Weather Ai Agent

This project demonstrates how to integrate Google Gemini API with custom tools managed by the MCP framework, process natural language queries, and use MCP tools to perform specific operations.

Artificial intelligence chatbots Developer tools #API integration #Multi - cloud platform #Natural language processing #Tool management .Python

rating : 2 points

downloads : 12

update time : 2025-04-29

What is the integration of Gemini API and MCP tools?

The integration of Gemini API and MCP tools allows users to interact with the system through natural language input, and the system will automatically call the corresponding tools to complete tasks.

How to use the integration of Gemini API and MCP tools?

Simply provide natural language instructions, and the system will automatically call the appropriate tools based on the instructions and return the results.

Applicable scenarios

Suitable for workflows that require quick response and automated processing, such as data analysis and file management.

Main features

Natural language processingSupport interaction with the system through natural language input.

Automatic tool callThe system can automatically call relevant MCP tools according to the user's intention.

Multi - cloud platform supportCompatible with multiple cloud environments to meet different business needs.

🚀 Google Gemini API Integration with MCP Tools Project Description

This project demonstrates how to integrate the Google Gemini API with custom tools managed by the Multi-Cloud Platform (MCP) framework. It leverages the Gemini API to handle natural language queries and uses MCP tools to perform specific operations based on the query intent.

🚀 Quick Start

Prerequisites

Before running this project, ensure the following conditions are met:

Python 3.7 or higher.
The Gemini API in your Google Cloud project is enabled, and you have obtained an API key.
The MCP environment is set up, and the necessary tools are installed.
Create a .env file in the project root directory and add the following environment variables:

GEMINI_API_KEY=<Your Gemini API Key>
GEMINI_MODEL=<Your Gemini Model Name>
MCP_RUNNER=<Path to the MCP Runner>
MCP_SCRIPT=<Path to the MCP Script>

📦 Installation

Clone the repository:

git clone <Repository URL>
cd <Repository Directory>

Create a virtual environment (recommended):

python3 -m venv venv
source venv/bin/activate  # On macOS/Linux

Install the required dependencies using uv:

uv pip install dotenv google-generativeai mcp
uv add "mcp[cli]" httpx
uv pip install python-dotenv google-generativeai mcp

Create a .env file in the project root directory and add your environment variables.

GEMINI_API_KEY=Your API Key
GEMINI_MODEL=gemini-pro
MCP_RUNNER=Path to the MCP Runner
MCP_SCRIPT=Path to the MCP Script

💻 Usage Examples

Basic Usage

To run the application, execute the following command:

python main.py

🔧 Technical Details

How It Works

The application loads and verifies the existence of environment variables.
It establishes a connection with the MCP client.
It retrieves the available tools in the MCP session.
It sends a prompt to the Gemini API along with tool definitions.
It processes any tool calls from the model.
It returns the final response, including the results of tool calls.

Customization Guide

To customize the prompt or behavior:

Modify the prompt variable to set the desired text.
Adjust the get_contents() function to change the prompt format.
Extend the process_response() function to handle different types of responses.