Advanced MCP Server
This Advanced MCP Server provides AI assistants with real-time data access and local system exploration capabilities through three main tools:
๐ฆ๏ธ Weather Alerts - Fetch active weather alerts for any US state using two-letter state codes (e.g., 'CA', 'NY', 'TX') via the National Weather Service
๐ฐ News Search - Search for recent news articles on specific topics using NewsAPI with real-time results
๐ Directory Explorer - List and explore local filesystem directories safely, defaulting to the current directory
Deployment & Testing: Can be deployed to cloud services like Railway with secure API key management via environment variables. Includes both local stdio testing and online SSE testing options for different development scenarios.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Advanced MCP Servershow me active weather alerts for California"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
๐ 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.
โจ Features
๐ฆ๏ธ Weather Alerts: Fetches active US weather alerts from the National Weather Service.
๐ฐ News Search: Real-time news searching using the NewsAPI.
๐ Directory Explorer: Allows the AI to list and explore local system directories safely.
๐ Secure Secrets: Uses
.envfor safe API key management.
๐ ๏ธ Getting Started
Prerequisites
Python 3.10+
uv (Recommended)
Installation
Clone the repository:
git clone https://github.com/Rahii123/mcp.git cd mcpInstall dependencies:
uv sync
Setup
Create a .env file in the root directory and add your NewsAPI key:
NEWS_API_KEY=your_actual_key_here๐ Running the Server
Run directly with uv:
uv run server.py๐งช Testing Your Server
We have provided two separate clients for testing:
๐ 1. Local Testing (Stdio)
Use this when you are developing on your own machine.
uv run client_local.pyThis starts the server as a background process and communicates directly.
๐ 2. Online Testing (SSE)
Use this after you have deployed your server to the web (e.g., Railway).
uv run client_online.pyThis asks for your deployment URL and connects over the internet.
โ๏ธ 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 main2. 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
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).
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
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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