Fonyuy Gita

Posted on Dec 10

MCP Isn’t Hard, Here’s the Easiest Beginner-Friendly MCP MASTERCLASS EVER🤗 (PART 1)

Welcome Back

I have been away for a while, caught up with work and life moving faster than expected. But amid the pressure, I kept feeling that pull to come back to what I truly enjoy. Sharing knowledge. Breaking down complex ideas. Helping beginners take their first confident steps into the world of AI.

So today I am back on dev.to, refreshed, recharged, and ready to continue the journey with you. We are kicking things off with something exciting. MCP. A new way of building powerful tools and creating smarter interactions between apps and AI systems.

This is the perfect moment to start learning it. Welcome to "MCP Isn't Hard, Here's the Easiest Beginner-Friendly Tutorial Ever."

If you have been curious about MCP, or if you have ever wondered how to set it up and even build your own MCP server with Python, this is the guide you have been waiting for. Let's dive in.

Part 1: The Foundation

Chapter 1: The Evolution of AI - From Simple Models to Intelligent Agents

1.1 The Great AI Misconception - When Did AI Really Begin?
1.2 The Pioneers Who Dreamed of Thinking Machines
1.3 The Long Road - Breakthroughs and Setbacks Before 2022
1.4 November 2022 - The Moment Everything Changed
1.5 From Conversations to Actions - Understanding AI Agents
1.6 The Integration Crisis - Why We Need MCP Today

Chapter 1: The Evolution of AI - From Simple Models to Intelligent Agents

1.1 The Great AI Misconception - When Did AI Really Begin?

Let me tell you a story. Over the past few years, I have had the privilege of speaking at events, leading workshops, hosting podcasts, visiting universities, and sitting with bright young minds who are hungry to understand artificial intelligence. Everywhere I go, from small community meetups to large tech gatherings, I keep hearing the same surprising idea. People believe that AI was basically invented in November 2022 when ChatGPT was released to the world.

It always makes me smile, not because the thought is wrong, but because it shows how powerful and unforgettable that moment was. The truth is that AI did not suddenly appear in a single month. What happened in 2022 was not the birth of AI but the moment the world finally woke up to it. It was the moment AI left research labs, stepped out of academic papers, and walked straight into everyday life. Students felt it. Developers felt it. Even people who never cared about technology felt it too.

Every time I share this with young learners, something changes in the room. They begin to understand that AI has a long story, shaped by decades of experiments, failures, breakthroughs, and bold ideas. They start to see themselves as part of that story. Not as spectators, but as the next generation of builders, thinkers, and innovators.

That is when the real conversation begins.

I remember a text message from a student, who I won't name 😏, boldly saying, "Ever since AI started…" (he meant ChatGPT 😂).

This misconception is everywhere. Turn on the news, listen to government officials talk about AI policy, read business articles about the AI revolution, and you will hear this narrative that artificial intelligence sprang into existence sometime around 2022. It is as if we collectively decided that history began the moment ChatGPT could write our emails and help us debug code.

But here is the truth that I peacefully shared with that student. Artificial intelligence has been a problem, a dream, and an obsession for brilliant minds for over seventy years.

1.2 The Pioneers Who Dreamed of Thinking Machines

The real story of AI begins not in Silicon Valley boardrooms or with billion-dollar training runs, but with a brilliant British mathematician named Alan Turing. In 1950 (yes, 1950) Turing published a groundbreaking paper titled "Computing Machinery and Intelligence" in the philosophy journal Mind. This was not a minor academic footnote. This paper introduced what we now call the Turing Test and asked a question that still haunts and inspires us today. "Can machines think?"

What strikes me most about reading Turing's original work is how contemporary it feels. He did not just ask if machines could think. He cleverly reframed the question by proposing what he called the "imitation game," where a human interrogator would try to distinguish between a human and a machine through conversation. Sound familiar? We are essentially still grappling with variations of this same test seventy-four years later.

Turing was not working in a vacuum. Researchers in the United Kingdom had been exploring machine intelligence for up to ten years before 1956, when AI was formally named as a field. This was a community of thinkers who believed that intelligence (that most human of qualities) could perhaps be understood, replicated, and even improved upon by machines.

Want to dive deeper? I highly recommend reading Turing's original 1950 paper. It is surprisingly accessible and will give you chills when you realize how far ahead of his time he was.

Then came the summer of 1956, a moment that officially birthed the field we call artificial intelligence. The Dartmouth Summer Research Project on Artificial Intelligence kicked off on June 18, 1956, organized by four American computer scientists: John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. You can read more about this historic gathering in the original Dartmouth proposal and this excellent IEEE Spectrum article on the birth of AI at Dartmouth.

These were not just dreamers throwing around ideas. In their proposal, McCarthy and his colleagues stated their belief that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." That is an audacious claim. They thought they could crack the code of intelligence itself in a single summer with ten carefully chosen researchers.

They were spectacularly wrong about the timeline but beautifully right about the possibility.

1.3 The Long Road - Breakthroughs and Setbacks Before 2022

Here is where the story gets really interesting, and where we learn some of the most important lessons about AI development. The years following Dartmouth were not a smooth climb toward ChatGPT. They were a rollercoaster of wild optimism, crushing disappointment, and the kind of resilience that defines great scientific endeavors.

The 1960s and early 1970s saw genuine excitement. Researchers were making progress on problems that seemed impossibly hard. Computers were learning to play checkers and chess, proving mathematical theorems, and solving algebra problems. By the mid-1960s, artificial intelligence research in the United States was being heavily funded by the Department of Defense, and AI laboratories had been established around the world.

But then came what we now call the First AI Winter.

From 1974 to 1980, AI funding declined drastically in what became known as the First AI Winter. Why? Because researchers had made promises they could not keep. AI researcher Hans Moravec put it bluntly. "Many researchers were caught up in a web of increasing exaggeration. Their initial promises to DARPA had been much too optimistic."

The breaking point came from multiple directions. In 1973, Professor Sir James Lighthill was asked by the UK Parliament to evaluate AI research. His report, now called the Lighthill Report, criticized what he saw as AI's failure to achieve its "grandiose objectives." The impact was swift and brutal. Following this report, the UK government dramatically cut AI research funding, essentially eliminating support for most AI work throughout British universities.

In the United States, DARPA funding for AI research plummeted from approximately thirty million dollars annually in the early 1970s to almost nothing by 1974. Labs closed. Researchers left the field or moved into other areas. The term "artificial intelligence" became toxic in funding proposals.

This was not just about money. The knowledge diaspora began as researchers moved into adjacent fields or left academia entirely, meaning that when AI interest revived in the 1980s, much institutional knowledge had to be rebuilt from scratch.

The field recovered in the 1980s with expert systems, showing commercial promise. By 1985, corporations were investing over a billion dollars annually in AI, focusing on in-house AI departments and companies like Teknowledge and Intellicorp. But history repeated itself. When desktop computers from Apple and IBM became more powerful than expensive specialized LISP machines in 1987, the market collapsed. The Second AI Winter had arrived.

These were dark times. The AI winter extended even to the Turing Awards, as between 1995 and 2010, sixteen successive selection committees found that AI had not produced advances matching progress in areas like databases, cryptography, and networking.

For those of us who lived through or studied these periods, the lessons are clear: hype without delivery kills fields. Unrealistic promises lead to unrealistic disappointments. And yet, throughout both winters, dedicated researchers kept working, kept believing, kept pushing forward.

You can learn more about these challenging periods in this comprehensive DataCamp article on AI Winter history.

1.4 November 2022—The Moment Everything Changed

So what made 2012 (not 2022, we will get there) the turning point? What finally broke the cycle of boom and bust?

Remember this moment: The answer is AlexNet.

Image source: Pinecone - ImageNet Series

In September 2012, a team called SuperVision, consisting of Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, submitted a deep convolutional neural network to the ImageNet Large Scale Visual Recognition Challenge. AlexNet achieved a top-five error rate of 15.3 percent, winning the contest by more than 10.8 percentage points above the runner-up. This was not an incremental improvement. This was a revolution.

Let me explain why this mattered so much. For years, computer vision researchers had been making tiny, incremental progress on image recognition. Then AlexNet came along and, as one researcher described it to me, "blew the doors off." The runner-up had an error rate of 26.2 percent. AlexNet cut that almost in half.

What made AlexNet possible? Three things converged: deep neural networks that researchers had been refining for decades, ImageNet's massive labeled dataset completed in 2009, and GPUs that provided enough computational power to train these massive models. You can read the groundbreaking technical details in the original AlexNet paper.

Fei-Fei Li, who created ImageNet, later reflected that this moment was significant because "three fundamental elements of modern AI converged for the first time." It was not that any one piece was new. Neural networks had been around since the 1980s. Large datasets were growing. GPUs were getting faster. But putting them together at the right time, with the right architecture, changed everything.

After AlexNet, the dam burst. Researchers who had been skeptical of deep learning suddenly became believers. Investment poured back into the field. Within a few years, we had architectures like ResNet, VGGNet, and GoogleNet, each pushing the boundaries further.

But AlexNet was about computer vision. The moment that made AI feel like magic to regular people came ten years later, in November 2022, when OpenAI released ChatGPT to the public.

Remember this moment? Watch Sam Altman's announcement:

I remember that week vividly. My Twitter feed exploded with friends asking, "Have you tried this thing?" Within five days, ChatGPT had a million users. Within two months, one hundred million. It was the fastest-growing consumer application in history.

What made ChatGPT different from earlier AI systems? It was not necessarily more capable than GPT-3, which had been around since 2020. But it was accessible. It had a simple chat interface that anyone could use. It was free. And crucially, it arrived at a moment when the technology was finally good enough to be genuinely useful for everyday tasks.

People were using ChatGPT to write emails, debug code, plan trips, learn new concepts, and even just have interesting conversations. For the first time, AI felt less like a research project and more like a tool anyone could pick up and use.

But as humans, we always want more, so...

1.5 From Conversations to Actions - Understanding AI Agents

Now here is where things get really interesting, and where we start heading toward why you are reading this tutorial about MCP.

ChatGPT was impressive, but it had a fundamental limitation: it could only talk. It could not do anything. You could ask it to help you write a report, but it could not actually create the document in Google Docs. You could ask it about your calendar, but it could not check or modify your actual calendar. It was like having a brilliant assistant who was locked in a soundproof booth and could only communicate through written notes.

This is where the concept of AI agents comes in.

An AI agent is not just a conversational model. It is an AI system that can perceive its environment, make decisions, and take actions to achieve specific goals. Think of it as the difference between someone who can tell you how to cook a meal versus someone who can actually cook it for you.

The agent paradigm represents a fundamental shift in how we think about AI. Instead of AI as a clever text generator, we now think of AI as a system that can observe its environment through various tools and APIs, reason about what actions to take based on its observations and goals, execute actions in the real world through integration with other systems, learn from the outcomes of its actions, and persist and work autonomously toward complex, multi-step objectives.

Companies like Anthropic, OpenAI, and Google have been racing to build these capabilities. The vision is an AI that does not just advise you but actually helps you get things done.

But here is the problem we ran into.

1.6 The Integration Crisis—Why We Need MCP Today

As AI agents became more sophisticated, we hit a wall. And it was not a wall of intelligence or capability. It was a wall of integration.

Let me paint you a picture of the problem. Imagine you are building an AI agent that needs to help users with their daily work. This agent needs to read and write emails (so it needs to connect to Gmail or Outlook), access and create calendar events (Google Calendar, Outlook Calendar), retrieve files from cloud storage (Google Drive, Dropbox, OneDrive), query databases (PostgreSQL, MySQL, MongoDB), search through documentation (Notion, Confluence), fetch data from APIs (weather, stock prices, news), execute code and run computations, and access the web for current information.

In the current landscape, connecting your AI to each of these services requires writing custom integration code for each service, learning each service's unique API, handling authentication differently for each one, managing rate limits, error handling, and retries, keeping up with API changes and deprecations, securing credentials for each service, and testing each integration thoroughly.

Multiply this by the hundreds of tools and services that users might want to connect to their AI, and you have a massive integration nightmare.

This is not a theoretical problem. This is the problem I see teams struggling with right now. Every AI company is building the same integrations over and over again. Every developer who wants to create an AI-powered app needs to solve the same connection problems. There is massive duplication of effort, and the result is a fragmented ecosystem where AI systems cannot easily talk to the tools they need.

We have been here before. In the early days of the web, every application had its own protocol for communication. Then HTTP standardized how systems talk to each other over networks, and the web exploded. Email had dozens of competing protocols until SMTP, POP, and IMAP became standards.

This is exactly the moment we are at with AI right now. We have powerful models that could do incredible things, but they are trapped in silos, unable to easily access the data and tools they need.

And this, finally, brings us to why you are here reading this tutorial.

In late 2024, Anthropic introduced the Model Context Protocol (MCP). You can read about it in their official announcement. Introducing the Model Context Protocol. It is an open standard designed to solve this exact problem. Instead of building custom integrations for every possible service, MCP provides a universal way for AI systems to connect to data sources and tools.

Think of MCP as the HTTP of AI. Just as HTTP standardized how computers exchange web pages, MCP aims to standardize how AI systems exchange context and capabilities with external tools and services.

But here is what makes this moment exciting: we are getting in at the beginning. MCP is still young. The ecosystem is just forming. The standards are being refined. And developers who learn MCP now will be the ones building the next generation of AI-powered applications.

We have spent seventy years getting from "Can machines think?" to machines that can genuinely assist us with complex tasks. We have weathered two AI winters. We have seen the breakthrough of deep learning, the emergence of large language models, and the dawn of the agent era.

And now, we are at the next frontier: making all of these incredible AI capabilities actually accessible and useful through standardized, reliable connections to the tools and data they need.

That is what MCP is about. That is why it matters. And that is what the rest of this tutorial will teach you to build with.

In the next chapter, we will dive into what MCP actually is, how it works, and why some people are calling it the most important protocol since HTTP.

But first, take a moment to appreciate the journey we have covered. From Turing's 1950 paper to AlexNet's 2012 breakthrough to ChatGPT's 2022 moment to MCP's 2024 introduction, we are living through one of the most exciting times in the history of technology.

And you are about to learn how to build with the tools that will define its next chapter.

See you in Part 2, where we cover Chapters 2 to 4 (Understanding MCP).

Connect With Me

I hope this first chapter has sparked your curiosity and given you a deeper appreciation for the incredible journey that artificial intelligence has taken over the past seven decades. Understanding where we came from helps us better appreciate where we are going, and I am excited to continue this journey with you in the upcoming chapters.

If you have questions, thoughts, or just want to discuss AI and technology, I would love to hear from you. Learning is always better when it is a conversation, and I genuinely enjoy connecting with readers who are passionate about these topics.

Let's stay connected:

🐦 X (Twitter): Follow me on X at Fonyuy Gita for daily insights, updates on new tutorials, and discussions about the latest developments in AI and machine learning. I regularly share tips, interesting research papers, and behind-the-scenes thoughts on what I am working on.

I believe that the best learning happens in community, and I have built these channels specifically to support learners like you. Do not hesitate to reach out. Whether you are stuck on a concept, excited about a breakthrough you have had, or just want to say hello, I read and respond to every message.

Stay tuned for Part 2, where we will explore the technical architecture of MCP, set up your first MCP server, and begin building practical AI integrations that actually work. I promise you will come away with hands-on skills that you can start using immediately.

DEV Community