The Model Context Protocol (MCP) is revolutionizing how AI applications interact with external data sources and tools. Released by Anthropic in November 2024, MCP has rapidly become the standard for building professional agentic AI systems that go beyond simple chatbots.

The Problem MCP Solves

Before MCP:

• Every developer created custom API layers for external integrations
• Each integration required custom function definitions and schemas
• No standardization across different tools and services
• Constant reinvention for common integrations

After MCP:

• Unified protocol layer that standardizes tool and resource definitions
• Consistent API across all integrations
• Reusable servers that can be shared across projects
• Standardized schemas, functions, and documentation

In this module, you'll discover:

• Why MCP was created and the problems it solves
• The fundamental concepts that make MCP work
• How to build production-ready AI systems with MCP
• Real-world enterprise applications and benefits

MCP provides a standardized way for AI applications to:

1. Access Resources: Connect to data sources, files, and APIs
2. Execute Tools: Perform actions and operations
3. Use Prompts: Leverage pre-built prompt templates

<CodeExample title="Python MCP Server (Modern Approach)" language="python" code={`from mcp import FastMCP

Create MCP server instance

mcp = FastMCP( name="production-server", host="localhost", port=8050 )

Define tools using decorators

@mcp.tool def get_customer_data(customer_id: str) -> dict: """Retrieve customer information from database.""" return {"id": customer_id, "name": "John Doe", "tier": "enterprise"}

@mcp.tool def create_support_ticket(title: str, description: str) -> dict: """Create a new support ticket.""" return {"ticket_id": "TICK-001", "status": "created"}

Run server

if name == "main": mcp.run(transport="stdio") # or mcp.run_sse() for HTTP`} highlightLines={[3, 4, 5, 10, 11, 15, 16]} />

Key Components

1. Resources Resources are read-only data sources that AI can access. Examples include:

• Database records
• File contents
• API responses
• System information

2. Tools Tools enable AI to perform actions. Examples include:

• Writing files
• Sending emails
• Executing commands
• Calling APIs

3. Prompts Reusable prompt templates that ensure consistent AI behavior.

MCP follows a client-server architecture:

1. MCP Server: Your application that exposes resources and tools
2. MCP Client: The AI application (like Claude) that connects to your server
3. Transport Layer: How they communicate (stdio, HTTP, WebSocket)

<Diagram id="mcp-architecture" title="MCP Architecture Overview" description="The flow of communication between AI applications and MCP servers" />

Communication Flow

sequenceDiagram
    participant AI as AI Application
    participant Client as MCP Client
    participant Server as MCP Server
    participant System as Your Systems
    
    AI->>Client: Request resource
    Client->>Server: MCP protocol request
    Server->>System: Fetch data
    System->>Server: Return data
    Server->>Client: MCP protocol response
    Client->>AI: Formatted response

Design Principles

MCP is built on several key principles:

1. Simplicity: Easy to implement and understand
2. Security: Controlled access with clear boundaries
3. Flexibility: Works with any programming language
4. Extensibility: Add capabilities as needed