Gemini API with MCP Tool Integration
@hitechdk
AI agent that retrieves weather data from the MCP server to provide automated forecasts. Ideal for integration into weather-related applications.
Overview
What is Gemini API with MCP Tool Integration?
This project integrates the Google Gemini API with custom tools managed by the MCP (Multi-Cloud Platform) framework. It processes natural language queries via Gemini and executes specific actions based on query intent using MCP tools.
How to use Gemini API with MCP Tool Integration?
Install Python 3.7+, set up a Google Cloud project with Gemini API enabled, configure a .env file with GEMINI_API_KEY, GEMINI_MODEL, MCP_RUNNER, and MCP_SCRIPT, then run python main.py. Customize prompt, get_contents(), and process_response() as needed.
Key features of Gemini API with MCP Tool Integration
- Integrates Google Gemini API with MCP custom tools
- Uses environment variables for configuration
- Processes tool calls made by the model
- Supports customizable prompt and response handling
- Automates actions based on natural language queries
Use cases of Gemini API with MCP Tool Integration
- Automating cloud tasks through natural language commands
- Building AI assistants that trigger external tools
- Prototyping applications combining Gemini with MCP-based services
- Enabling conversational interfaces with backend action execution
FAQ from Gemini API with MCP Tool Integration
What is the purpose of this integration?
It demonstrates how to combine the Gemini API with MCP framework tools to interpret natural language and perform corresponding actions.
What are the prerequisites?
Python 3.7+, a Google Cloud project with the Gemini API enabled, an API key, and an MCP environment with the necessary tools.
How do I install the required dependencies?
Use uv to install dotenv, google-generativeai, mcp, and other packages as listed in the README.
How do I run the application?
After setting up the .env file, execute python main.py from the project root.
