AI  

MCP Role in AI

Pre-requisite to understand this

To understand MCP clearly, it helps to be familiar with:

  • Large Language Models (LLMs): AI models trained to understand and generate human-like text.

  • Client–server architecture: A system where clients request services and servers provide them over a network.

  • REST/RPC style APIs: Interfaces for communication where REST focuses on resources and RPC focuses on calling remote functions.

  • Tool calling/function calling in LLMs: Letting AI models invoke external functions or tools to perform tasks beyond text generation.

  • Concepts of context, prompts, and tokens: Context is conversation history, prompts are input instructions, and tokens are chunks of text the model processes.

  • Basic understanding of agent-based AI systems: AI systems where autonomous agents perceive, decide, and act to achieve goals.

Introduction

MCP (Model Context Protocol) is an open protocol that standardizes how AI models communicate with external tools, data sources, and services. Instead of tightly coupling AI models to specific APIs or plugins, MCP introduces a clean separation between the AI (client) and the systems that provide data or actions (servers). This allows AI systems to dynamically discover, request, and use capabilities in a consistent, secure, and scalable way.

In short, MCP turns AI models into first-class clients of real-world systems without hardcoding integrations.

What problem can we solve with MCP?

Without MCP, integrations to AI systems are often:

  • Custom-built

  • Hard to maintain

  • Tightly coupled to one model

  • Difficult to reuse across different AI providers

Problems MCP solves:

  • Hardcoded tool integrations per model

  • Vendor lock-in (OpenAI-only, Claude-only tools)

  • Poor scalability of AI-agent systems

  • Inconsistent tool schemas and calling methods

  • Security risks from direct system access

With MCP, we can:

  • Standardize AI to tool communication

  • Reuse tools across multiple AI models

  • Enable multi-agent and autonomous systems

  • Securely expose enterprise systems to AI

  • Build composable AI architectures : Designing AI systems in a way where different AI components or modules can be combined, replaced, or reused easily, much like building blocks

How to implement/use MCP?

MCP uses a client-server architecture:

  • MCP Client → Typically the AI application or agent

  • MCP Server → Exposes tools, resources, and prompts

  • Transport → stdio, HTTP, WebSocket, etc.

High-level implementation flow:

  • Build an MCP Server that exposes:

    • Tools (functions/actions)

    • Resources (files, DB records)

    • Prompts (templates)

  • Connect an AI model as an MCP Client

  • Let the model dynamically discover and invoke capabilities

Typical use cases:

  • AI accessing company databases

  • AI reading/writing files

  • AI controlling DevOps or CI pipelines

  • AI-powered IDEs

MCP Sequence Diagram

seq

This diagram shows how MCP enables structured interaction between an AI model and external systems.

Steps:

  • User initiates a request

  • AI Model (MCP Client) interprets intent

  • Client discovers available tools dynamically

  • AI selects and invokes the appropriate tool

  • MCP Server executes the request safely

  • Result is returned in a structured format

  • AI uses the result to generate a final response

Why this matters:

  • AI never directly accesses systems

  • Tools are discoverable, not hardcoded

  • Clear separation of responsibilities

  • Enables autonomous reasoning + action loops

  • MCP Component Diagram

Component Diagram

This diagram illustrates the logical architecture of MCP in an AI system.

comp

  • LLM (MCP Client): The AI model that sends structured requests to MCP, reads tool metadata, and generates responses based on results.

  • MCP Server: Acts as the central coordinator, enforcing access rules and executing requested tools safely.

  • Tool Registry: Maintains a catalogue of available tools, including their actions, input/output schemas, and usage contracts.

  • Resource Manager: Manages access to files, databases, and documents that the AI or tools may need.

  • Enterprise Systems: Backend systems (databases, APIs, files) that remain secure and isolated, accessed only through MCP.

Architectural benefits:

  • Loose coupling

  • Replaceable AI models

  • Centralized governance

  • Clear security boundaries

Advantages of MCP

  1. Plug-and-play AI integrations

  2. Model-agnostic

  3. Secure access to sensitive systems

  4. Reusable tools across projects

  5. Better agent reasoning and autonomous

  6. Scalable enterprise AI architecture

  7. Clear schemas and contracts

  8. Faster development

Summary

MCP (Model Context Protocol) is a foundational protocol that transforms how AI systems interact with the real world. By standardizing tool discovery, invocation, and data access, MCP removes tight coupling between AI models and external systems. This enables scalable, secure, and reusable AI architectures that support advanced use cases like autonomous agents, enterprise AI, and multi-model ecosystems. MCP is not just a tooling solution—it is an architectural shift toward AI as a first-class system client.

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