What is MCP (Model Context Protocol)?

Model Context Protocol (MCP) is a standardized way for AI models to interact with external systems, tools, data sources, and contextual information.

Think of MCP as a universal connector between an AI model and the resources it needs to perform useful work.

Without MCP:

User → LLM → Response

With MCP:

User
  ↓
LLM
  ↓
MCP
 ├─ Files
 ├─ Databases
 ├─ APIs
 ├─ Vector DBs
 ├─ Enterprise Applications
 └─ Tools

The primary goal of MCP is to help AI systems:

Access external knowledge
Use tools and APIs
Maintain context
Interact with enterprise systems
Execute actions safely and consistently

Analogy:

MCP is to AI what USB is to hardware: a standard way to connect different components without custom integrations for every new system.

Why Do We Need MCP?

A standalone LLM has limitations:

❌ Doesn't know your company's latest data
❌ Cannot directly query databases
❌ Cannot call APIs by itself
❌ Cannot access internal documents
❌ Cannot execute business workflows

MCP solves these limitations by enabling structured communication between the model and external resources.

Core Components of MCP

A typical MCP architecture consists of the following components:

1. Client

The client is the AI application that initiates requests.

Examples:

Chatbot
Copilot
Web application
Agentic workflow

User
 ↓
MCP Client

Responsibilities:

Send user requests
Request tools/resources
Receive responses

2. Host (LLM Application)

The host is the application running the AI model.

Examples:

ChatGPT-like application
Microsoft Copilot
Enterprise assistant
Custom AI agent

Responsibilities:

Process user intent
Decide when to use tools
Coordinate MCP communication

User
 ↓
Host Application
 ↓
MCP

3. MCP Server

The MCP Server exposes capabilities to the AI model.

Examples:

Database access
CRM system
HR application
File storage
Knowledge repository

MCP Server
 ├─ Database
 ├─ File System
 ├─ API
 └─ Business Application

Responsibilities:

Expose resources
Expose tools
Return data
Execute operations

4. Resources

Resources provide data to the model.

Examples:

PDF documents
Text files
SharePoint content
Database records
Knowledge articles

Example:

Employee Handbook
Leave Policy
IT Support Guide

The model can retrieve these resources through MCP.

5. Tools

Tools allow the AI to perform actions.

Examples:

Send email
Create ticket
Query database
Update CRM record
Fetch employee details

Example:

get_employee_details(employee_id)

Instead of answering from memory, the model can call the tool.

6. Prompts

MCP can expose reusable prompts.

Example:

Summarize this document

Generate a project status report

These prompts standardize common workflows.

MCP Workflow (Step-by-Step)

Let's use a simple scenario:

User asks:

Show employee details for employee 101

Step 1: User Sends Request

User
 ↓
Host Application

Example:

Show employee details for employee 101

Step 2: LLM Understands Intent

The LLM analyzes the question.

It determines:

This information is not in my context.
I need external data.

Step 3: Discover Available Tools

Through MCP, the model sees:

Tool:

get_employee_details()

Step 4: Tool Call Generated

The model generates:

{
  "tool": "get_employee_details",
  "employee_id": "101"
}

Step 5: MCP Server Executes Tool

LLM
 ↓
MCP Server
 ↓
HR Database

Example:

SELECT * FROM employees
WHERE employee_id = 101

Step 6: Server Returns Results

{
  "name": "John Doe",
  "title": "Engineer"
}

Step 7: LLM Generates Human-Friendly Response

The model receives the result and responds:

Employee 101 is John Doe and works as an Engineer.

Step 8: Response Sent to User

User
 ↑
Response

Process completed.

MCP Workflow Diagram

User
 ↓
Host Application
 ↓
LLM
 ↓
MCP Client
 ↓
MCP Server
 ↓
Tool / Resource
 ↓
Result
 ↓
LLM
 ↓
Response
 ↓
User

MCP and RAG Relationship

Many people ask:

Is MCP the same as RAG?

No.

RAG

Focuses on:

Retrieve information
+
Generate answer

Example:

Question
 ↓
Vector Search
 ↓
Documents
 ↓
LLM
 ↓
Answer

MCP

Focuses on:

Tools
Resources
Prompts
RAG
Memory
Context

Example:

Question
 ↓
MCP
 ├─ RAG
 ├─ Tools
 ├─ APIs
 ├─ Files
 └─ Databases
 ↓
LLM

RAG is often one capability within an MCP-enabled architecture.

Real-World Example

Imagine an HR Copilot.

User asks:

How many leave days do I have?

MCP workflow:

LLM identifies need for employee data
Calls HR API via MCP
Retrieves leave balance
Generates response

Output:

You currently have 12 leave days remaining.

The answer comes from the HR system, not from the model's training data.

Benefits of MCP

✅ Standardization
One protocol for multiple systems.
✅ Tool Integration
Easy access to APIs and applications.
✅ Security
Controlled access to enterprise data.
✅ Scalability
Add new tools without rewriting the AI application.
✅ Interoperability
Different AI models can use the same MCP server.

Summary

MCP (Model Context Protocol) is a standard protocol that enables AI models to securely access and interact with external tools, resources, databases, APIs, and enterprise systems.

Core Components

Client – initiates MCP requests
Host – AI application running the LLM
MCP Server – exposes capabilities
Resources – data sources
Tools – executable actions
Prompts – reusable workflows

Workflow

User Request
↓
LLM Understands Intent
↓
Discover MCP Tools/Resources
↓
Call Tool or Resource
↓
MCP Server Executes Request
↓
Return Results
↓
LLM Generates Final Answer
↓
User Receives Response

In one sentence:

MCP provides a standardized bridge between AI models and the external world, allowing them to retrieve information, use tools, and perform real business tasks.