Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas

Name: Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas
Author: Karlheinzniebuhr

@Karlheinzniebuhr

リポジトリ

Proyecto Cliente MCP con Gemini y Servidor de Herramientas Meteorológicas について

概要はまだありません

基本情報

カテゴリ

AI とエージェント

ランタイム

python

トランスポート

stdio

公開者

Karlheinzniebuhr

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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).

「AI とエージェント」の他のコンテンツ

Perplexity Ask MCP Server

ppl-ai

The official MCP server implementation for the Perplexity API Platform

2.4K

Shell and Coding agent for Claude and other mcp clients

rusiaaman

Shell and coding agent on mcp clients

666

Gemini MCP Server

aliargun

MCP server implementation for Google's Gemini API

259

meGPT - upload an author's content into an LLM

adrianco

Code to process many kinds of content by an author into an MCP server

287

mcp-hfspace MCP Server 🤗

evalstate

MCP Server to Use HuggingFace spaces, easy configuration and Claude Desktop mode.

387