And this is the direct URL to the console:
https://lightsail.aws.amazon.com/ls/webapp/home/containers
Amazon Lightsail also provides managed Instances:
Virtual private server instances in Lightsail
AWS CLI
The AWS CLI provides a command line tool to directly access AWS services from your current environment. Full details on the CLI are available here:
Install Docker, AWS CLI, and the Lightsail Control plugin for containers
Lightsail Control Plugin
The Lightsail Plugin allows the AWS CLI tools to interact with Lightsail. The full GitHub repo is available here:
GitHub - aws/lightsailctl: Amazon Lightsail CLI Extensions
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 is built with HTTP transport. This server is validated with Gemini CLI in the local environment.
This 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 MCP server is wrapped in a container with docker and deployed to Amazon Lightsail Instances. This remote deployment is validated with Gemini CLI running as a MCP client.
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-aws
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-aws
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-aws
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 run 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-lightmi-python-aws
Refresh the AWS credentials:
xbill@penguin:~/gemini-cli-aws/mcp-lightmi-python-aws$aws login --remote
xbill@penguin:~/gemini-cli-aws/mcp-lightmi-python-aws$source save-aws-creds.sh
Exporting AWS credentials...
Successfully saved credentials to .aws_creds
The Makefile will now automatically use these for deployments.
Run the deploy version on the local system:
xbill@penguin:~/gemini-cli-aws/mcp-lightmi-python-aws$make deploy
Creating Lightsail instance mcp-vps-python-aws...
Instance already exists or creation in progress.
Waiting for instance mcp-vps-python-aws to reach 'running' state...
Instance is running.
make[1]: Entering directory '/home/xbill/gemini-cli-aws/mcp-lightmi-python-aws' 0.0s
You can validate the final result by checking the messages:
xbill@penguin:~/gemini-cli-aws/mcp-lightmi-python-aws$make status
Checking AWS Lightsail container service status for mcp-lightsail-python-aws...
No container service found.
Checking AWS Lightsail instance status for mcp-vps-python-aws...
---------------------------------------------------
| GetInstance |
+---------------------+----------------+----------+
| Name | PublicIp | State |
+---------------------+----------------+----------+
| mcp-vps-python-aws | 13.218.126.2 | running |
+---------------------+----------------+----------+
You can then get the endpoint:
Running make endpoint now returns the functional URL:
http://13.218.126.2:8080
>make endpoint
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Shell make endpoint │
│ │
│ http://13.218.126.2:8080 │
│ │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ http://13.218.126.2:8080
The service will be visible in the AWS console:
Gemini CLI settings.json
Once you have the deployed endpoint — update the Gemini CLI MCP settings:
{"mcpServers":{"aws-lightsailmi-python":{"httpUrl":"http://13.218.126.2:8080/mcp"}}}
Remote MCP Server Testing
Restart Gemini CLI and check for the new MCP tools:
xbill@penguin:~/gemini-cli-aws/mcp-lightmi-python-aws$gemini
▝▜▄ Gemini CLI v0.40.1
▝▜▄
▗▟▀ Signed in with Google /auth
▝▀ Plan: Gemini Code Assist Standard /upgrade
🟢 aws-lightsailmi-python - Ready (1 tool)
Tools:
- mcp_aws-lightsailmi-python_greet
Then the remote MCP tool in Lightsail can be called:
>greet hello lightsail instances
Greeting Lightsail Instances: Greeting the Lightsail instances using the greet tool.
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ greet (aws-lightsailmi-python MCP Server) {"param":"hello lightsail instances"} │
│ │
│ hello lightsail instances │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ The greet tool returned: "hello lightsail instances"
Gemini CLI can also summarize the deployment:
> summarize the deployment
✦ The project is currently deployed to an Amazon Lightsail Managed Instance (VPS).
Current Status
* Instance Name: mcp-vps-python-aws
* Public IP: 13.218.126.2
* State: Running
* Container Service: None active (Container deployment is supported but not currently in use).
Deployment Architecture
* Host OS: Amazon Linux 2023.
* Runtime: Python MCP server running as a systemd service (mcp-server).
* Port: 8080 (HTTP).
* Automation: Orchestrated via Makefile (e.g., make instance-deploy, make instance-sync).
Summary
The strategy for using Python for MCP development with Gemini CLI on Amazon Lightsail Instances was validated with a incremental step by step approach.
This MCP server was then deployed to Amazon Lightsail Instances. The local copy of Gemini CLI was used as a MCP client to validate the connection.
This approach can be extended to more complex deployments using other MCP transports and Cloud based options.