Introducing Model Context Protocol (MCP): A Developer's Guide to Smarter AI Integrations

Hasan Naqvi
May 9
2 min read

In the ever-evolving world of AI, the ability for models to interact intelligently with real-world tools is no longer just a luxury—it’s becoming the standard. Enter Model Context Protocol (MCP), a new open protocol designed to help AI systems connect with applications, services, and data in a secure, structured, and scalable way.

What Is MCP?

Think of MCP as the universal connector between AI models and external tools. While language models like Claude, ChatGPT, or Gemini are great at understanding and generating language, they can’t inherently book meetings, pull database entries, or process documents.

MCP solves that by allowing AI to "call out" to specialized services—called MCP servers—that provide real functionality.

How MCP Works – Simplified

Imagine you're building a virtual assistant. You want it to:

Read a customer file from SharePoint
Add a note in your CRM
Fetch a real-time weather update

Using MCP:

Your AI assistant sends a request via an MCP client.
The request is routed to an MCP server, which knows how to handle real-world tasks.
The server returns structured data, and the AI continues the conversation.

Each MCP server exposes a set of capabilities (e.g., read a file, send an email, log an action) through a standardized interface.

Core Components

Component	Role
MCP Host	The main AI system (e.g., Claude Desktop) that handles orchestration
MCP Client	The interface that communicates with MCP Servers
MCP Server	The tool or data provider (e.g., Calendar, SharePoint, Database)
Data Source	Underlying system where the actual info lives

Why Developers Should Care

As AI shifts from text generation to action execution, MCP offers a developer-friendly, standardized way to enable AI with:

Tool usage
Context-aware operations
Access to structured and unstructured data
Secure and auditable actions

You no longer have to hand-craft APIs or create isolated integrations. MCP simplifies the interaction model.

A Practical Example

Let’s say you want your AI to schedule meetings using Google Calendar.

Without MCP: You build a custom integration, handle OAuth, parse data, manage errors… lots of boilerplate.With MCP: You define a “Calendar” MCP server. The AI uses that server to send a request like:

{
  "action": "createEvent",
  "params": {
    "title": "Meeting with Tracy",
    "datetime": "2025-05-12T14:00:00"
  }
}

The server books the meeting, confirms availability, and sends a response.

Security & Permissions

Since MCP allows AI to interact with external systems, security is paramount. Proper authentication, permission management, and endpoint validation must be in place. Tools like MCP Safety Scanner can be used to validate that your servers don’t expose unintended actions.

Learn More

Here are some resources to help you dive deeper:

Final Thoughts

The Model Context Protocol is a big leap in making AI not just smart, but useful. It’s a win-win for developers and users—AI becomes more actionable, and developers get a clean, standardised way to expose tools and data. Whether you’re building enterprise apps, custom assistants, or internal copilots, MCP is something you’ll want to keep on your radar.