Integrates with Google's Gemini API for AI capabilities, allowing the MCP server to leverage Gemini models for processing and responding to requests.
Enables interaction with Kubernetes clusters, allowing for management and orchestration of containerized applications through the Kubernetes API using the Python client.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@kube-mcp list all pods in the default namespace"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Goto https://aistudio.google.com/ and get yourself an API Key. Currently, gemini-2.0-pro-exp-02-05 LLM is available absolutely free of charge. Other models available for very cheap price also.
Related MCP server: mcp-kubernetes-server
Goose is an open source AI agent that supercharges your software development by automating coding tasks. We will use Codename Goose as it has a built in MCP client. Install Codename Goose by following the steps here https://block.github.io/goose/docs/getting-started/installation. Setup GOOGLE_API_KEY environment variable so that Goose knows to use Gemini API. Understand how to configure using goose configure and start as session using goose session.
Read about MCP by reading the documentation : https://modelcontextprotocol.io/introduction and specifically the Python SDK : https://github.com/modelcontextprotocol/python-sdk
Clone this repository and test it using mcp dev server.py. Note that this project uses uv package manager instead of pip. Learn about uv by reading docs : https://github.com/astral-sh/uv
This project uses the kubernetes python client: https://github.com/kubernetes-client/python
Install minikube by following isntructions : https://minikube.sigs.k8s.io/docs/start/?arch=%2Flinux%2Fx86-64%2Fstable%2Fbinary+download
Ensure that the config to the cluster is provided to the MCP server. Look at the KubernetesManager and config.load_kube_config() to understand how the config is loaded.
Add the MCP Server as an extension by reading the following docs : https://block.github.io/goose/docs/getting-started/using-extensions
Start a new goose session using command goose session --with-builtin developer --with-extension "uvx kube-mcp"
Try giving a command in Goose and make it interact with Minikube using the MCP Server
This server cannot be installed
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
MCP server for building and testing AI agents with multi-model experimentation and insights.