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Build an MCP Server in 3 Steps

...without writing any code.

🔥Turn ANY website into LLM-ready data (37k stars)

AI systems love neatly formatted data—Markdown, Structured data, HTML, etc.

And now it is easier than ever to produce LLM-digestible data!

Firecrawl is a framework that takes a URL, crawls it, and converts it into a clean markdown or structured format.

FireCrawl GitHub

Why Firecrawl?

  • LLM-ready formats → Markdown, HTML, Structured data, metadata.

  • Handles the hard stuff → proxies, anti-bots, dynamic content.

  • Customizable → exclude tags, custom headers, max depth.

  • Reliable → gets the data you need, no matter what.

  • Batching → scrape thousands of URLs at once

  • Media parsing → PDFs, DOCX, images

  • Actions → click, scroll, input, wait.

If you prefer FireCrawl’s managed service, you can use the code “DDODS” for a 10% discount code here →

Thanks to Firecrawl for partnering with us today!


Build an MCP Server in 3 Steps

We found the easiest way to build an MCP server.

Just follow these 3 steps:

  • Use Gitingest to convert the entire FastMCP repo into LLM-ready text.

  • Download the text file.

  • Upload it to Google AI Studio and specify the type of MCP server you want to build.

That's all!

Gemini 2.5 Pro builds it for you.

We have attached a video walkthrough at the top!

If you don't know about MCP servers, we covered them recently in the newsletter here:

Thanks for reading!


P.S. For those wanting to develop “Industry ML” expertise:

At the end of the day, all businesses care about impact. That’s it!

  • Can you reduce costs?

  • Drive revenue?

  • Can you scale ML models?

  • Predict trends before they happen?

We have discussed several other topics (with implementations) that align with such topics.

Develop "Industry ML" Skills

Here are some of them:

  • Learn sophisticated graph architectures and how to train them on graph data.

  • So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.

  • Learn how to run large models on small devices using Quantization techniques.

  • Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.

  • Learn how to identify causal relationships and answer business questions using causal inference in this crash course.

  • Learn how to scale and implement ML model training in this practical guide.

  • Learn techniques to reliably test new models in production.

  • Learn how to build privacy-first ML systems using Federated Learning.

  • Learn 6 techniques with implementation to compress ML models.

All these resources will help you cultivate key skills that businesses and companies care about the most.

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