Model Context Protocol (MCP): A Step Towards the Future of Artificial Intelligence

If you are just starting out in the world of programming or are simply passionate about technology, you probably agree that there is one concept that underpins all modern software interactions: context. In this article, we will demystify a term that, although it may not (yet) be found in a standard textbook, defines one of the greatest challenges and opportunities in IT: Model Context Protocol (MCP).

At NewTech Academy, we believe that understanding fundamental concepts is just as important as mastering syntax. And MCP is a key concept.

Let’s be clear from the outset: a Model Context Protocol is not a rigid, set-in-stone standard like HTTP (Hypertext Transfer Protocol) or TCP/IP (Transmission Control Protocol/Internet Protocol). You won’t find an official RFC (Request for Comments) for it.

So what is it?

Model Context Protocol (MCP) is a conceptual term that describes the set of rules, methods, and architectures by which a software system (a “Model”) understands, maintains, and uses previous information (“Context”) to process a new request.

Think of it as the short-term memory of an application. Without a context protocol, every interaction you have with software would be like the first one.

A Brief History: The Evolution of Context Management

To understand why a Context Protocol Model is so important now, we need to look back. The history of software is, in essence, a continuous struggle to overcome a fundamental problem: the stateless nature of the internet.

1. The “Stateless” Era (the 1990s)

In the early days of the web, the HTTP protocol was designed to be “stateless.” That meant the web server treated every request from you as completely new. You clicked on a link, you got a page. You clicked again, the server had completely forgotten who you were. It was efficient, but terrible for the user experience. You couldn’t have a shopping cart, a user account, or any form of personalization.

2. The First Context “Protocols”: Cookies and Sessions (2000s)

To solve this, programmers came up with some clever solutions.

• Cookies: Small text files stored in your browser. When you visit a site, it gives you a “cookie” (like a badge). On your next visit, your browser showed the badge to the server, and the server knew: “Ah, it’s you! I know you added that product to your cart.”
• Sessions: Similar, but the information was stored on the server. The browser only received a unique session ID (usually via a cookie) that unlocked your “file” on the server.

These were the first rudimentary forms of the Context Protocol Model. The “model” was the web application (e.g., an online store), and the “context protocol” was the system of cookies and sessions that allowed it to remember you.

3. The Era of Modern Applications: State Management (2010s)

With the emergence of complex web applications (Single Page Applications – SPA) built with React, Angular, or Vue, the context problem exploded. Now, not only did the server need to know who you were, but the application running in your browser also had to manage a huge amount of “state” (local context): are you logged in? What filter did you select? What are you writing in this form?

Dedicated tools for “state management” appeared, such as Redux or Context API (in React). These are, in essence, advanced implementations of a Model Context Protocol at the client (frontend) level.

What’s New About MCP and Why Is It Vital in the AI Era?

And so we arrive at the present. The reason why a concept such as Model Context Protocol is becoming central is the explosion of Large Language Models (LLMs), such as ChatGPT, Claude, or Gemini.

For an LLM, context is everything.

The Context Window

An LLM does not “learn” in real time and does not have a permanent memory of your conversations (for privacy and technical design reasons). All an LLM knows about you and the current conversation is what you send it with each request.

This package of information – your current prompt PLUS the conversation history – is called the “context window.”

This is where the Model Context Protocol (MCP) comes in in the AI era. It defines:

1. What information from history is selected to be sent to the model
2. How this information is formatted
3. How the model manages this context to give a relevant response

Imagine reading a book, but on each new page, you completely forget the previous pages, except for a small summary that someone writes down for you. The quality of that summary (the context) determines whether or not you will understand the story.

A weak MCP leads to chatbots that “forget” what you said three replies ago. A robust MCP enables fluid, nuanced conversations where the AI remembers key details and builds on them.

MCP: The Standardized Communication Bridge Between Applications and LLMs

So far, we have discussed why context is needed. But how does this context actually get from the application you are using (be it a text editor, CRM software, or programming IDE) to the AI server (an LLM such as GPT-4 or Gemini)?

This is where the Model Context Protocol (MCP) plays a key role as a communication standard. According to emerging definitions, MCP is the set of rules and data formats that allow an external application to reliably “package” and send relevant context to an AI model.

Think of MCP as a universal “container” or common language. Instead of sending just the text of the conversation, an application can use this protocol to send complex “context objects”: a PDF file, a dataset from a database, user permissions, or the current state of the interface. The LLM model is trained to understand this standardized container. Thus, MCP becomes the essential interoperability layer that allows any application to become “intelligent” and “context-aware” without each developer having to reinvent the way they communicate.

Practical Uses: Where do we Encounter a Model Context Protocol Today?

Even if you don’t call it by name, you interact with dozens of implementations of a Model Context Protocol every day.

1. Chatbots and virtual assistants (e.g., ChatGPT, Google Assistant)

• The challenge: maintaining the thread of conversation.
• MCP implementation: For every message you send, the application (e.g., ChatGPT) adds your message to a conversation history. It then sends this history (or a relevant part of it) to the LLM. The AI’s response is also added to the history. This continuous cycle is the protocol.

2. Recommendation systems (e.g., Netflix, Spotify)

• The challenge: To suggest relevant content based on what you have consumed in the past.
• MCP implementation: The “model” is the recommendation algorithm. The “context” is your viewing/listening history, ratings, searches, and even what other users with similar tastes are watching. The protocol defines how much of this history is relevant (perhaps what you watched 5 years ago doesn’t matter as much as what you watched yesterday).

3. AI Programming Tools (e.g., GitHub Copilot)

• The challenge: To suggest code that fits your project.
• MCP implementation: When Copilot suggests a line of code, it doesn’t just look at the line above. Its “context” includes the entire file you’re working on, other files open in your editor, and sometimes the libraries you’re using. The way it selects and sends this context to the Codex/GPT-4 model is an extremely sophisticated Model Context Protocol.

4. E-commerce Applications (e.g., Amazon)

• The challenge: Managing a complex shopping cart and various customizations.
• MCP implementation: Here, the protocol combines classic methods (server sessions to remember the cart) with modern methods (real-time analysis of your clicks—the behavioral “context”—to reorder the products on the page).

Implementation Method: How Does a Programmer Construct an MCP?

For a beginner programmer, “implementing” a Model Context Protocol does not mean writing a protocol from scratch. It means using the right tools to manage the state (context) of your application.

Let’s take the most relevant modern example: communicating with an AI API.

Suppose you want to build a simple chatbot in Python using the OpenAI API.

Naive implementation (without MCP):

Python

# The programmer sends ONLY the new message

response = client.chat.completions.create(

  model=”gpt-3.5-turbo”,

  messages=[

    {“role”: “user”, “content”: “What is a ‘Model Context Protocol’?”}

  ])

# … receives the response …

# User asks: “What did you say it was called?”

# The programmer sends ONLY the new message AGAIN

response_2 = client.chat.completions.create(

  model=”gpt-3.5-turbo”,

  messages=[

    {“role”: “user”, “content”: “What did you say it was called?”}

  ])

# The AI WILL RESPOND: “I didn’t say it was called anything. What are we talking about?”

The AI is “stateless.” It has completely forgotten the first question.

Correct implementation (with a simple MCP):

The programmer must maintain the context.

Python

# Step 1: The programmer defines a “history” (the context)

conversation_history = [] 

# Step 2: First question

user_message_1 = “What is a ‘Model Context Protocol’?”

conversation_history.append({“role”: “user”, “content”: user_message_1})

response_1 = client.chat.completions.create(

  model=”gpt-3.5-turbo”,

  messages=conversation_history # We send the entire history!

)

ai_response_1 = response_1.choices[0].message.content

# Step 3: The programmer SAVES the AI response in context

conversation_history.append({“role”: “assistant”, “content”: ai_response_1})

# Step 4: Second question

user_message_2 = “What did you say it was called?”

conversation_history.append({“role”: “user”, “content”: user_message_2})

# We send the COMPLETE history AGAIN

response_2 = client.chat.completions.create(

  model=”gpt-3.5-turbo”,

  messages=conversation_history 

)

# The AI WILL RESPOND: “I mentioned ‘Model Context Protocol’.”

This manual process of adding messages to a list and sending the entire list each time is a rudimentary Model Context Protocol.

The real challenges (and where software engineering comes in) are:

• What do you do when conversation_history becomes too large for the model’s “context window”?
• Do you need to summarize the conversation?
• Do you need to select only the relevant parts? (This process is called RAG – Retrieval-Augmented Generation).

What’s Next? The Future for the Model Context Protocol 

The fight for a better, bigger, and smarter context is the next big race in technology. What we call the Model Context Protocol today will evolve into concrete standards and technologies.

1. Giant Context Windows

The future is already here. We have gone from context windows of 4,000 “tokens” (words/parts of words) to 128,000 (GPT-4 Turbo) and now to 1 million (Gemini 2.5 Pro). This means that an AI will be able to “remember” an entire book or a complete code base in a single conversation.

2. Persistent and Proactive Context

In the future, an MCP will not just be about sending conversation history. It will be about giving the model secure and continuous access to a “personal context.” Imagine an AI assistant that knows what meetings you have in your calendar, understands the content of your recent emails, and remembers that three weeks ago you were looking for a flight to Berlin. This AI will be able to become proactive, suggesting things before you ask for them.

3. Context Standardization

As more and more applications are built on the foundation of AI, we will need standards. A formalized Model Context Protocol will need to emerge, defining how an application (e.g., your email) can safely share context with another application (e.g., your AI assistant) without compromising your privacy.

Conclusion: Why Does MCP Matter to You?

As someone entering the world of technology, whether as a programmer or an enthusiast, the concept of the Model Context Protocol (MCP) is fundamental.

Understanding MCP means knowing that modern software is no longer about isolated commands, but about conversations. We are no longer just building tools, but digital partners.

• If you want to be a Frontend Developer, you will implement an MCP using Context API in React to maintain the state of the interface.
• If you want to be a Backend Developer, you will implement an MCP using sessions, JWT tokens, and databases to manage user data.
• If you want to be an AI/ML Engineer, you will design a sophisticated MCP using techniques such as RAG and vector databases to give LLM models the “memory” they need.

Model Context Protocol is more than just a buzzword; it is the description of the new paradigm in programming. It is the bridge between static data and fluid intelligence.