Model Context Protocol - The Why?
Introduction
The world of Artificial Intelligence has changed dramatically after the arrival of Large Language Models (LLMs) such as OpenAI’s ChatGPT. What initially started as a fascinating conversational chatbot quickly evolved into something much bigger. AI systems moved from being simple “question-answering tools” to becoming intelligent assistants capable of helping developers, analysts, writers, designers, and entire organizations in their day-to-day work.
However, as AI adoption increased, a deeper problem slowly started to appear. AI systems became powerful, but they were disconnected from the real working environment where actual data and workflows existed. Important information was scattered across tools like GitHub, Slack, Google Drive, Jira, databases, and internal company systems. Because of this fragmentation, AI assistants often lacked the complete context required to perform meaningful work.
This is the exact problem that the Model Context Protocol, commonly known as MCP, attempts to solve.
The Arrival of LLMs and the Beginning of a New Software Era
The story of MCP really begins with the public release of ChatGPT on November 30, 2022. Within just a few days, millions of users started experimenting with it, and it rapidly became one of the fastest-growing software products ever created. What made ChatGPT special was not merely its ability to answer questions. Computers had already been answering questions for decades. The real breakthrough was that, for the first time, humans could communicate with machines using natural language in a conversational way.
Before LLMs, our relationship with machines was mostly transactional. We pressed buttons, clicked menus, filled forms, or executed commands. Every interaction required us to adapt ourselves to the machine’s interface.
With LLMs, the interaction model changed completely. Instead of learning how machines work, humans could simply explain what they wanted in plain language. Suddenly, software started feeling less like a rigid tool and more like a collaborative assistant.
This shift may sound subtle, but it fundamentally changed how people thought about software.
The Three Waves of AI Adoption
The adoption of AI systems did not happen all at once. Instead, it evolved in multiple stages.
Stage 1 — The Stage of Wonder
In the early days of AI adoption, most people were not using AI for serious productivity or professional work. The entire phase was largely driven by curiosity, excitement, and a sense of amazement at what these systems were capable of doing. People simply wanted to explore the limits of this new technology and see how intelligently it could respond to unusual or imaginative prompts.
Users started asking AI all kinds of creative and absurd questions, not because they needed practical answers, but because they were fascinated by how naturally the system could respond. Someone might ask the AI to explain quantum physics from the viewpoint of a cat, while another person could request a Shakespeare-style song about pizza. Others experimented with bizarre hypothetical situations such as imagining a world where gravity worked in reverse.
Stage 2 — Professional Adoption
After the initial excitement settled down, people began asking a more serious question:
“Can this technology actually help me in my work?”
This was the beginning of the second wave of adoption.
Lawyers started summarizing contracts with AI. Developers started debugging code using AI assistants. Teachers started generating lesson plans and study material. Analysts began simplifying reports and extracting insights faster.
This stage was extremely important because people collectively realized that AI was not merely a toy. It could genuinely improve productivity.
Tasks that previously required six hours could sometimes be completed in three hours with AI assistance. The idea of AI becoming a “work partner” slowly became realistic.
Stage 3 — The API Revolution
The third wave began when companies started exposing AI capabilities through APIs.
OpenAI did not only release ChatGPT as a standalone product. It also released APIs that allowed developers to integrate LLM capabilities directly into their own applications.
This triggered an explosion of AI-powered software.
Examples included:
Microsoft integrating Copilot into Word and Excel Google adding AI features to Gmail and Docs AI-first tools such as Perplexity AI and Cursor emerging rapidly
AI was no longer limited to a single chatbot interface. It started appearing everywhere.
And this is exactly where a new problem began to emerge.
The Problem of Fragmentation
As AI became integrated into more software systems, organizations unknowingly created isolated AI ecosystems. For example:
- Notion had its own AI
- Slack had its own AI
- VS Code extensions had their own AI
- Internal enterprise systems had their own AI assistants
Each system could perform intelligent tasks inside its own environment, but none of them understood the complete picture.
This created fragmentation.
Imagine a developer working on a feature implementation:
- Requirements exist in Jira
- Discussions exist in Slack
- Code exists in GitHub
- Documentation exists in Google Drive
- Database schema exists in MySQL
- Deployment configuration exists elsewhere
The AI assistant inside one tool has no visibility into the data stored in another tool.
As a result, users constantly switch between applications, manually collecting information and feeding it into AI systems.
This was the opposite of the original AI vision.
People wanted one unified AI assistant capable of understanding their entire workflow end-to-end.
Instead, they received many disconnected AI systems.
Understanding Context
To understand MCP properly, you must first understand the idea of context.
In simple terms:
Context is all the information visible to an AI model when it generates a response.
For example, if you ask:
“What is quantum physics?” “Which books should I read to learn it?” “How difficult is it to master?”
The AI understands that “it” refers to quantum physics because the previous conversation history is part of the context.
Conversation history is one form of context.
But in professional systems, context becomes much more complex.
Context in Real Software Engineering Workflows
Consider a software engineer implementing Two-Factor Authentication (2FA) in a production system. To complete this task, the engineer may need:
- Jira tickets
- GitHub repositories
- Existing authentication code
- Database schemas
- Security guidelines
- Team discussions from Slack
- Infrastructure configurations
Notice something important here:
The context is scattered across multiple systems.
Unlike a simple chatbot conversation, professional work environments involve distributed context.
This creates a serious challenge for AI systems.
The Copy-Paste Problem
Without proper integrations, developers are forced to manually assemble context for AI systems. A typical workflow becomes painfully repetitive:
- Copy Jira ticket details
- Paste them into ChatGPT
- Copy relevant code files
- Paste them into ChatGPT
- Copy database schema
- Paste it into ChatGPT
- Copy Slack discussions
- Paste them too
Only after all this preparation can the developer ask the actual question. This process creates what many developers informally call “copy-paste hell.”
The bigger the project becomes, the worse the situation gets. A developer working on a massive enterprise codebase cannot realistically summarize everything manually for the AI assistant. The entire workflow becomes inefficient and unscalable.
This was one of the major limitations preventing the creation of truly unified AI assistants.
Function Calling — The First Major Breakthrough
To solve this problem, OpenAI introduced a powerful concept called Function Calling in 2023.
The idea was surprisingly simple. Instead of limiting LLMs to conversation only, developers could allow AI systems to invoke external functions programmatically.
For example, suppose an AI system has access to a function called:
load_file(filename)
The AI is also given a description explaining what the function does. Now, when the user asks:
Read the contents of abc.txt
The LLM understands that this request requires executing a tool rather than generating a plain text response. It automatically selects the appropriate function and calls it with the correct arguments. This changed everything. AI systems could now:
- Access databases
- Read files
- Perform web searches
- Query APIs
- Execute code
- Fetch GitHub repositories
AI assistants started evolving into intelligent orchestration systems rather than simple chat interfaces.
The Rise of AI Tools
Once function calling became popular, the AI ecosystem exploded with tools and integrations.
Companies built integrations for:
- Slack
- GitHub
- Google Drive
- Salesforce
- Databases
- Internal enterprise systems
AI-first products also started building specialized tools.
For example:
- Cursor added intelligent codebase access
- Perplexity AI added web browsing capabilities
- ChatGPT Plus introduced browsing and file upload support
This dramatically improved context assembly because AI systems could now fetch information automatically instead of depending entirely on manual copy-paste workflows.
The Hidden Problem with Tool Integrations
Although function calling solved one problem, it introduced another major challenge. Suppose a company uses:
- 3 AI chatbots
- 20 enterprise tools
Now imagine that every chatbot needs to integrate with every tool.
The total number of integrations becomes:
n×m
Where:
- n = number of AI systems
- m = number of external services
This creates an integration nightmare.
Each integration requires:
- Authentication handling
- API communication logic
- Error handling
- Data transformation
- Security management
- Maintenance
As APIs evolve, integrations break and require constant updates.
Organizations suddenly need dedicated engineering teams just to maintain AI integrations.
The original goal was to simplify developer workflows, but the integration burden itself became another huge engineering problem.
The Core Idea Behind MCP
This is the exact problem MCP attempts to solve.
Instead of every AI tool building its own custom integration for every external service, MCP introduces a standardized communication protocol.
MCP uses two major entities:
- Client
- Server
The client is typically the AI application itself, such as:
- ChatGPT
- Cursor
- Claude
- Perplexity
The server represents the external service or tool, such as:
- GitHub
- Slack
- Google Drive
- Databases
The communication between the client and server happens using a shared protocol called the Model Context Protocol.
MCP Architecture
An MCP-based architecture usually looks like this:
AI Client
|
|---- MCP Server (GitHub)
|
|---- MCP Server (Slack)
|
|---- MCP Server (Google Drive)
|
|---- MCP Server (Database)
The important difference is that the intelligence for handling integrations now moves to the server side.
MCP vs Traditional Tool Calling
This is one of the most important concepts to understand.
In traditional function calling:
- The server exposes APIs
- The client writes custom integration code
- Every AI application implements its own connector logic
In MCP:
- The server handles the heavy lifting
- The AI client only needs to understand MCP
- Integration logic becomes reusable
This means that if GitHub provides an official MCP server, then any MCP-compatible AI client can communicate with it without building a custom GitHub integration from scratch.
This dramatically simplifies the ecosystem.
The Server Does the Heavy Lifting
One of the most important ideas in MCP is this:
The server is responsible for the complexity. An MCP server handles:
- Authentication
- API communication
- Error handling
- Rate limiting
- Data transformation
- Business logic
The client only establishes a connection and communicates using the MCP protocol.
This separation of responsibilities reduces complexity significantly on the AI application side.
Benefits of MCP
Reduced Integration Complexity
Previously, companies needed:
n×m
integrations.
With MCP, the architecture becomes closer to:
m+n
This reduction is extremely important at enterprise scale.
Lower Maintenance Overhead
Since integration logic exists mainly on the server side, AI clients require fewer updates when APIs change.
The server provider handles compatibility updates internally.
Faster Development
Developers no longer need to write large amounts of repetitive integration code.
If an MCP server already exists, an AI application can connect to it immediately. This accelerates development dramatically.
Better Security
MCP also improves security organization.
Instead of spreading credentials and API keys across many integration files, configurations can be centralized and managed more cleanly.
This makes auditing and maintaining secure connections easier.