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MCP Protocol: Is This the 'USB-C Moment' for AI Agents?

AI Agent, MCP, Anthropic, Protocol Standard —

In November 2024, Anthropic launched MCP (Model Context Protocol). Few paid attention at the time—just another ‘big tech vanity standard,’ right?

But a year later, things have changed.

From ‘Every Man for Himself’ to ‘Universal Interface’

Previously, every AI app integrating external tools needed custom adapter code. OpenAI had Function Calling, Google had Tool Use, each with different API formats. Developers suffered.

MCP’s idea is simple: just as USB-C unified charging ports, create a universal standard for AI Agent-to-system communication.

Three core concepts:

  • Resources: External data sources (databases, files, APIs)
  • Tools: Functions the AI can invoke
  • Prompts: Pre-defined interaction templates

Essentially: transform integration from ‘custom negotiation every time’ to ‘plug and play.’

Why Is It Heating Up Now?

Two reasons.

First, Agents actually need this. 2026 AI isn’t just ‘chatbots’ anymore—it’s Agents that ‘do things.’ Agents need to query databases, call APIs, manipulate files. Without unified protocols, this doesn’t work.

Second, ecosystem momentum. Claude Desktop, Cursor, Cline—these mainstream tools now support MCP. GitHub MCP server counts grew from dozens early this year to hundreds now.

What’s interesting: MCP wasn’t created by an official standards body—it was pushed by Anthropic alone. But because it’s open-source and well-designed, the market is voting with its feet.

How’s It Actually Work?

I tried it myself. My take: ideal is rich, reality has gaps.

Works well for:

  • Local file operations (read/write/search)
  • Database queries (SQL clients)
  • Simple API calls

Okay for:

  • Complex business workflow orchestration
  • Long-running tasks requiring state management

Pain points:

  • Documentation still patchy—some server configs require guessing
  • Weak error handling—hard to debug when things break
  • Permission control granularity insufficient

MCP vs. Function Calling

Common question: what’s the difference between MCP and OpenAI’s Function Calling?

Function Calling is a ‘model-level capability’—the model knows it can invoke functions, but how and what to call is the app’s responsibility.

MCP is a ‘protocol-level standard’—defining how Agents and external systems discover, communicate, and return results.

Analogy: Function Calling is ‘knowing how to use chopsticks.’ MCP is ‘restaurant utensil standards.’ One is a personal skill; the other is industry specification.

What’s Next?

My prediction: MCP has a strong chance of becoming the de facto standard, but won’t be the only one.

Just as programming has Python, Java, Go, AI Agent communication will have multiple protocol options. MCP leads now, but Google’s A2A and OpenAI’s Agents SDK are pushing their own solutions.

For developers, the best strategy: learn MCP, but don’t go all-in. Maintain protocol-agnostic designs for lower switching costs later.

What’s your take? Do you think MCP could become the HTTP of the AI era?

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