Advanced MCP Server
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.
About Advanced MCP Server
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
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.
🚀 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
.envfor safe API key management.
🛠️ Getting Started
Prerequisites
- Python 3.10+
- uv (Recommended)
Installation
- Clone the repository:
git clone https://github.com/Rahii123/mcp.git cd mcp - 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
- Go to Railway.app and log in.
- Click + New Project > Deploy from GitHub repo.
- Select your
mcprepository.
3. Configure the Service
- Environment Variables:
- Go to the Variables tab in Railway.
- Add
NEWS_API_KEY:(Your actual NewsAPI Key)
- Start Command:
- Railway should automatically detect
pyproject.toml, but if needed, set the start command to:uv run server.py
- Railway should automatically detect
- Networking:
- Railway will automatically detect the port from the
$PORTenvironment variable. Ensure yourserver.pyis usingmcp.run(transport='sse')(I've already configured this for you).
- Railway will automatically detect the port from the
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
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