Kubernetes MCP Server
An experimental Model Context Protocol (MCP) server that exposes Kubernetes resources to AI chat interfaces.
What is Kubernetes MCP Server?
Kubernetes MCP Server is an experimental Model Context Protocol (MCP) server that exposes Kubernetes resources to AI chat interfaces, enabling AI assistants to help analyze and manage cluster resources.
How to use Kubernetes MCP Server?
To use the server, clone the repository, build the project, and configure your AI assistant to interact with the server using the provided tools.
Key features of Kubernetes MCP Server?
- Integration of Kubernetes functionality with AI assistants
- Ability to list Kubernetes namespaces and resources
- Extensible architecture for adding more Kubernetes functionalities
Use cases of Kubernetes MCP Server?
- Resource discovery through AI queries
- Troubleshooting and log analysis with AI assistance
- Resource management and modification with AI help
- Documentation and explanation of Kubernetes resources
- Security analysis of cluster configurations
FAQ from Kubernetes MCP Server?
- What is the main purpose of this project?
To integrate Kubernetes resources with AI chat interfaces for easier management and analysis.
- Is it easy to extend the server with new functionalities?
Yes! The architecture is designed to be easily extended with additional Kubernetes functionalities.
Kubernetes MCP Server
An experimental Model Context Protocol (MCP) server that exposes Kubernetes functionality to AI chat interfaces through kubectl. This project demonstrates how to integrate Kubernetes with AI assistants, allowing them to help analyze and manage your cluster resources using familiar kubectl commands.
Overview
This project is built on top of the amazing mcpr project by @conikeec, which provides a protocol for AI assistants to interact with external tools and services.
The server provides a single kubectl tool that can execute any kubectl command, giving AI assistants full access to your Kubernetes cluster's capabilities.
Use Cases
- Resource Discovery: Ask your AI assistant to list and describe resources using kubectl commands
- Troubleshooting: Have the AI help analyze logs and resource states using kubectl
- Resource Management: Get AI assistance in creating or modifying resources
- Documentation: Ask the AI to explain what a particular resource does in your cluster
- Security Analysis: Have the AI help identify potential security issues in your cluster configuration
Example Interaction
User: Can you show me all pods in the default namespace?
AI: I'll check that for you...
[Uses the kubectl tool with command "get pods -n default"]
Here are the pods in the default namespace:
NAME READY STATUS RESTARTS AGE
nginx-7c658794b9-2j4k5 1/1 Running 0 2d
Future Possibilities
The current implementation provides a foundation for many powerful interactions:
- Complex troubleshooting scenarios using multiple kubectl commands
- Automated resource analysis and recommendations
- Interactive debugging sessions
- Security scanning and recommendations
- Performance optimization suggestions
Installation
- Clone the repository:
git clone https://github.com/yourusername/mcp-kubernetes.git
cd mcp-kubernetes
- Build the project:
cargo build --release
- Configure your AI assistant to use the server (instructions depend on your AI platform)
Requirements
- Rust 1.70 or later
- kubectl installed and configured
- Kubernetes cluster with appropriate permissions
- kubeconfig configured for your cluster
Configuration
The server uses your default kubeconfig and kubectl installation. Make sure you have the necessary permissions to execute the commands you want to run.
Security Considerations
Since this server allows execution of arbitrary kubectl commands, it's important to:
- Run it with appropriate RBAC permissions
- Consider implementing command validation or whitelisting
- Monitor and audit command execution
- Use it in a controlled environment
Acknowledgments
This project is built on top of the mcpr project by @conikeec. Many thanks to the author for creating this excellent protocol and implementation.
License
This project is licensed under the MIT License - see the LICENSE file for details.