AI versus Marshy - Exploring MCPs and Deploying Them with Claude Desktop

I’ve been exploring the world of AI and automation, and I wanted to dive deeper into Model Context Protocol (MCP). MCP is a new set of rules for helping Large Language Models (LLMs) talk to other software. I’ve been following its development, but I recently decided to take a closer look with Claude Desktop. If Japanese is your native dialect, for other people to interact with you fully - they need to speak Japanese as well. But unlike the language analogy, MCPs improve over time as more and more people use them, and the data going in and out of an MCP increases. It’s a standard, and as the standard increases so has interest. That be a chart I’ve avoided the hype up until recently, but with Claude’s capabilities getting better and better, it was time to see what the fuss is about. I installed a number of MCPs (official and unofficial) to see what I could do. So here’s 3 I tested: Apify Airtable n8n Sidebar: I tested these with Claude Desktop and instructions on how to do this are here . There are other (and much more secure) ways to set up connections so do your own research or wait until the tech becomes more accessible. Apify is a scraping tool that has a number of “actors” that will run basic through to complex scraping requests either as one-off runs or on an ongoing schedule. Originally appeared in newsletter : AI versus Marshy 61 - MCPs, LinkedIn insights, and webinars