Leveraging Gemini CLI and the underlying Gemini LLM to build Model Context Protocol (MCP) AI applications with Python with a local development environment hosted on an Azure Linux VM.
What is Gemini CLI?
The Gemini CLI is an open-source, terminal-based AI agent from Google that allows developers to interact directly with Gemini models, such as Gemini 2.5 Pro, for coding, content creation, and workflow automation. It supports file operations, shell commands, and connects to external tools via the Model Context Protocol (MCP).
The full details on Gemini CLI are available here:
Configuring an Azure Linux VM
Step by step instructions on configuring an Azure Linux VM are here:
Customizing an Azure VM for Cross Cloud Development
Why would I want Gemini CLI on Azure? Isn’t that a Google Thing?
Yes- Gemini CLI leverages the Google Cloud console and Gemini models but it is also open source and platform agnostic. Many applications are already cross-cloud so this enables familiar tools to be run natively on Microsoft Azure.
Node Version Management
Gemini CLI needs a consistent, up to date version of Node. The nvm command can be used to get a standard Node environment:
Verifying Node.js in Azure VM
The version of Node.js is verified that it is recent enough to support Gemini CLI:
azureuser@azure-new:~$ nvm --version
0.40.3
azureuser@azure-new:~$ node --version
v25.8.1
azureuser@azure-new:~$ npm --version
11.11.0
Gemini CLI Installation
You can then download the Gemini CLI :
npm install -g @google/gemini-cli
You will see the log messages:
azureuser@azure-new:~/gemini-cli-azure$ npm install -g @google/gemini-cli
npm warn deprecated prebuild-install@7.1.3: No longer maintained. Please contact the author of the relevant native addon; alternatives are available.
npm warn deprecated node-domexception@1.0.0: Use your platform's native DOMException instead
npm warn deprecated glob@10.5.0: Old versions of glob are not supported, and contain widely publicized security vulnerabilities, which have been fixed in the current version. Please update. Support for old versions may be purchased (at exorbitant rates) by contacting i@izs.me
added 618 packages in 1m
173 packages are looking for funding
run `npm fund` for details
Testing the Gemini CLI Environment
Once you have all the tools and the correct Node.js version in place- you can test the startup of Gemini CLI. You will need to authenticate with a Key or your Google Account:
gemini
Authentication
Several authentication options are available. To use an existing Code Assist licence — authenticate with a Google Account:
> /auth
▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ │
│ ? Get started │
│ │
│ How would you like to authenticate for this project? │
│ │
│ ● 1. Login with Google │
│ 2. Use Gemini API Key │
│ 3. Vertex AI │
│ │
│ (Use Enter to select) │
│ │
│ Terms of Services and Privacy Notice for Gemini CLI │
│ │
│ https://geminicli.com/docs/resources/tos-privacy/ │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Then set the GOOGLE_CLOUD_PROJECT to a valid project setup on the Google Cloud console:
~ $ export GOOGLE_CLOUD_PROJECT=comglitn
~ $
Other options include Google Cloud API Key that can be generated directly from the Google Cloud Console.
Installing Google Cloud Tools
To simplify working with Google Cloud — install the Google Cloud Tools:
https://docs.cloud.google.com/sdk/docs/install-sdk
Once the installation is completed — you can verify the setup:
william@Azure:~$ gcloud auth list
Credentialed Accounts
ACTIVE ACCOUNT
* xbill@glitnir.com
Installing Azure Customized GEMINI.md
A sample GitHub repo contains tools for working with Gemini CLI on Azure. This repo is available here:
git clone https://gitHub.com/xbill9/gemini-cli-azure
A sample GEMINI.md customized for the Azure environment is provided in the repo:
You are a cross platform developer working with
Microsoft Azure and Google Cloud
You can use the Azure CLI :
https://learn.microsoft.com/en-us/cli/azure/install-azure-cli
https://learn.microsoft.com/en-us/cli/azure/
https://learn.microsoft.com/en-us/cli/azure/reference
https://learn.microsoft.com/en-us/cli/azure/install-azure-cli-linux?view=azure-cli-latest&pivots=apt
## Azure CLI Tools
You can use the Azure CLI to manage resources across Azure Storage, Virtual Machines, and other services.
- **List Resource Groups** : `az group list -o table`
- **List Storage Accounts** : `az storage account list -o table`
- **List Virtual Machines** : `az vm list -d -o table`
### Azure Update Script
- `azure-update`: This script is specifically for Azure Linux environments. It updates all packages and ensures necessary libraries are instal
led.
## Automation Scripts
This repository contains scripts for updating various Linux environments and tools:
- `linux-update`: Detects OS (Debian/Ubuntu/Azure Linux) and runs the corresponding update scripts.
- `azure-update`: Updates Azure Linux packages and installs necessary dependencies.
- `debian-update`: Updates Debian/Ubuntu packages and installs `git`.
- `gemini-update`: Updates the `@google/gemini-cli` via npm and checks versions of Node.js and Gemini.
- `nvm-update`: Installs NVM (Node Version Manager) and Node.js version 25.
Python MCP Documentation
The official GitHub Repo provides samples and documentation for getting started:
The most common MCP Python deployment path uses the FASTMCP library:
Where do I start?
The strategy for starting MCP development is a incremental step by step approach.
First, the basic development environment is setup with the required system variables, and a working Gemini CLI configuration.
Then, a minimal Hello World Style Python MCP Server was built with stdio transport. This server was validated with Gemini CLI in the local environment.
This current setup validates the connection from Gemini CLI to the local process via MCP. The MCP client (Gemini CLI) and the Python MCP server both run in the same local environment.
Next- the basic MCP server is extended with Gemini CLI to add several new tools in standard Python code.
Setup the Basic Environment
At this point you should have a working Python interpreter and a working Gemini CLI installation. The next step is to clone the GitHub samples repository with support scripts:
cd ~
git clone https://github.com/xbill9/gemini-cli-azure
Then run init.sh from the cloned directory.
The script will attempt to determine your shell environment and set the correct variables:
cd gemini-cli-azure
source init.sh
If your session times out or you need to re-authenticate- you can run the set_env.sh script to reset your environment variables:
cd gemini-cli-azure
source set_env.sh
Variables like PROJECT_ID need to be setup for use in the various build scripts- so the set_env script can be used to reset the environment if you time-out.
Hello World with HTTP Transport
One of the key features that the standard MCP libraries provide is abstracting various transport methods.
The high level MCP tool implementation is the same no matter what low level transport channel/method that the MCP Client uses to connect to a MCP Server.
The simplest transport that the SDK supports is the stdio (stdio/stdout) transport — which connects a locally running process. Both the MCP client and MCP Server must be running in the same environment.
The HTTP transport allows the MCP Client and Server to be in the same environment or distributed over the Internet.
The connection over HTTP will look similar to this:
mcp.run(
transport="http",
host="0.0.0.0",
port=port,
)
Running the Python Code
First- switch the directory with the Python MCP sample code:
cd ~/gemini-cli-aws/mcp-https-python-azure
make release
You can validate the final result by checking the messages:
╭──────────────────────────────────────────────────────────────────────────────╮
│ │
│ │
│ ▄▀▀ ▄▀█ █▀▀ ▀█▀ █▀▄▀█ █▀▀ █▀█ │
│ █▀ █▀█ ▄▄█ █ █ ▀ █ █▄▄ █▀▀ │
│ │
│ │
│ FastMCP 3.1.1 │
│ https://gofastmcp.com │
│ │
│ 🖥 Server: hello-world-server, 3.1.1 │
│ 🚀 Deploy free: https://fastmcp.cloud │
│ │
╰──────────────────────────────────────────────────────────────────────────────╯
[03/16/26 17:37:02] INFO Starting MCP server 'hello-world-server' with transport 'http' on http://0.0.0.0:8080/mcp transport.py:273
INFO: Started server process [3700]
INFO: Waiting for application startup.
{"message": "StreamableHTTP session manager started"}
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
Gemini CLI settings.json
The default Gemini CLI settings.json has an entry for the Python source:
{
"mcpServers": {
"azure-https-python": {
"httpUrl": "http://127.0.0.1:8080/mcp"
}
}
}
Validation with Gemini CLI
Leaver the MCP server window running. Start a new shell. Gemini CLI is restarted and the MCP connection over HTTP to the Python Code is validated, The full Gemini CLI Session will start:
azureuser@azure-new:~/gemini-cli-azure/mcp-https-python-azure$ gemini
azureuser@azure-new:~/gemini-cli-azure/mcp-https-python-azure$ gemini
▝▜▄ Gemini CLI v0.33.1
▝▜▄
▗▟▀ Logged in with Google /auth
▝▀ Gemini Code Assist Standard /upgrade
> /mcp list
Configured MCP servers:
🟢 azure-https-python - Ready (1 tool)
Tools:
- mcp_azure-https-python_greet
? for shortcuts
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
shift+tab to accept edits 3 GEMINI.md files | 1 MCP server
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
>
And you can then connect to the MCP Server over stdio:
> greet Azure VM!
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Action Required 1 of 2 │
│ │
│ ? greet (azure-https-python MCP Server) {"param":"HTTPS transport!"} │
│ │
│ MCP Server: azure-https-python │
│ Tool: greet │
│ │
│ MCP Tool Details: │
│ (press Ctrl+O to expand MCP tool details) │
│ Allow execution of MCP tool "greet" from server "azure-https-python"? │
│ │
│ 1. Allow once │
│ 2. Allow tool for this session │
│ 3. Allow all server tools for this session │
│ ● 4. Allow tool for all future sessions │
│ 5. No, suggest changes (esc) │
│ │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I will greet the HTTPS transport and the Azure VM HTTPS transport using the MCP tool.
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ greet (azure-https-python MCP Server) {"param":"HTTPS transport!"} │
│ │
│ HTTPS transport! │
│ │
│ ✓ greet (azure-https-python MCP Server) {"param":"Azure VM HTTPS transport!"} │
│ │
│ Azure VM HTTPS transport! │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I've sent the greetings for the HTTPS transport and the Azure VM HTTPS transport.
Summary
The strategy for using Python for MCP development with Gemini CLI was validated with a incremental step by step approach.
A minimal HTTP transport MCP Server was started from Python source code and validated with Gemini CLI running as a MCP client in the same local environment.
This approach can be extended to more complex deployments using other MCP transports and Cloud based options.
Top comments (0)