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., "@MCP Gemini CLI Basewhat's the current weather in New York City?"
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.
MCP Project Setup
This document outlines the steps to set up the mcp project environment.
1. Create Conda Environment
Create a new conda environment named mcp with Python 3.12:
conda create -n mcp python=3.12 -y2. Install uv
Install the uv package manager using the following command:
curl -LsSf https://astral.sh/uv/install.sh | sh3. Install fastmcp
Install the fastmcp package using uv in the mcp environment:
conda run -n mcp uv pip install fastmcp4. Verify Installation
Confirm the fastmcp installation by running the following command:
conda run -n mcp fastmcp version5. Running the Hello World Server
mcp_hello.py is a "hello world" type mcp server. You can run the MCP inspector for it using the following command:
conda run -n mcp fastmcp dev mcp_hello.py:mcp6. Connecting with Proxy Session Token
Copy the provided session token from CLI, click on the provided link, paste in Configuration -> Proxy Session Token, click connect.
7. Inspect hello_world tool
Click on Tools in the top menu bar. "hello_world" should be listed with a parameter "name". Input your name and click "Run Tool". The tool should succeed and return a greeting.
8. Running Resource Tests
mcp_resources.py defines MCP resources. You can run tests for these resources using the --test argument:
uv run mcp_resources.py --test9. Weather Server
mcp_weather.py exposes a tool to get current weather data from OpenWeatherMap.
Before running: Ensure you have set your OPENWEATHER_API_KEY in the .env file:
OPENWEATHER_API_KEY=YOUR_API_KEY_HERETo run the weather server manually:
conda run -n mcp fastmcp dev mcp_weather.py:mcpTo run tests for the weather tool:
uv run mcp_weather.py --test10. Integrating with Gemini CLI
To allow the Gemini CLI to automatically start and connect to your mcp_weather server, you need to configure its settings.json file.
Locate
settings.json: Thesettings.jsonfile is typically located at:Linux/macOS:
~/.gemini/settings.jsonWindows:
%APPDATA%\gemini\settings.json
If the file or directory does not exist, create them.
Add
mcpServersentry: Add the following entry to themcpServerssection in yoursettings.jsonfile. Replace/mnt/d/Projects/_sandbox/mcp/with the absolute path to yourmcpproject directory.{ "mcpServers": { "weather_server": { "command": "uv", "args": [ "run", "/mnt/d/Projects/_sandbox/mcp/mcp_weather.py" ], "cwd": "/mnt/d/Projects/_sandbox/mcp", "timeout": 10000 } } }Once configured, when you run
gemini, the CLI will automatically start yourmcp_weather.pyserver and make itsget_current_weathertool available to the Gemini model.
References
Gemini CLI Configuration: https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/configuration.md - For information on setting up MCP with the Gemini CLI.
FastMCP: https://github.com/jlowin/fastmcp - The FastMCP library used for building MCP servers.
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