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Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteo…

@Karlheinzniebuhr

Repository

Basic information

Category

AI & Agents

Transports

stdio

Author

Karlheinzniebuhr

About Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas

No overview available yet

Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas config

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

{
  "mcpServers": {
    "MCP-Server-Client-Demo-with-Gemini": {
      "command": "python",
      "args": [
        "mcp_gemini_client.py",
        "weather_tool_server.py"
      ]
    }
  }
}

Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas 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 Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas?

This project demonstrates how to use the Model Context Protocol (MCP) to connect a Google Gemini AI client with a specialized server that provides weather tools (get_alerts, get_forecast). It acts as a bridge between the AI and external data, handling user input and tool execution.

How to use Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas?

Install dependencies with pip install -r requirements.txt, create a .env file with a Gemini API key (GEMINI_API_KEY=your_key), then run python mcp_gemini_client.py weather_tool_server.py. Type weather queries in the terminal when prompted.

Key features of Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas

  • Gemini AI decides when to call weather tools.
  • Standardized MCP communication between client and server.
  • Two weather tools: get_alerts and get_forecast.
  • Detailed logging of all interactions in mcp_log.txt.
  • Lightweight setup with minimal dependencies.

Use cases of Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas

  • Ask for the current weather forecast in natural language.
  • Request weather alerts for a specific location.
  • Learn how MCP connects AI agents with external tools.
  • Debug AI-tool interaction flow using comprehensive logs.

Frequently asked questions

1

What is the Model Context Protocol (MCP)?

MCP is the standardized communication protocol that lets the client discover and call tools on the server, handling messaging between both sides.

2

What credentials are required?

A Gemini API key from Google AI Studio must be saved in a `.env` file as `GEMINI_API_KEY`.

3

What tools does the weather server provide?

Two tools: `get_alerts` and `get_forecast`, each with their own schema and description.

4

How do I see the full communication flow?

A detailed log is written to `mcp_log.txt`, showing every message between the client, Gemini API, and server, including prompts and tool results.

5

What are the system requirements?

Python 3 with packages google-generativeai, python-dotenv, and the MCP protocol libraries (installed via requirements.txt).

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