Imagine you’re using ChatGPT or any AI model, and you want it to handle tasks like reading files from your computer, sending emails, or even writing code for you.
Right now, how does that usually work?
You manually open files, upload them to the AI interface, and then ask the model to process them — like uploading images, code files, or documents, and then waiting for a response.
This takes time and effort. But technology doesn’t stop evolving.
Recently, a new concept called MCP has emerged. MCP stands for Model Context Protocol, and its goal is to simplify how AI models interact with external tools and systems.
The Core Idea Behind MCP
Instead of every developer writing custom code to connect an AI model to files or services, MCP provides a universal standard that everyone can use. This makes things easier for developers and more efficient for the AI itself, saving time and reducing repetitive work.
For example:
If you’re using GPT-4 and want it to automatically interact with Gmail, instead of writing brand-new code from scratch, you could simply use MCP.
All you’d need to do is set up a single connection point and tell the AI: “Go and work on this service,” just like telling someone: “Take the train and go to City X.”
Where Did the Idea Come From?
The idea came from a real-world problem:
Every developer was connecting AI models to systems in their own way.
Mohamed wrote code one way, Ali another, and Youssef yet another — leading to confusion and lack of compatibility between systems.
There was no unified standard, which made integrating AI into real-world systems complex and inefficient.
So, MCP was created as a standardized way for developers and AI models to access resources like Gmail, GitHub, databases, servers, and more.
Who Developed MCP?
The company behind this idea is Anthropic, the same company that developed the powerful AI model Claude, a strong competitor to models like ChatGPT and Llama.
Claude is currently known as one of the best AI models for coding and software development, which is why Anthropic was able to introduce something innovative like MCP.
How Does MCP Work in Practice?
Let’s say you’re using GPT, Claude, or any other model, and you want it to interact with services like Gmail or GitHub.
Without MCP, you’d have to:
- Write custom code from scratch.
- Connect to the service via API or FTP.
- Handle authentication and permissions.
- Deal with errors when they occur.
With MCP, it becomes much simpler:
- You install an MCP Client inside your application or model.
- You provide it with an access token (like a GitHub token).
- Then, you simply ask the AI to perform a task, such as:
- “Read 1000 code files from the server”
- “Open a Pull Request on GitHub”
- “Send an email”
The model will connect to the service through an MCP Server, using a standard protocol called JSON-RPC, which is well-known among developers.
Why JSON-RPC Matters Here
JSON-RPC is a reliable, older protocol that allows for Remote Procedure Calls — meaning you can ask a remote system to run a function and get the result back.
This means:
- You can tell the AI: “Go and fetch this file”
- In the background, MCP handles the server communication
- The AI doesn’t need to know all the technical details — only that it has context, or knowledge of available resources and how to interact with them.
A Hands-On Example: Using MCP with Cursor IDE
In one experiment, I used a tool called Cursor IDE, which is similar to Visual Studio Code.
I installed an MCP Client inside it and added a GitHub token so the AI could interact with repositories there.
I asked it to:
- “Open a Pull Request”
- “Scan the code in the repository”
- “Check if it exists in my second repo”
- “If not, create a new repo and write a test in it”
Unfortunately, the connection failed, showing an error:
"Client connection failed"
Now, the question is:
Was the issue with MCP?
Was the token wrong?
Was the server down at that moment?
Or was there a configuration mistake?
These are the kinds of challenges we face when using new technologies like MCP. It’s still early, and not widely tested yet.
Can AI Solve All Problems by Itself?
No. AI is just a tool — like a hammer or a car.
The person who guides and manages the process is the software engineer.
If you want to succeed in the world of AI, you must understand the basics:
- How networks work
- How programs handle data
- How APIs are built
- How different systems communicate with each other
Technology changes constantly, but a deep understanding of fundamentals is what enables you to adapt to every change.
Is MCP the Final Solution for Everything?
Not exactly.
MCP is a step toward the future, but it’s not a replacement for programming or technical understanding.
Even with MCP, when something goes wrong, you need to know:
- Why did it happen?
- How to fix it?
- Was the problem in the server?
- Was the token invalid?
- Is the protocol unsupported?
And you can’t understand any of that unless you know how systems work under the surface.
Summary
- MCP (Model Context Protocol) is a new standard that helps AI models interact with external systems in a unified way.
- It’s not a replacement for programming, but a tool that makes developers’ lives easier.
- The future belongs to standardized tools, and MCP may be one of the most important steps in that direction.
- If you’re interested in AI, learning the fundamentals of programming and networking is essential.
- MCP is still in its early stages, and many challenges remain to be solved.
- You are the one driving the process — not AI alone.