MCP Kubernetes Server
by abhijeetka
An MCP server for Kubernetes that allows you to control your Kubernetes clusters through interactions with Large Language Models (LLMs). It wraps kubectl commands to provide a simple interface for managing Kubernetes resources.
Last updated: N/A
What is MCP Kubernetes Server?
The MCP Kubernetes Server is a tool that enables language models to interact with and manage Kubernetes clusters. It leverages the Model Context Protocol (MCP) to provide a standardized and type-safe interface for performing Kubernetes operations through natural language.
How to use MCP Kubernetes Server?
To use the MCP Kubernetes Server, you need a Kubernetes cluster with kubectl configured, Python 3.x, and the MCP framework installed. The server exposes Kubernetes functionality to LLMs through MCP, allowing you to manage resources using natural language prompts. Refer to the README for specific usage examples and configuration details.
Key features of MCP Kubernetes Server
Natural language interface for Kubernetes management
Simplified kubectl command execution
Integration with Large Language Models (LLMs)
Standardized interaction through Model Context Protocol (MCP)
Context awareness across multiple operations
Use cases of MCP Kubernetes Server
Automating Kubernetes deployments using natural language
Scaling Kubernetes resources with conversational commands
Troubleshooting Kubernetes issues through LLM-driven analysis
Creating and managing Kubernetes resources without needing to know kubectl syntax
FAQ from MCP Kubernetes Server
What is MCP?
What is MCP?
Model Context Protocol (MCP) is a framework that enables Language Models to interact with external tools and services in a structured way.
What are the requirements for using this server?
What are the requirements for using this server?
You need a Kubernetes cluster access configured via kubectl, Python 3.x, and MCP framework installed and configured.
How does this server integrate with LLMs?
How does this server integrate with LLMs?
The functions are decorated with @mcp.tool(), making them accessible to LLMs through the Model Context Protocol framework.
What kind of security measures should I take?
What kind of security measures should I take?
Ensure that proper access controls are in place for your Kubernetes cluster, the MCP server is running in a secure environment, and API access is properly authenticated and authorized.
How can I contribute to this project?
How can I contribute to this project?
Fork the repository, create a new branch for your feature, make your changes, write or update tests as needed, commit your changes, push to your branch, and open a Pull Request.