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Gemini API with MCP Tool Integration

Gemini API with MCP Tool Integration

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github.com/hitechdkAI & Agents0Updated 82w ago
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About Gemini API with MCP Tool Integration

Gemini API with MCP Tool Integration is a Python example that combines Gemini with tools supplied through an MCP environment. It is for developers building natural-language applications that can invoke tools and return their results.

Key features

  1. 01Send natural-language prompts to Gemini
  2. 02Retrieve tools from an MCP session
  3. 03Process model tool calls
  4. 04Customize prompt and response handling

Use cases

  1. 01Add MCP tool actions to a Gemini application
  2. 02Route natural-language requests to tools
  3. 03Return responses that include tool results

MCP support

Connects to an MCP client session, retrieves available tools, and passes their definitions to Gemini so it can invoke them in response to prompts.

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Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

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

MIT License

Comments

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New to MCP?

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