What is MCP? The Protocol That Lets AI Talk to Your Website

AI Wants to Do Stuff, Not Just Talk

So ChatGPT and other AI assistants are great at answering questions. But they have this limitation - they only know what they already know, or what they can browse from the web.

What if AI could actually DO things for you? Check your inventory. Update your CRM. Pull real-time data from your systems.

That's what MCP (Model Context Protocol) is about.

Ok So What is MCP?

It's basically a standard way for AI to connect to external tools, databases, and services. Think of it like a universal translator that lets AI talk to your business systems.

Before MCP, every AI integration was custom-built. Want ChatGPT to access your product database? Need developers to build it. Want it to check shipping status too? Another custom build.

MCP changes this. Standardized protocol, any AI can use it to connect to any compatible service. Build once, works everywhere.

Why Care?

Real-time data. Instead of AI working with old info, it can pull live data from your systems. Current prices, actual inventory, real shipping times.

Actions, not just answers. AI goes from "Here's info about your order" to "I've updated your shipping address and sent you a confirmation."

One integration, multiple AIs. Build the MCP connection once, works with different AI platforms.

How It Works (Simple Version)

Imagine a restaurant where the waiter speaks every language. Order in English, French, Japanese - doesn't matter, waiter translates perfectly for the kitchen.

MCP is like that waiter for AI.

You expose your data/tools through an MCP server (standardized interface). AI connects using the protocol. AI can now access your data and do things, within permissions you set.

The key word is "standardized." Everyone speaks the same language so everything works together.

For E-commerce This Gets Interesting

Product discovery. AI can query your actual catalog in real-time. Not cached data from months ago - what's actually available now.

Personalized recommendations. When customers ask AI for help, it can access their purchase history and preferences (with permission) for actually personalized suggestions.

Order management. "Where's my order?" AI checks your actual system and gives precise answers. Or makes changes the customer requests.

Inventory queries. "Do you have this in size 10?" AI checks real inventory, not a snapshot from last week.

For Content Sites

AI can access your latest articles and updates instead of relying on whenever it last crawled your site. With permissions, it can access user preferences to personalize responses. Newsletter signups, account creation, bookmarking - all through conversation with AI.

How This Connects to GEO

Here's an important thing - MCP and GEO work together but differently.

GEO is about making your content AI-readable. Passive - you optimize, AI reads.

MCP is about giving AI active access to your systems. Dynamic - AI queries, you respond.

Complete AI strategy includes both. GEO so AI can find and recommend you. MCP so AI can provide rich real-time experiences.

Getting Started

MCP is still pretty new but adoption is growing fast. Here's how to prepare:

Figure out what data would be valuable for AI to access in real-time. Product catalogs, pricing, availability, order status?

Think about what actions AI could do for customers. Place orders, update preferences, check status, make reservations?

Consider security. MCP has permission systems - what should AI see? What needs user authentication?

Watch for platform support. Major AI platforms are adding MCP support quickly.

What About Privacy/Security?

Fair concern. MCP is designed with security in mind.

You control exactly what AI can see and do. Read-only access to products? Full access to orders? Your call.

Users can be required to authenticate before AI accesses their data. You can track what AI accessed and when. And you can turn off AI access anytime.

The key is YOU control the MCP server. AI can only do what you explicitly allow.

Picture This Scenario (2027)

Customer: "Hey ChatGPT, I need running shoes. Check my favorite sports store, find something for flat feet under $150, order my usual size."

ChatGPT: "Found three options at SportStore. Based on your history you like Brooks. They have the Ghost 16 in your size for $139. Order to your saved address?"

Customer: "Yeah do it."

ChatGPT: "Done. Arriving Thursday. Applied your loyalty points for free shipping."

That's MCP. AI isn't just recommending, it's doing. And all because the store exposed their systems through MCP.

What to Do Now

Today: Run a Recomaze audit to make sure your baseline AI readiness is solid. Document what data and actions could benefit from AI access.

Near-term: Follow MCP development and platform adoption. Figure out which AI platforms your customers actually use.

When ready: Implement MCP server for key systems. Start read-only, expand as you get comfortable.

The AI world moves fast. MCP is the infrastructure powering the next generation of AI interactions. Understanding it now puts you ahead.

Check your current AI readiness to see where you stand today.