MCP Is Becoming the USB-C for AI Agents: This Matters More Than You Think

I took on a consulting project this week. The client wanted to build an internal AI Agent system, and as soon as we discussed requirements, my head started hurting—they had seven or eight existing business systems, each with completely different interfaces. Getting AI to connect and orchestrate these systems would require months just writing adapter layers.

So I threw the MCP protocol at them.

MCP stands for Model Context Protocol, an open-source protocol from Anthropic. Simply put, it aims to define a universal “plug standard” for AI Agents—allowing AI to connect to external data sources, tools, and APIs in a standardized way, without writing custom adapter code for each platform.

It sounds technical, but the implications are huge. Modern AI apps have a common pain point: models keep getting stronger, but their ability to interact with the real world remains fragmented. Want AI to query your database, call an API, or manipulate a file system? Each scenario requires custom development. MCP targets this “last mile” problem.

What impressed me most is MCP’s ecosystem growth.

Anthropic open-sourced this protocol just a few months ago, and GitHub already has hundreds of MCP servers—from GitHub, Slack, and Notion to Postgres and MySQL, even Blender and Figma. What does this mean? You can theoretically build an AI Agent that “plug and plays” with these systems instead of writing integration code from scratch.

And this integration is bidirectional. MCP servers let AI read data (querying databases) and execute actions (sending messages, creating files). This evolves Agents from “just chatting” to “actually getting things done.”

Of course, as a picky engineer, I must mention its limitations.

MCP’s biggest current issue—it’s still very much an “Anthropic thing.” While the protocol is open-source, ecosystem building mainly relies on Anthropic’s push. Other major model providers’ support is inconsistent. OpenAI has Function Calling, Google has its own tool-calling approach. For MCP to become a true industry standard, it needs broader vendor buy-in.

Also, MCP’s design philosophy is “standardization,” but real-world needs are often “custom.” Some complex business logic can’t be expressed through MCP’s standard interfaces and requires falling back to traditional API calls.

That said, I still want to give MCP credit.

In the AI Agent space, we desperately need this kind of infrastructure-level standardization. Every vendor pushes their own Agent framework—LangChain, LlamaIndex, AutoGPT—each with different approaches. Developers waste time just choosing. MCP at least offers a possibility: regardless of your Agent framework, underlying tool calls can be unified.

I’ve been experimenting with converting a small project to MCP architecture. Current feeling: the learning curve is steeper than expected, but once it works, extensibility is much better.

If you’re building Agents, I recommend investing time in MCP. It may not be perfect, but in this chaotic ecosystem, it’s a bet worth making.

After all, who doesn’t want their Agents to just plug in and work?