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Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteo…

@Karlheinzniebuhr

Repository

About Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteo…

No overview available yet

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"
      ]
    }
  }
}

Tools

No tools detected

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.

FAQ from Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas

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.

What credentials are required?

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

What tools does the weather server provide?

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

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.

What are the system requirements?

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

Frequently asked questions

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.

What credentials are required?

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

What tools does the weather server provide?

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

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.

What are the system requirements?

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

Comments

Basic information

Category

AI & Agents

Transports

stdio

Author

Karlheinzniebuhr

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