MCP Mastery: Unlock Next-Gen LLM Integrations with MCP

This is another step in my expanding from front-end development and architecture, into creating and deploying AI-powered tools. MCP Servers really intrigue me - they give LLMs access to specific knowledge bases and data sets that are maintained and updated elsewhere, so you’re not limited to the data that the model was trained on.

If you’re interested in a course like this, a few prerequisites will help:

• A cursory knowledge of python, as most of the courses use a python-based implementation (though MCP servers can be created/connected using several other languages)
• Check what coding IDE the tutorial will use, and whether you can access it. for example, this one demonstrates everything in Cursor, which I’m not allowed to install on my work-issued machine. So I watched the videos from the course on my work laptop while doing the exercises on my personal Mac.
• Check what platform the instructor is using. Though this one doesn’t get as deep as the LLM training course that I took, the instructor is using Windows where I was using a Mac, and if you’re unfamiliar with how to ‘translate’ what they’re talking about, you might seek a different path.
• Prepare to register for a lot of free API keys from various service providers. The signups can take some extra time, and I created a potential throwaway email address to use for them. This course had me sign up for a bunch of them (unit conversion, flight data, etc.) in order to follow along with the examples.
• knowing your way around github and how to create personal access tokens, and authorize oAuth applications, will be useful.

In addition to installing Cursor, I also found that I eventually ran out of ‘free’ model usage and I needed to sign up for the trial of Cursor’s “Pro” plan in order to finish the course. (and then I canceled before the billing for Pro kicked in - sorry Cursor, I don’t need another paid LLM-powered dev environment!)

Another example has you calling OpenAI APIs from a Google Collab notebook. this will require you to create an OpenAI account and fund it. I added $5 to mine, and turned off automatic re-billing when my tokens run out.

Overall, I’ve learned a lot about MCP servers from this course and I’m glad I took it. I can see this being useful not only for work projects but even for personal ones. I can essentially turn any data source into a tool that I can integrate into Cursor or Claude or VSCode w/Copilot.