Why MCP Is the USB-C of AI: A Founder's Guide to Agent Interoperability

The Integration Nightmare

Right now, connecting AI tools to your data looks like this:

• ChatGPT: Custom GPT with uploaded files (stale immediately)
• Claude: Copy-paste or API wrapper (manual overhead)
• Cursor: .cursorrules and context files (per-project setup)
• Custom agents: Build integration from scratch (weeks of work)

Each tool has its own way of accessing context. None of them talk to each other. You maintain 4+ integrations for the same data.

This is the "different charger for every device" era of AI.

What MCP Actually Is

Model Context Protocol (MCP) is an open standard, created by Anthropic, that defines how AI tools connect to external data and capabilities.

Build one MCP server. Every MCP-compatible tool connects instantly.

The USB-C Parallel

USB-C won because:

1. One standard replaced dozens of proprietary connectors
2. Major players adopted it (Apple, Google, everyone)
3. It was good enough for most use cases
4. Network effects made holdouts painful

MCP is following the same playbook:

1. One protocol for AI-to-data connections
2. Anthropic backs it, others are adopting
3. It handles common patterns well
4. Early adopters gain compound advantages

What MCP-Compatible Means for You

When your data is MCP-accessible:

• Claude Desktop can query your business knowledge directly
• Cursor can understand your codebase context
• Custom agents can read/write without custom code
• Future tools you haven't heard of yet will just work

One investment, unlimited integrations.

Before vs. After

What Becomes Possible

With MCP-connected context, Claude (and other tools) can:

Query across your entire knowledge base

"What patterns do you see across our customer segments and their feature requests?"

Update your knowledge in conversation

"Add this insight to our competitive analysis"

Chain complex operations

"Review our Q3 decisions in context of current constraints and flag any that need revisiting"

Maintain continuity

Every conversation picks up where the last one left off, with full context.

The Early Adopter Advantage

Standards create network effects. Early positioning matters.

If you adopt MCP now:

• You build context infrastructure that works with emerging tools
• Your data is ready for whatever AI tools win next
• You compound learning while others catch up

If you wait:

• You'll eventually migrate anyway (or be left behind)
• You'll do the work without the early compound benefits
• Competitors who moved first will have richer context

Will MCP Win? (The Skeptic's View)

Fair concerns:

• "Standards fail all the time" True. But MCP has Anthropic's backing and a head start.
• "OpenAI might do something different" Possible. But fragmentation hurts everyone; interoperability pressure is real.
• "It's early and limited" Yes. But the core patterns are stable.

Our take: MCP or something very similar will become standard. Building on it now is low-risk, high-optionality.

Getting Started

1. Audit your AI tools. Which ones support MCP? (Claude Desktop does. Others are coming.)
2. Structure your core context. MCP connects data, so make sure that data is organized.
3. Set up one integration. Start with Claude Desktop + your knowledge base.
4. Experience the difference. One connected conversation will show you the potential.

The charger era is ending. Be ready for what comes next.