Content
# GKE MCP Server and Gemini CLI Extension
Enable MCP-compatible AI agents to interact with Google Kubernetes Engine.
<img src="https://raw.githubusercontent.com/GoogleCloudPlatform/gke-mcp/main/assets/gke-mcp-gemini-cli-demo.gif" alt="A demonstration of using the GKE MCP server with the Gemini CLI" width="600">
## Installation
Choose a way to install the MCP Server and then connect your AI to it.
### Use as a Gemini CLI Extension
1. Install [Gemini CLI](https://github.com/google-gemini/gemini-cli?tab=readme-ov-file#-installation).
2. Install the extension
```sh
gemini extensions install https://github.com/GoogleCloudPlatform/gke-mcp.git
```
### Use in MCP Clients / Other AIs
#### Quick Install (Linux & macOS only)
```sh
curl -sSL https://raw.githubusercontent.com/GoogleCloudPlatform/gke-mcp/main/install.sh | bash
```
#### Manual Install
If you haven't already installed Go, follow [these instructions](https://go.dev/doc/install).
Once Go is installed, run the following command to install gke-mcp:
```sh
go install github.com/GoogleCloudPlatform/gke-mcp@latest
```
The `gke-mcp` binary will be installed in the directory specified by the `GOBIN` environment variable. If `GOBIN` is not set, it defaults to `$GOPATH/bin` and, if `GOPATH` is also not set, it falls back to `$HOME/go/bin`.
You can find the exact location by running `go env GOBIN`. If the command returns an empty value, run `go env GOPATH` to find the installation directory.
For additional help, refer to the troubleshoot section: [gke-mcp: command not found](TROUBLESHOOTING.md#gke-mcp-command-not-found-on-macos-or-linux).
### Add the MCP Server to your AI
For detailed instructions on how to connect the GKE MCP Server to various AI clients, including cursor, Visual Studio Code, and claude desktop, please refer to our dedicated [installation guide](docs/installation_guide/).
## MCP Tools
- `cluster_toolkit`: Creates AI optimized GKE Clusters.
- `list_clusters`: List your GKE clusters.
- `get_cluster`: Get detailed about a single GKE Cluster.
- `create_cluster`: Create a new GKE Cluster.
- `get_kubeconfig`: Config the kubeconfig to a single GKE Cluster.
- `giq_generate_manifest`: Generate a GKE manifest for AI/ML inference workloads using Google Inference Quickstart.
- `list_recommendations`: List recommendations for your GKE clusters.
- `query_logs`: Query Google Cloud Platform logs using Logging Query Language (LQL).
- `get_log_schema`: Get the schema for a specific GKE log type.
## MCP Commands
Commands provide in-context domain specific functionality based on expert knowledge and best practices.
- `gke-upgrade-risk-report`: GKE control plane upgrade risk report, analyzing the potential risks of upgrading from its current version to the target version. Performs pre-upgrade checks, API deprecations scans, and more.
- `gke-upgrades-best-practices-risk-report`: GKE control plane upgrade best practices, applied for the specified cluster. Helps making upgrades uneventful.
## MCP Context
In addition to the tools above, a lot of value is provided through the bundled context instructions.
- **Cost**: The provided instructions allows the AI to answer many questions related to GKE costs, including queries related to clusters, namespaces, and Kubernetes workloads.
- **GKE Known Issues**: The provided instructions allows the AI to fetch the latest GKE Known issues and check whether the cluster is affected by one of these known issues.
## Supported MCP Transports
By default, `gke-mcp` uses the [stdio]("https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#stdio") transport. Additionally, the [Streamable HTTP](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http) transport is supported as well.
You can set the transport mode using the following options:
`--server-mode`: transport to use for the server: stdio (default) or http
`--server-port`: server port to use when server-mode is http or sse; defaults to 8080
```sh
gke-mcp --server-mode http --server-port 8080
```
> [!WARNING]
> When using the `Streamable HTTP` transport, the server listens on all network interfaces (e.g., `0.0.0.0`), which can expose it to any network your machine is connected to.
> Please ensure you have a firewall ad/or other security measures in place to restrict access if the server is not intended to be public.
### Connecting Gemini CLI to the HTTP Server
To connect Gemini CLI to the `gke-mcp` HTTP server, you need to configure the CLI to point to the correct endpoint. You can do this by updating your `~/.gemini/settings.json` file. For a basic setup without authentication, the file should look like this:
```json
{
"mcpServers": {
"gke": {
"httpUrl": "http://127.0.0.1:8080/mcp"
}
}
}
```
This configuration tells Gemini CLI how to reach the gke-mcp server running on your local machine at port 8080.
## Development
To compile the binary and update the `gemini-cli` extension with your local changes, follow these steps:
1. Remove the global gke-mcp configuration
```sh
rm -rf ~/.gemini/extensions/gke-mcp
```
1. Build the binary from the root of the project:
```sh
go build -o gke-mcp .
```
1. Run the installation command to update the extension manifest:
```sh
./gke-mcp install gemini-cli --developer
```
This will make `gemini-cli` use your locally compiled binary.
## Disclaimers
- The Google Cloud Platform Terms of Service (available at [https://cloud.google.com/terms/](https://cloud.google.com/terms/)) and the Data Processing and Security Terms (available at [https://cloud.google.com/terms/data-processing-terms](https://cloud.google.com/terms/data-processing-terms)) do not apply to any component of the GKE MCP Server software.
- This tool is provided for education and experimentation, and is not an officially supported Google product. It is maintained on a best-effort basis, and may change without notice.
- This project interacts with Large Language Models and comes with inherent risks.
- **Use at Your Own Risk:** This software is experimental, non-deterministic, and provided "AS IS" with NO GUARANTEES or warranties.
- **NOT FOR PRODUCTION USE.**
- **Data Sensitivity:** Avoid using untrusted data. NEVER input secrets, API keys, or sensitive information.
- **Verify Outputs:** LLM responses can be unpredictable and may be inaccurate. Always verify results.
