A Comprehensive Guide to Understanding MCP (Model Context Protocol) and Its Importance

What is MCP?

To understand the meaning of MCP, let's break down the acronym letter by letter:

M: Stands for Model, specifically referring to Large Language Models (LLMs) like ChatGPT, Grok, or others capable of processing natural language (human language).

C: Stands for Context, the context in which interactions with the AI model occur.

P: Stands for Protocol, a set of rules governing the communication process (similar to protocols used when receiving a head of state).

Therefore, MCP means: Model Context Protocol. It is a standardized set of rules agreed upon (by developers) to define the conversational context for interacting with AI models.

Why Do We Need MCP?

Today, web applications communicate with each other using established standards like REST, gRPC, and GraphQL.

However, these protocols do not support natural language. You cannot interact with them directly using human language. Instead, they require precise technical knowledge and the writing of structured requests by a programmer. 

This is where MCP comes in:

The MCP protocol allows web applications to communicate with each other using natural language via LLMs.

Example:

If you wanted to get the Bitcoin price from an API previously, you had to:

• Search for the API documentation.
• Understand how to send the request.
  
• Manually write an HTTP request according to the instructions.

Whereas if a site supports MCP, you can simply write: "What is the price of Bitcoin today?" The LLM will understand your question, communicate with the API via MCP, and fetch the price for you – no code writing or documentation reading required.

Solving the Problem of Outdated Information in LLMs

As you know, LLMs are trained on historical data and cannot access information created after their training cutoff date.

For example, if a model was trained up to the end of 2024, it knows nothing about 2025.

If you ask about today's weather or the current Bitcoin price, you might get outdated answers that don't reflect reality. But... by using MCP, the model can be informed that a specific site provides weather data or cryptocurrency prices. The model can then communicate directly with the site (via MCP) and fetch accurate, up-to-date information for you.

A Revolution in Application Design

Using MCP, you are not forced to restrict users to a fixed set of buttons or predefined interfaces. You can build an application that interacts entirely through conversation.

Example:

Imagine a banking application where the user types:

"Transfer $100 from my account to Muhammad Al-Ahmadi's account."

The LLM would then:

Understand the intent of the sentence.

Search the associated MCPs for the money transfer service.

Potentially also search for a service providing the IBAN number associated with a specific name (here, "Muhammad Al-Ahmadi").

Send the requests on the user's behalf.

All this… without writing code, without reading documentation, and without prior knowledge of the request format.

How Do I Start Developing MCP-Supported Applications?

The developers of this protocol have prepared several Software Development Kits (SDKs) to easily build MCP applications in major programming languages, including:

 

• Python
  
• JavaScript
  
• Java
  
• Kotlin
  
• C#
  
• Swift

 

They have also written several small example applications in the languages above. You can visit their official website today and start learning:

https://modelcontextprotocol.io/introduction