/       # Compatible MCP server implementations
│   ├── cursor/               # Cursor-compatible servers
│   ├── windsurf/             # Windsurf-compatible servers
│   ├── minimal/              # Minimal server implementations
│   └── generic/              # Generic MCP servers
├── requirements.txt          # Python dependencies
├── setup.py                  # Package setup script
├── pyproject.toml            # Project configuration
├── MANIFEST.in               # Package manifest
├── LICENSE                   # MIT License
├── CHANGELOG.md              # Version history
├── .gitignore                # Git ignore file
├── install.sh                # Installation script
├── publish.sh                # PyPI publishing script
└── start_mcp_server.sh       # Server startup script

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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