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Advanced MCP Server▌

by Rahii123

Advanced MCP Server: real-time NWS weather alerts, NewsAPI news search, and a safe local business directory for AI assis

Provides real-time weather alerts from the National Weather Service, news search capabilities via NewsAPI, and safe local directory exploration for AI assistants.

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    what it does

    Provides real-time weather alerts from the National Weather Service, news search capabilities via NewsAPI, and safe local directory exploration for AI assistants.

    about

    Advanced MCP Server is a community-built MCP server published by Rahii123 that provides AI assistants with tools and capabilities via the Model Context Protocol. Advanced MCP Server: real-time NWS weather alerts, NewsAPI news search, and a safe local business directory for AI assis It is categorized under file systems, developer tools.

    how to install

    You can install Advanced MCP Server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

    license

    MIT

    Advanced MCP Server is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

    readme

    🚀 Advanced MCP Server

    A professional Model Context Protocol (MCP) server built with Python and FastMCP. This server extends AI capabilities by providing real-time data and local system access.

    ✨ Features

    • 🌦️ Weather Alerts: Fetches active US weather alerts from the National Weather Service.
    • 📰 News Search: Real-time news searching using the NewsAPI.
    • 📁 Directory Explorer: Allows the AI to list and explore local system directories safely.
    • 🔐 Secure Secrets: Uses .env for safe API key management.

    🛠️ Getting Started

    Prerequisites

    • Python 3.10+
    • uv (Recommended)

    Installation

    1. Clone the repository: 
      git clone https://github.com/Rahii123/mcp.git
cd mcp
    2. Install dependencies: 
      uv sync

    Setup

    Create a .env file in the root directory and add your NewsAPI key:

    NEWS_API_KEY=your_actual_key_here

    🚀 Running the Server

    Run directly with uv:

    uv run server.py

    🧪 Testing Your Server

    We have provided two separate clients for testing:

    🏠 1. Local Testing (Stdio)

    Use this when you are developing on your own machine.

    uv run client_local.py

    This starts the server as a background process and communicates directly.

    🌐 2. Online Testing (SSE)

    Use this after you have deployed your server to the web (e.g., Railway).

    uv run client_online.py

    This asks for your deployment URL and connects over the internet.

    ☁️ Deployment to Railway (Step-by-Step)

    1. Push to GitHub

    Ensure all your changes are committed and pushed to your GitHub repository:

    git add .
git commit -m "Prepare for deployment"
git push origin main

    2. Connect to Railway

    1. Go to Railway.app and log in.
    2. Click + New Project > Deploy from GitHub repo.
    3. Select your mcp repository.

    3. Configure the Service

    1. Environment Variables:
      • Go to the Variables tab in Railway.
      • Add NEWS_API_KEY: (Your actual NewsAPI Key)
    2. Start Command:
      • Railway should automatically detect pyproject.toml, but if needed, set the start command to: 
        uv run server.py
    3. Networking:
      • Railway will automatically detect the port from the $PORT environment variable. Ensure your server.py is using mcp.run(transport='sse') (I've already configured this for you).

    4. Fetch your URL

    Once the build is finished, Railway will provide a public URL (e.g., https://mcp-production.up.railway.app). The MCP endpoint will be at: https://your-app-url.up.railway.app/sse