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Gemini API with MCP Tool Integration

@hitechdk

Repository

Basic information

Category

AI & Agents

License

MIT license

Transports

stdio

Author

hitechdk

About Gemini API with MCP Tool Integration

AI agent that retrieves weather data from the MCP server to provide automated forecasts. Ideal for integration into weather-related applications.

#weather-ai#weather-forecast#gemini-api

Gemini API with MCP Tool Integration config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "weather-ai-agent": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Gemini API with MCP Tool Integration tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

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

Frequently asked questions

1

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.

2

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.

3

How do I install the required dependencies?

Use `uv` to install `dotenv`, `google-generativeai`, `mcp`, and other packages as listed in the README.

4

How do I run the application?

After setting up the `.env` file, execute `python main.py` from the project root.

Comments

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