If you’ve ever tried to plug an LLM into a real-world system; whether it’s Salesforce, Oracle, Uber, or even a simple internal tool, you know the pain. Every platform speaks a different language, every API is its own adventure. As someone who’s been in the trenches building with AI, I’ve seen firsthand how slow and messy these integrations can get.

That’s why I’ve been paying close attention to the Model Context Protocol (MCP). Introduced by Anthropic in late 2024, MCP is basically trying to do for AI agents what USB-C did for hardware: make everything plug-and-play. It’s an open protocol that lets AI systems talk to other software in a consistent, secure, and modular way.

Here’s my take; what excites me, what worries me, and why it might (or might not) become the middleware layer we’ve been waiting for.

Why MCP Feels Like a Game-Changer

MCP wants to become the standard interface between AI agents and external tools. Think about an agent that books an Uber, reads your support tickets, pulls CRM data from Salesforce, and schedules meetings; all without custom wiring for each app.

Here’s why it’s promising:

• It’s modular: You can swap out the backend system without breaking your AI logic. Just like how GraphQL or REST changed how frontends talk to servers, MCP could standardize the “how” in AI.

• It saves time: Companies like Replit and Sourcegraph have said it took them under an hour to integrate MCP. That’s huge.

• It’s secure: Since it’s standardized, you get consistent logging, permissions, and governance out of the box.

OpenAI backing it is a big deal. They’ve already added MCP to their Agents SDK and plan to support it across the ChatGPT desktop app and their Responses API. That’s like Apple saying they’re shipping USB-C; everyone pays attention.

The MuleSoft Parallel

This reminds me of how MuleSoft grew. MuleSoft didn’t start by being flashy—it was just a really good way to connect enterprise systems. Eventually, it became a category-defining platform and was acquired by Salesforce for $6.5 billion in 2018. MCP has similar vibes. It’s not trying to “wow” users, it’s trying to make developers’ lives easier. And if it does that well, it could build a durable ecosystem of its own.

But Here’s Where It Gets Complicated

MCP sounds great in theory, but there are real challenges:

• The ‘Lowest Common Denominator’ trap: When you try to standardize across vastly different systems, you often lose the depth of what makes each system special. As Steve Jobs said about Flash, abstraction can come at the cost of capability.

• Why would platforms play along? Instacart doesn’t want to be a “dumb pipe” for an AI agent. It makes money from ads and upsells. Uber wants you in their app so they can nudge you into a Black car. Salesforce is pushing its own AI tools. Letting external agents control the UX means giving up revenue and user ownership. That’s not an easy sell.

• It's still Anthropic’s show: Even though it’s an open protocol, it’s driven by Anthropic. What happens if OpenAI or Google starts adding their own tweaks? We’ve seen this before—the moment the standard forks, adoption stalls.

What Needs to Happen Next

If MCP is going to succeed, we need:

• A true community model: Not just Anthropic steering the ship. Other players need a say.

• Better tooling: Reference implementations, sandboxes, and tutorials so developers can get started fast.

• Early wins: Real-world success stories—ideally beyond developer tools—are crucial.

I'm already running MCP in a sandbox. It’s not in production yet, but even at this stage, the ease of integration is obvious. The insights I’m gathering are already shaping how I think about future architecture and tooling.

Final Thoughts

MCP couldn’t have come at a better time. We’re all trying to make agents smarter and more useful; but until they can reliably interact with external systems, we’re stuck in demo land. MCP offers a path forward.

But it’s not guaranteed. Middleware wins only if everyone agrees to play by the same rules. Otherwise, we’re back to custom bridges and broken connectors.

Still, this feels like a moment. And whether MCP becomes the standard; or simply kickstarts the race to build one; we’ll look back at 2024–2025 as the beginning of the AI middleware era.

If you’re building agents, it’s worth getting your hands dirty. This protocol might not be perfect; but it’s real, it’s working, and it’s probably not going away.

This is the time for "testing" :)