🌍 Global News MCP Server

A Model Context Protocol (MCP) powered global news application that collects news from multiple countries and categories using RSS feeds and exposes them as reusable MCP tools.

The project contains:

• 🧠 MCP Server for news intelligence

• 🌐 Streamlit web interface

• 📰 RSS-based news aggregation

• 🔎 Global news search

• 🌍 Country-based filtering

• 💻 Technology and science news categories

No API keys are required.

✨ Features

🌎 Global News

Fetch worldwide news from trusted RSS sources:

• BBC World

• The Guardian

Related MCP server: News Aggregator MCP Server

🌍 Country News

Get news by country:

💻 Technology News

Technology updates from:

• TechCrunch

• The Verge

🚀 Science News

Science and space updates from:

• NASA

• Science Daily

🔎 Global Search

Search news articles across multiple RSS feeds.

Examples:

Artificial Intelligence
Space
Climate Change
Robotics
Technology

🏗️ Architecture

                    User
                     |
                     |
              Streamlit UI
                     |
                     |
                MCP Client
                     |
                     |
                MCP Server
                     |
          -----------------------
          |                     |
    News Service            MCP Tools
          |
          |
     RSS Feed Sources
          |
          |
    Structured JSON Response

🔌 MCP Tools

The MCP Server exposes these tools:

get_global_news()

Returns latest worldwide news.

Example response:

[
 {
  "source": "BBC World",
  "title": "Latest World News",
  "summary": "News summary",
  "link": "article url"
 }
]

get_country_news(country)

Fetch news from a specific country.

Example:

get_country_news("India")

Returns:

[
 {
  "source": "The Hindu",
  "title": "India News",
  "summary": "...",
  "link": "..."
 }
]

get_technology_news()

Returns technology-related articles.

Sources:

• TechCrunch

• The Verge

get_science_news()

Returns science-related articles.

Sources:

• NASA

• Science Daily

search_news(keyword)

Searches across available RSS feeds.

Example:

search_news("AI")

📂 Project Structure

news-mcp/

│
├── app.py                    # Streamlit frontend
│
├── requirements.txt          # Dependencies
│
├── mcp_server/
│   │
│   ├── __init__.py
│   ├── server.py             # MCP server and tools
│   └── news_service.py       # RSS processing logic
│
└── components/
    │
    ├── __init__.py
    └── cards.py              # News UI components

🛠️ Tech Stack

Backend

• Python 3.11

• Model Context Protocol (MCP)

• FastMCP

• Feedparser

Frontend

• Streamlit

Data Sources

RSS feeds from:

• BBC

• The Guardian

• The Hindu

• CNN

• CBC

• DW

• France24

• TechCrunch

• NASA

• Science Daily

⚙️ Installation

Clone Repository

git clone https://github.com/kevin-arock/news-mcp.git

Navigate:

cd news-mcp

Create Virtual Environment

python -m venv venv

Activate environment.

Windows

venv\Scripts\activate

Linux / Mac

source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

▶️ Run Application

Start Streamlit:

streamlit run app.py

Application opens at:

http://localhost:8501

🔄 Application Flow

User selects news category
            |
            |
      Streamlit Interface
            |
            |
        MCP Client
            |
            |
        MCP Server
            |
            |
       News MCP Tools
            |
            |
       RSS News Sources
            |
            |
      JSON News Response
            |
            |
        UI Cards

💡 Why MCP?

Instead of directly connecting the UI to data sources, MCP creates a reusable tool layer.

Advantages:

✅ AI agents can discover and call tools
✅ Backend capabilities are separated from UI
✅ Multiple AI clients can use the same tools
✅ External data sources can be integrated easily

🎯 Interview Overview

"I built a Global News MCP Server that exposes news retrieval capabilities as MCP tools. The server collects RSS feeds from different countries, processes them into structured JSON responses, and allows a Streamlit MCP client to consume those tools. This demonstrates how AI applications can use MCP as a standard way to connect with external capabilities."

🚀 Future Improvements

Planned enhancements:

• 🤖 AI-powered article summarization

• 🌐 Multi-language translation

• 🧠 Semantic search using embeddings

• 📊 News sentiment analysis

• 🔔 Personalized news recommendations

• 💬 Chat-based AI news assistant

• 🔗 Integration with AI clients supporting MCP

👨‍💻 Author

Arockia Kevin J

GitHub:

https://github.com/kevin-arock

⭐ Support

If you like this project, consider giving it a ⭐ on GitHub.