What Is the Model Context Protocol (MCP) and Why Your Business Needs It

The AI Integration Problem

Every business today faces the same question: how do we make our product work with AI?

Right now, when someone asks an AI assistant to "check our inventory levels" or "create a support ticket," the AI can't do it. It doesn't have access to your systems. The user has to leave the AI, open your app, do the thing manually, then go back to the AI.

This is like the early web — before APIs existed, every system was an island. APIs connected those islands. Now, the Model Context Protocol (MCP) is doing the same thing for AI.

What MCP Actually Is

MCP is an open standard created by Anthropic that defines how AI assistants communicate with external tools and data sources. Think of it as a USB-C port for AI — a universal connector that lets any AI assistant plug into any business tool.

Here's the simple version:

• Without MCP: Each AI platform needs custom code to talk to your product. You build one integration for Claude, another for ChatGPT, another for Cursor. Three separate codebases doing the same thing.
• With MCP: You build one MCP server. Every AI assistant that supports MCP can immediately use your product. Build once, work everywhere.

How MCP Servers Work

An MCP server is a lightweight service that sits between your existing APIs and AI assistants. It defines:

Tools — Actions the AI can take. Examples:

• create_order — Place an order in your e-commerce system
• get_customer_info — Look up customer details
• generate_report — Pull analytics data
• submit_ticket — Create a support ticket

Resources — Data the AI can read. Examples:

• Product catalogs
• Documentation
• Knowledge bases
• Configuration settings

Prompts — Pre-built templates that help the AI use your tools effectively.

The AI assistant discovers these tools at connection time. When a user asks "show me last month's sales," the AI sees that your MCP server has a get_sales_report tool, calls it with the right parameters, and presents the results — all in natural language.

Why This Matters for Your Business

1. Your Product Becomes AI-Native

Customers are spending more time inside AI assistants. If your product isn't accessible there, you're invisible during those workflows. MCP makes your product a first-class citizen in every AI conversation.

2. Reduce Support Load

When AI assistants can directly query your systems, users get instant answers instead of filing support tickets. "What's the status of my order?" becomes a question the AI can answer in real time.

3. Enterprise Sales Advantage

Enterprise buyers are evaluating vendors on AI compatibility. Having an MCP server means your product integrates with their AI workflows out of the box. It's becoming a checkbox on RFPs.

4. Future-Proof Architecture

MCP is backed by Anthropic and adopted by major players. Building on MCP means you're building on a standard, not a proprietary integration that could break or become obsolete.

What Connecting Your Product Looks Like

A typical MCP server project follows four phases:

Discovery — We review your product's API, identify the highest-value actions to expose, and determine the right tool surface.

Architecture — We design tool definitions, parameter schemas, authentication flow, and error handling. You get an architecture document before any code is written.

Build — Development and testing. You see a working prototype early so we can refine which actions feel right when accessed through AI.

Deploy — We deploy to your infrastructure, set up monitoring, and hand over documentation. Your product is now available in AI.

Who's Already Using MCP

MCP adoption is accelerating across the industry. Companies are building MCP servers for:

• SaaS platforms — Making their core product features AI-accessible
• Internal tools — Connecting CRMs, databases, and ticketing systems to AI assistants for internal teams
• Developer tools — Exposing APIs, documentation, and deployment workflows
• E-commerce — Enabling AI-driven product search, order management, and customer service

Getting Started

The first step is a consultation. We'll review your product, your APIs, and identify which actions would have the most impact as MCP tools. You'll walk away with a clear architecture proposal and project scope — whether you build it yourself or have us do it.

The consultation costs $250 for the first hour. That buys you a concrete, actionable plan for making your product available in AI.