Everyone’s buzzing about Model Context Protocol, but most conversations I’m seeing focus on the wrong thing. Yes, it’s exciting that we can add AI capabilities to desktop apps, but that’s like getting excited about using a Ferrari to drive to the grocery store. You’re missing the real potential.

MCP isn’t just about making your text editor smarter. It’s about building AI systems that can actually do things in your business, not just generate text about doing things. And if you’re serious about implementing AI that moves beyond party tricks, you need to understand how this works.

The Core Problem: AI That Can’t Act

Think about how most of us interact with large language models today. You type a question, the AI responds with words. Those words might be brilliant, insightful, even actionable, but at the end of the day, they’re still just words on a screen.

This is like having a brilliant consultant who can analyze any situation and give perfect advice, but they’re locked in a soundproof room. They can see your problems through a tiny window and write solutions on paper, but they can’t actually touch anything in your business.

That’s the fundamental limitation we’re dealing with. Your AI might know exactly what needs to happen, but it can’t check your inventory system, update your customer database, or schedule that follow-up meeting. It’s all talk, no action.

But what if that consultant could step out of the room? What if they could access your systems, pull real data, and make things happen? That’s where Model Context Protocol comes in.

MCP: The Bridge Between AI and Action

Model Context Protocol creates a standardized way for AI applications to connect with the tools and data they need to be useful. Think of it like creating a universal adapter that lets your AI plug into any system in your business.

Here’s how it works in practice. You have what MCP calls a “host application” – this is your AI agent or the software that’s running your AI. This host uses an MCP client to communicate with MCP servers that house all the actual capabilities your AI needs.

Those servers contain three critical components that transform AI from a chatbot into a working assistant:

• tools that can perform actions
• resources that provide current data
• prompts that help structure interactions

The beauty is in the separation. Instead of hardcoding every capability into your AI application, you create modular servers that can be shared, updated, and combined. It’s like the difference between buying a Swiss Army knife with fixed tools versus having a workshop where you can grab exactly the right tool for each job.

A Real Example: AI That Manages Your Sales Pipeline

Let me walk you through a concrete example that shows the power of this approach. Imagine you’re building an AI system to help manage your sales pipeline. Your sales team is constantly juggling leads, scheduling calls, updating CRM records, and trying to figure out the best next steps for each prospect.

Without MCP, you’d have to build all this functionality directly into your AI application. You’d need to write code to connect to your CRM, integrate with your calendar system, pull in email data, and handle all the authentication and error handling for each service. It’s a massive undertaking, and when you’re done, nobody else can benefit from your work.

With MCP, you create specialized servers for each function. Your CRM server knows how to read and update customer records, search for similar deals, and flag opportunities that need attention. Your calendar server can check availability, schedule meetings, and send invites. Your email server can analyze communication patterns and suggest follow-up timing.

When a sales rep asks the AI, “What should I focus on this week?” the system springs into action. The host application queries each server to understand what’s available, then asks the language model to determine what information it needs based on the question.

The AI might decide it needs recent activity from the CRM, this week’s calendar to see available time slots, and email patterns to understand which prospects are most engaged. It requests this data from the appropriate servers, processes it all together, and comes back with specific, actionable recommendations – complete with the ability to actually schedule those recommended calls and update the CRM with next steps.

The Enterprise Advantage: Composable AI Systems

What makes this approach powerful for business isn’t just the individual capabilities, but how they combine. MCP servers can themselves be clients of other servers, creating chains of functionality that would be incredibly complex to build from scratch.

Consider a marketing automation system that needs to analyze website traffic, segment customers, generate personalized content, and schedule email campaigns. Each of these functions could be handled by different MCP servers, possibly built by different teams or even different companies.

Your analytics server pulls traffic data and identifies trending content. Your customer segmentation server takes that data and combines it with purchase history from your e-commerce server. Your content generation server creates personalized emails based on the segments and trending topics. Your email server handles the actual sending and tracks results.

The result is an AI system that can run sophisticated marketing campaigns with minimal human intervention, but it’s built from composable pieces that can be updated, replaced, or reused in other contexts.

Technical Reality: JSON-RPC and HTTP

Now, let’s talk about how this actually works under the hood, because the technical choices matter for implementation. MCP uses JSON-RPC over HTTP with Server Sent Events for communication. Yes, some developers have raised eyebrows at these choices, but they’re pragmatic decisions that prioritize compatibility and ease of implementation over theoretical elegance.

The protocol supports both local communication through standard input/output (useful for development and simple desktop integrations) and network communication through HTTP (essential for enterprise deployments). For business applications, you’ll almost certainly want the HTTP option since it allows your AI systems to connect with services running anywhere in your infrastructure.

The communication flow is straightforward but powerful. Your host application can query servers for their capabilities, request specific resources with parameters, and invoke tools with structured data. Servers can even send asynchronous notifications back to clients, enabling real-time updates and event-driven workflows.

Building vs. Buying: The Strategic Choice

One of the most compelling aspects of MCP for business leaders is how it changes the build-versus-buy equation for AI capabilities. Instead of choosing between developing everything in-house or relying entirely on external providers, you can mix and match.

You might build custom MCP servers for your unique business processes while using community-developed servers for common functions like calendar management or document processing. Your internal development team can focus on what makes your business unique while leveraging the work of the broader ecosystem for everything else.

This approach also future-proofs your AI investments. As new capabilities emerge or existing services improve, you can swap out individual MCP servers without rebuilding your entire AI system. It’s modular architecture applied to artificial intelligence.

The Broader Vision: AI That Actually Works

Model Context Protocol represents a shift from AI as a novelty to AI as infrastructure. Instead of building isolated systems that impress in demos but struggle in real workflows, we can create AI that integrates seamlessly with existing business processes.

This isn’t about replacing human workers with robots. It’s about giving your team AI assistants that can handle the routine, time-consuming tasks that eat up their days – checking multiple systems for information, updating records across platforms, scheduling and rescheduling meetings, generating reports from scattered data sources.

When your AI can actually access and act on your business data, it stops being a fancy search engine and becomes a genuine force multiplier for your team.

The companies that understand this distinction and invest in building proper agentic AI systems will have a significant advantage over those still treating AI as a content generation tool. Model Context Protocol gives us the foundation to build that future, starting today.