The world of artificial intelligence is moving fast. Every week, it seems like there’s a new tool, framework, or model that promises to improve AI.
But as developers develop more AI applications, a bigger problem continues to emerge: the lack of context.
Each tool works on its own. Each model has its own memory, its own data, and its own way of understanding the world. This makes it difficult for different parts of the AI ​​system to talk to each other.
That’s where the Model Context Protocol, or MCP, comes in.
It’s a new standard for how AI tools share context and communicate. It allows large language models and permissions AI agent To systematically integrate external data sources, apps, and tools.
MCP is like the missing piece that helps AI systems work together instead of apart.
MCP is becoming one of the most important theories in modern AI development. In this article, you’ll learn how MCPAI combines tools and data sources, making advanced AI apps smarter, faster, and much easier to build.
Table of Contents
Imagine you are building a customer support chatbot using a major language model like GPT. The model may generate great responses, but it knows nothing about your actual customers.
To make it useful, you connect it to your CRM so it can search for customer records. You then connect it to your ticketing system to view open cases. You can also link it to a knowledge base for reference.
Each of these integrations is a separate task. You can make custom API calls, format responses, manage validation, and handle errors. Each new data source means more glue code. LLM does not naturally know how to interact with these systems.
Now imagine you have five or ten tools like your AI assistant, your search engine, your summary tool, and some automation scripts. Each stores information differently.
None of them share a context. If one model learns something about user intent, others cannot use it. You end up with silos of intelligence instead of connected ecosystems.
This is the problem that MCP was designed to solve.
What is Model Context Protocol?
A model context protocol is a standard that defines how AI systems should exchange context. It was introduced to make it easier for models, tools and environments to interact in a predictable way. You can think of it as an “API for AI context”.
At its core, MCP allows for three types of communication:
Models can request context from external tools or data sources.
Tools can update the model or send back new information.
Both can share metadata about what they know and how they can help.
It sounds technical, but the result is simple. This makes AI apps more aware of their environment.
Instead of manually wiring integrations, developers can rely on a common protocol that defines how everything fits together.
From plugins to protocols
To understand MCP, it helps to see how OpenAI has handled this problem before.
when Chat GPT plugin Introduced, they allowed GPT models to access external APIs, for example, to book a flight, get weather updates, or search the web. Each plugin had its own schema that specified what data it could handle and what actions it could perform.
MCP takes this idea further. Rather than a plugin designed just for ChatGPT, MCP defines a universal language that any AI system can use. It’s like moving from private integration to open standards.
If you’ve ever worked with APIs, you might think of MCP as to AI what HTTP did to the web. HTTP allowed browsers and servers to communicate using common rules. MCP allows models and tools to share context consistently.
Below is a pseudocode example that demonstrates how you can develop a Model Context Protocol (MCP) server that exposes a SQL database through the context of AI models.
This is theoretical pseudocode. It captures flow, not specific syntax, and handles an MCP-compliant environment where LLMS can request data from external tools through a standard interface.
The goals are to expose your SQL database (for example, a customers or orders table) through an MCP server so that an AI model can query and understand its contents in context. For example, you can say “Show me all pending orders.”
// MCP SQL Context Server Pseudocode
---
// Step 1: Initialize server and dependencies
MCPServer = new MCPServer(name="SQLContextServer")
Database = connect_to_sql(
host="localhost",
user="admin",
password="password",
database="ecommerce"
)
// Step 2: Define available context schemas
// These describe what data the server can provide
MCPServer.register_context_schema("orders", {
"order_id": "integer",
"customer_name": "string",
"status": "string",
"amount": "float",
"created_at": "datetime"
})
// Step 3: Define request handler for context queries
MCPServer.on_context_request("orders", function(queryParams):
sql_query = build_sql_query(
table="orders",
filters=queryParams.filters,
limit=queryParams.limit or 50
)
results = Database.execute(sql_query)
return MCPResponse(data=results)
)
// Step 4: Define actions (optional)
// Allows the model to perform updates, inserts, etc.
MCPServer.register_action("update_order_status", {
"order_id": "integer",
"new_status": "string"
}, function(args):
Database.execute("UPDATE orders SET status = ? WHERE order_id = ?",
(args.new_status, args.order_id))
return MCPResponse(message="Order updated successfully")
)
// Step 5: Start the MCP server and listen for model requests
MCPServer.start(port=8080)
log("MCP SQL Context Server is running on port 8080")
// Example of how a model might call this server:
//
// Model -> MCPServer:
// RequestContext("orders", filters={"status": "pending"})
//
// MCPServer -> Model:
// ({"order_id": 42, "customer_name": "John Doe", "status": "pending", "amount": 199.99})
How it works:
The model sends a request via MCP, such as a context request
orders where status="pending".The server translates this into a SQL query, fetches the data, and returns it as a structured context.
The model now uses this perspective to provide accurate responses, automate workflows, or make decisions (such as “send refund email on pending orders older than 5 days”).
Optional MCP Actions Let the model perform secure updates, enabling a two-way workflow (in context, Action Out).
Improving AI apps
Intelligence in AI doesn’t just come from model size. It also comes from how contextual the model is.
A small model with rich context can improve a large one that is unaware of its surroundings. With MCP, a model can access the right context at the right time.
For example, let’s say a customer support bot receives a message,
“I’m still waiting for my refund.”
Typically, the model can respond with general amnesty. But with MCP, it can pull the customer’s order history from a connected device, check the refund status, and respond with the like.
“Your refund for order #1423 has been processed and should arrive in your account by Tuesday.“
This is possible because MCP invokes model requests from external sources using struct calls. Now it doesn’t work blindly. It works with context, making responses more relevant and accurate.
As more tools adopt MCP, models will become context-aware in multiple domains, from finance and healthcare to software development and education.
Making AI Apps Faster (and Easier)
Speed ​​in AI applications is not just about how quickly a model generates text. True speed comes from how efficiently the system gathers, processes and applies information.
Without MCP, AI systems waste time performing repetitive tasks such as fetching data from various sources, cleaning it, and transforming it into compatible formats.
Each new integration adds latency. Developers often create caching layers, write adapters, or simply batch process data to make things run smoothly. All this adds complexity and slows down development.
MCP removes most of this overhead. Because it defines a common structure for context, models and tools can seamlessly exchange data. There is no need to translate or edit the information, because everything speaks the same language. The result is low latency, fast response, and a clean architecture.
Consider an example: you’re building an AI coding assistant. Without MCP, you would need to manually connect to your file system, your Git repository, and your IDE, each requiring different integrations.
With MCP, all three can communicate through a common protocol. Assistant instantly understands where your code resides, what files have changed, and what actions it can perform.
This simplicity benefits not only developers but also users. With MCP, your context, your preferences, recent tasks, and open projects can travel with you across different apps. It’s like having a portable memory layer for the AI ​​world, letting each device know what you’re doing no matter where you go.
big picture
The rise of MCP signals a shift in how we think about AI systems. We are moving from isolated models to connected ecosystems.
In the early days of the web, each site was its own island. Then came standards like HTTP and HTML, which made everything interoperable. That’s when the web really exploded.
AI is on a similar point. Right now, every company is building its own stack, its own integration, notation and memory systems. But this approach is not scale. MCP can be the layer that connects them all.
Once contextualized and actionable, AI apps can collaborate in new ways. A writing assistant can talk to your research tool. A design bot can work with your file system. Coding Assistant can coordinate with your deployment manager.
This kind of shared intelligence is what makes AI truly useful. It’s no longer about one model doing everything. It’s about many specialized models working together seamlessly.
The result
MCP is still new, but the idea behind it is powerful. By creating a common protocol for the context, it lowers the barrier to innovation.
Developers can focus on what their AI does, not how it connects. Companies can develop products that work well with others instead of locking customers into closed systems.
In the long term, this could lead to an open AI ecosystem, where models, tools and data sources communicate freely, much like websites do today. You can mix and match skills without friction.
The goal is not just smart AI, but simple AI. AI that understands what’s going on around it, reacts in real time, and works naturally with the tools you already use.
The Model Context Protocol is a big step towards that future. It’s the bridge between intelligence and context, and it’s what will make tomorrow’s AI systems faster, more reliable, and far more human in how they perceive the world.
Hope you enjoyed this article. Sign up for my free AI newsletter turingtalks.ai For more tutorials on AI. You can also search Visit my website.