FeedDiscussion
Mateo Ruiz
Senior Tech Consultant
Really enjoyed the way you explained MCP through the “USB-C moment for AI tools” analogy. The part about having to build the same integration repeatedly before a common standard existed really makes the value of MCP easy to understand.
What’s interesting is how this could extend beyond developer tools too. As AI systems become more connected to real-world business data, having structured and reliable information available to those systems becomes increasingly important. That’s one of the areas I find interesting with Oglas AI making business information easier to discover and use in AI-driven search and recommendation experiences.
Feels like MCP could be one of the pieces that helps move AI from simply generating answers to actually connecting with the information behind them.
The “adopt MCP when you’d otherwise write the same adapter twice” rule is probably the most practical takeaway here.
One thing I’d add from a production architecture perspective: MCP also creates a new boundary that needs to be treated like an API contract, not just a tool interface. Tool schemas, permissions, timeouts, idempotency, and result validation all become part of that contract.
The standard can remove a lot of integration glue, but it doesn’t remove the engineering responsibility around that boundary. That’s where the real production complexity starts.