MCP Is Not the USB of AI—It’s Just HTTP

There’s a growing tendency in AI marketing circles to refer to MCP—the Model Context Protocol—as the “USB of AI.” The idea, presumably, is that it offers some kind of plug-and-play universal interface between language models and tools. But this metaphor is worse than lazy—it’s actively misleading.

Let’s dig into why this comparison doesn’t work, and why we should be framing MCP for what it really is: the HTTP of agentic AI.

First: What MCP Actually Is

MCP (as defined by Anthropic and others) is a simple schema-driven way to expose external tools (functions, APIs, models, etc.) to an LLM via a list of structured descriptors. At its core, MCP provides:

• A list of tools or functions that can be called by the model
• Structured schema and descriptions of input/output format
• A protocol for how the model requests tool usage and how the host responds
• A shallow interface layer—no built-in auth, no rate-limiting, no security posture—meaning vulnerabilities can propagate downstream fast if not handled by the infrastructure
  
  

That’s it. MCP is not a system, not a framework, not a runtime. It is a spec. It’s a way for an LLM to say: “Hey, I want to call this function using these parameters—please go run it and return the result.”

This is incredibly useful. But it’s also incredibly basic.

Why USB Is the Wrong Analogy

Let’s break down what USB actually is, and then compare it to MCP.

What USB Offers:

• Device discovery and negotiation
• Power delivery
• Automatic driver management
• Hot-swapping
• Universal compatibility across platforms

Compare that to MCP:

• No discovery—you must define and pass in the tools explicitly
• No state management
• No retry logic or dynamic negotiation
• No compatibility enforcement
• No orchestration, fallback, or sequencing logic

If MCP were USB, you’d have to:

• Manually describe the specs of every USB device
• Define which voltages it supports
• Hardcode the communication logic for every host\
• Hope nothing fails

That’s not a plug-and-play interface. That’s an instruction manual stapled to a cable and handed to the user.

The Better Analogy: HTTP for Agents

If you must draw a parallel, MCP is far closer to HTTP than USB.

Think about it:

• HTTP defines how clients and servers talk to each other.
• It says nothing about what the server actually does with the request.
• It’s stateless, schema-driven, and relies on external logic to do anything interesting.

Same with MCP:

• It defines how a model communicates tool invocation.
• It describes the function schema.
• It sends a structured request.
  What happens after that—logging, retries, state updates, business logic—is all up to the caller.
  
  

MCP is the transport layer. It’s the protocol that carries the payload. It is not the workflow engine. It is not the strategy. It is not the brain.

Why This Matters

Bad metaphors aren’t just annoying—they confuse builders.

When people call MCP the “USB of AI,” they:

• Overstate its capabilities
• Confuse non-technical stakeholders
• Create false expectations of plug-and-play AI behavior

That leads to frustration when things break, or when orchestrating a multi-step agent requires 5x more infrastructure than people were led to believe.

It also obscures the actual architectural responsibilities. If you think MCP handles memory, recovery, decision-making, or retries, you’re going to be disappointed—and possibly ship unreliable agents.

What We Actually Need

Instead of pretending MCP is smarter than it is, let’s invest in:

• Layered orchestration frameworks (true agent state machines)
• Caching, retries, and dynamic fallback logic
• Memory and context tracking layers
  Better visualization of tool call graphs and execution traces

These are the things that make agents robust—not a glorified JSON schema and a loop.

Final Word

MCP is good. It is useful. But it is not magical.

It’s a transport layer.

It’s a protocol.

It’s HTTP, not USB.

Let’s build the rest on top.

Want to talk about workflows, tool routing, or how to actually make AI agents production-grade? We’ve been in the weeds building and monitoring them. Happy to compare notes.