MCP Explained Simply: How Model Context Protocol Actually Works Under the Hood

Model Context Protocol is revolutionizing how AI models interact with tools and data, but what is it and how does it work? MCP is not just a new standard, it's a game-changer for AI development. In this deep dive, we'll explore the ins and outs of MCP and how it's transforming the AI landscape.

Introduction to MCP

To understand MCP, we need to start with the basics. Model Context Protocol (MCP) is a standard that allows AI models to interact with tools and data in a more efficient and scalable way. Think of it like a common language that enables different AI systems to communicate with each other. This standardization enables developers to build more complex AI applications without worrying about compatibility issues.

The key idea behind MCP is to provide a standardized way for AI models to request and receive data and tools, making it easier to develop and deploy AI applications.

An AI model is a program that uses data to make predictions or decisions. An embedding — a list of numbers that captures meaning — is used to represent the input data in a way that the model can understand.
For example, when you ask a virtual assistant to book a flight, the assistant uses an AI model to understand your request and make a booking. The model uses embeddings to represent your request, such as the destination, departure date, and return date.

// Example of an embedding
const destinationEmbedding = [0.1, 0.2, 0.3, 0.4, 0.5];
const departureDateEmbedding = [0.6, 0.7, 0.8, 0.9, 1.0];

In plain English, an embedding is like a set of coordinates on a map that helps the AI model locate and understand the input data.

How MCP Works

MCP works by providing a standardized way for AI models to request and receive data and tools. This is achieved through a service context, which defines the tools and resources available to the model. The service context is like a menu that lists all the available options, and the model can request specific tools or resources by referencing the menu.

// Example of a service context
const serviceContext = {
  tools: ['naturalLanguageProcessing', 'imageRecognition'],
  resources: ['userDatabase', 'productCatalog'],
};

The key takeaway here is that the service context provides a clear and standardized way for AI models to request and receive the tools and resources they need.

Think of it like ordering food at a restaurant. You look at the menu (service context), choose what you want (request a tool or resource), and the restaurant (MCP) delivers it to you.

Tip: When working with MCP, it's essential to define a clear service context that lists all the available tools and resources.

Building an MCP Server in Node.js

To build an MCP server in Node.js, we need to use the @aws-sdk/client-lambda package, which provides a set of tools for working with AWS Lambda functions.

// Import the required packages
import { LambdaClient } from '@aws-sdk/client-lambda';

// Create a new Lambda client
const lambdaClient = new LambdaClient({ region: 'us-east-1' });

We also need to define a handler function that will process incoming requests and return responses.

// Define a handler function
async function handler(event) {
  // Process the incoming request
  const tool = event.tool;
  const resource = event.resource;

  // Return a response
  return {
    statusCode: 200,
    body: `Tool: ${tool}, Resource: ${resource}`,
  };
}

The key idea here is to define a clear and standardized way for the MCP server to process incoming requests and return responses.

Real-World Applications of MCP

MCP has many real-world applications, including natural language processing, image recognition, and recommendation systems.
For example, a virtual assistant can use MCP to request natural language processing tools and resources, such as speech recognition and text analysis.

// Example of a virtual assistant using MCP
const virtualAssistant = {
  requestTool: 'naturalLanguageProcessing',
  requestResource: 'userDatabase',
};

In plain English, MCP enables virtual assistants to request and receive the tools and resources they need to perform tasks, such as understanding voice commands or providing personalized recommendations.

Common Gotchas and Best Practices

When working with MCP, there are several common gotchas to watch out for, including confusing tools, resources, and prompts.

Tip: Make sure to clearly define the tools, resources, and prompts available in your service context to avoid confusion.

Another gotcha is requiring ES modules (esm) in Node 22, which can break existing Lambda layers silently.

// Example of requiring ES modules in Node 22
require('esm')(module);

The key takeaway here is to be aware of the potential gotchas when working with MCP and to follow best practices to avoid issues.

The Takeaway

Here are the key takeaways from this deep dive into MCP:

• MCP provides a standardized way for AI models to request and receive data and tools.
• The service context defines the tools and resources available to the model.
• MCP can be used to build more efficient, scalable, and secure AI applications.
• Common gotchas include confusing tools, resources, and prompts, as well as requiring ES modules in Node 22.
• Best practices include clearly defining the service context and following established guidelines for working with MCP. > Remember, MCP is a powerful tool for building AI applications, but it requires careful planning and attention to detail to avoid common gotchas and ensure success.

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Transparency notice

This article was written with the help of an AI system — Groq (LLaMA 3.3 70B).

Published: 2026-08-13 · Primary focus: MCP

All code blocks are intended to be correct and runnable, but please verify them
against the Model Context Protocol spec before using in production.

Find an error? Drop a comment — corrections are always welcome.