MCP integration. An example of scrapping the web

Goal

Build a tool with the MCP (Model Context Protocol) where an AI agent scrapes the latest news headlines. It should use either a topic or a keyword as a touchpoint.

The agent checks up-to-date headlines from real sources. It doesn’t rely on outdated training data.

Step-by-Step MCP Integration: Live News Scraper Tool

Choose the Data Source

• Pick a public news API. Make sure to use a reliable public API (e.g. NewsAPI, GNews, Bing News Search).

Another option,

• Build a basic web scraper with BeautifulSoup or Playwright. Furthermore, it targets Google News, Bing, or another websites.

Build the MCP Server

1. Install an open MCP Software Development Kit. It has everything for your MCP project.
2. Install Zod for Data validation. It checks the result that the server receives from the sources.
3. Create a tool to serve as the foundation for your news source and the AI agent.

Little breakdown:

• AI sees requests like: “Find the latest news on climate change,”.
• It verifies data.
• The tool returns a list of headlines and links from your chosen source.

Add Authentication. Secure the Server

1. Protect your MCP tool from spam.
2. Use API keys as authentication.
3. Apply rate limits.
4. Ensure only authorized agents can access it.

Register in Copilot/Agent Platform

1. Use an agent platform that supports MCP, for example, Microsoft Copilot Studio or Claude Pro.
2. Provide the MCP server address to an agent.
3. Register your news-reading tool.
4. Add a short description to it.

For example:

“What’s trending in renewable energy this morning?” The agent sends MCP-formatted request to your server. Аnd, it uses the response in its reply. The AI will pass that to your tool and return real-time news. ***

Test Use Cases

1. Test user prompts.
2. Make sure: the tool returns true and related news. Response is quick. Sources are credible and current ones.

This step is key. You can provide the project to users after that.

Add Features Over Time

1. Filter news by language or country.
2. Automatically summarize long articles.
3. Cache repeated results to reduce API costs and increase productivity.

Drawbacks

• Usage limits. Most news APIs limit how many requests you can make per day.
• Cost. An extra payment is required to increase usage limits.
• Latency. A couple seconds of delays take place.
• Limited tool chaining. Most agents only use one tool at a time. You can not find news and summarize it in a PDF in the same step.
• Context size. One reply has limits. Too much data can overflow the LLM.
• Security risks. Malicious inputs could manipulate your server. Also, fake search results appear.

Benefits

• Real-time accuracy. Users receive fresh data.
• User-level control. Anyone can ask for news on any topic.
• MCP-compatible AI agent can use the tool instantly.
• Reusable tool. A Single well-built MCP server can support multiple agents and clients.
• No retraining required. You don’t need to fine-tune models. The data is always current.

Conclusion

MCP is a breakthrough technology. It satisfies the needs of every user, from professionals to beginners. It is a universal base for AI development. It simplifies AI to get current information.