Building MCP Client with Google Gemini and Python!
Clone from Github
https://github.com/theailanguage/mcp_...
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00:00 Introduction
00:13 1 minute preview - what we’ll build
01:59 Please subscribe for AI, Agents and more
02:12 Overview of MCP
02:47 Why use Gemini?
03:18 Objective
03:38 Setup Project
05:53 Get Gemini API Key
07:17 MCP client.py Code Walkthrough
07:43 Client.py Imports
08:51 MCPClient Class
09:23 Connect to Server
13:42 Process Query
17:08 Chat Loop, Cleanup, main functions
17:59 Test your MCP client
🚀 MCP Client with Gemini AI
This is an MCP (Model Context Protocol) client that connects to an MCP server and integrates with Google’s Gemini AI.
The client sends user queries to Gemini, allows Gemini to call external tools from the MCP server, executes them, and keeps track of the conversation history.
🔹 MCP (Model Context Protocol) allows AI models to interact with external tools and fetch information dynamically.
🔹 Google’s Gemini AI processes user queries and calls MCP tools when needed.
🔹 This client keeps a history of interactions so Gemini can remember past commands.
📌 Features
✅ Connects to an MCP server (Python or Node.js)
✅ Sends queries to Google Gemini AI
✅ Lets Gemini call external tools from the MCP server
✅ Executes MCP tool commands and returns the results
✅ Maintains conversation history, so Gemini remembers past queries
📦 Installation
1️⃣ Install the required dependencies using uv (Universal Virtualenv):
uv add mcp python-dotenv google-genai
2️⃣ Clone the repository:
git clone https://github.com/your-username/mcp-...
cd mcp-client-gemini
3️⃣ Set up the project and virtual environment:
uv init mcp-client
cd mcp-client
uv venv
4️⃣ Activate the virtual environment:
On Windows:
.venv\Scripts\activate
On MacOS/Linux:
source .venv/bin/activate
🔑 Setting Up the API Key
To use Google Gemini AI, you need an API key.
1️⃣ Create a .env file:
touch .env
2️⃣ Add your API key inside .env:
GEMINI_API_KEY=your_api_key_here
3️⃣ Make sure .env is ignored in Git:
echo ".env" {angled brackets} .gitignore
🚀 Running the MCP Client
Start the MCP client and connect it to an MCP server:
uv run client.py path/to/server.py # Use a Python server
uv run client.py path/to/server.js # Use a Node.js server
Example (if using a weather server):
uv run client.py ./server/weather.py
🔧 How It Works
1️⃣ The user enters a query (e.g., "Create a file named test.txt").
2️⃣ The MCP client sends the query to Gemini AI.
3️⃣ Gemini AI checks available MCP tools and calls the correct one.
4️⃣ The MCP client executes the command and returns the result.
5️⃣ Gemini remembers past interactions and adjusts responses accordingly.
📁 Project Structure
mcp-client-gemini/
│── client.py # MCP Client (Main script)
│── .env # Stores API Key (ignored in Git)
│── README.md # Documentation
│── requirements.txt # Dependencies (optional)
│── server/ # Folder for MCP server scripts (e.g., weather.py)
│── .gitignore # Ignores sensitive files
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🎯 Contributing
Feel free to submit issues or contribute improvements via pull requests.
🚀 Now you're ready to build your own MCP client with Gemini AI!
Let me know if you have any questions in the comments! 😊🎯
