Model Context Protocol (MCP): Structuring Intelligence in AI Systems
As artificial intelligence systems grow more complex and interconnected, one of the biggest challenges isn't just how models think—it is the way they offer their knowledge. That’s where the Model Context Protocol (MCP) comes in.
What is the Model Context Protocol (MCP)?
The Model Context Protocol is a proposed framework that standardizes the way AI models store, transmit, and retrieve contextual information. Whether it's a large language model chatting with a user or a multi-agent system collaborating on a task, MCP ensures all models operate with a shared understanding of "what's going on."
MCP is not just a data format — it’s a semantic and procedural contract that governs:
What context is,
How it's serialized and transferred,
Who owns or modifies it, and
How models consume it consistently.
Why Does MCP Matter?
Today’s AI models often operate in silos — each with its own memory, prompt, and understanding of the environment. This fragmentation leads to:
Loss of important state between interactions
Repetition or inconsistency in responses
Inefficient coordination between models
With MCP, we aim to bridge these gaps by enabling persistent, shareable, and modular context that travels with the user or task.
API vs MCP: What’s the difference, and why does it matter?
Traditional API: You’re ordering from a fixed menu at a restaurant. You must select specific items (endpoints), provide exact specifications (parameters), and the server (API) takes your order to the chef. If what you want isn’t on the menu, you’re out of luck
MCP: You’re telling the chef directly about your preferences and dietary needs. “I like spicy food with complex flavours,” and the chef—understanding cooking fundamentals—creates a custom dish that meets your requirements. There’s no fixed menu limiting what’s possible.
Core Components of MCP
The Model Context Protocol (MCP) is built on a flexible, extensible architecture that enables seamless communication between LLM applications and integrations. This document covers the core architectural components and concepts.
Overview
MCP follows a client-server architecture where
Hosts are LLM applications (like Claude Desktop or IDES) that initiate connections
Clients - maintain 1:1 connections with servers, inside the host application
Servers - provide context, tools, and prompts to clients
Example Use Case: AI Assistant Suite
Imagine a suite of AI agents — a calendar bot, an email summarizer, and a travel planner — all serving the same user.
Without MCP:
Each model would require its own prompts, data access, and partial memory.
You might get mismatched responses or repetitive queries.
With MCP:
All agents access a shared CSO reflecting the user’s schedule, preferences, and ongoing tasks.
Coordination becomes seamless: “Reschedule the meeting” triggers context updates across all agents.
Benefits of MCP in AI Architectures
Persistent Memory: Share context across sessions, not just calls.
Multi-Model Coherence: Enable LLMs, vision models, and tools to align on state.
Composable Intelligence: Build intelligent systems where models plug in like microservices.
Privacy-Aware Design: Control what each model sees and logs, improving transparency and safety.
Challenges Ahead
Defining a universal schema that works across domains
Governance of context in dynamic environments
Ensuring data privacy and avoiding context leakage
Conclusion
In a future where AI agents are embedded everywhere — from your car to your doctor’s office — the Model Context Protocol could serve as the foundational glue that gives machines memory, responsibility, and alignment. As MCP evolves, we may see it integrated into AI operating systems, enterprise stacks, and even edge devices.