Gemini API with MCP Tool Integration
About
AI agent that retrieves weather data from the MCP server to provide automated forecasts. Ideal for integration into weather-related applications.
Details
- Author
- hitechdk
- Downloads
- 329
- Categories
- AI
Jump to
- 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
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Gemini API with MCP Tool IntegrationCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"gemini api with mcp tool integration": {
"weather-ai-agent": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"weather-ai-agent": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}Gemini API with MCP Tool Integration
This project demonstrates how to integrate the Google Gemini API with custom tools managed by the MCP (Multi-Cloud Platform) framework. It uses the Gemini API to process natural language queries, and leverages MCP tools to execute specific actions based on the query's intent.
Prerequisites
Before running this project, ensure you have the following:
Python 3.7 or higher
A Google Cloud project with the Gemini API enabled and an API key.
An MCP environment set up with the necessary tools.
.env file with the following environment variables:
GEMINI_API_KEY=<your_gemini_api_key>
GEMINI_MODEL=<your_gemini_model_name>
MCP_RUNNER=<path_to_mcp_runner>
MCP_SCRIPT=<path_to_mcp_script>
Installation
1. Clone the repository:
git clone <repository_url>
cd <repository_directory>
2. Create a virtual environment (recommended):
python3 -m venv venv
source venv/bin/activate # On macOS/Linux
3. 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
4. Create a .env file in the project root and add your environment variables.
GEMINI_API_KEY=your_api_key_here
GEMINI_MODEL=gemini-pro
MCP_RUNNER=path_to_mcp_runner
MCP_SCRIPT=path_to_mcp_script
Usage
To run the application, execute the following command:
python main.py
How It Works
1. The application loads environment variables and validates their presence
2. Establishes a connection with the MCP client
3. Retrieves available tools from the MCP session
4. Sends the prompt to Gemini's API along with tool definitions
5. Processes any tool calls made by the model
6. Returns the final response that includes results from tool calls
Customization
To customize the prompt or behavior:
1. Modify the prompt variable with your desired text
2. Adjust the get_contents() function to change how prompts are formatted
3. Extend process_response() to handle different response types
License
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