Model Context Protocol (MCP)

Understanding Anthropic's open standard for connecting AI assistants to data sources and enabling secure, controlled access to external systems.

Explain Like I'm 5

Model Context Protocol is like having a universal language that all AI agents can understand! Imagine if every toy from different companies could talk to each other and share information. MCP helps different AI agents share their memories and knowledge, just like how you can play with LEGO blocks and Duplo blocks together because they fit! It makes all the AI agents work better as a team by giving them a common way to talk and share information safely.

What is Model Context Protocol?

Model Context Protocol (MCP) is an open standard created by Anthropic that enables AI assistants to securely connect to data sources and tools. It provides a standardized way for AI models to access external information while maintaining security and user control.

Key Features:

Standardized protocol for AI-to-system communication
Secure, permission-based access to external data
Interoperability between different AI systems
Real-time data access and tool integration

How MCP Works

The architecture and components of Model Context Protocol

1. MCP Client

The AI assistant (like Claude) that wants to access external data or tools.

2. MCP Server

The service that provides access to specific data sources or tools (databases, APIs, files).

3. Transport Layer

The communication channel (HTTP, WebSocket, etc.) that connects clients and servers.

MCP Communication Flow:

1AI assistant discovers available MCP servers and their capabilities

2User grants permission for specific data access or tool usage

3AI assistant makes structured requests through MCP protocol

4MCP server processes request and returns structured data

5AI assistant incorporates data into response or action

MCP Capabilities

What AI assistants can do through MCP connections

Data Access

• Read files and documents
• Query databases
• Access cloud storage
• Retrieve API data

Tool Integration

• Execute code and scripts
• Control applications
• Manage workflows
• Automate tasks

Real-time Updates

• Live data streaming
• Event notifications
• Status monitoring
• Dynamic responses

Security Controls

• Permission management
• Access logging
• Data encryption
• Audit trails

MCP Implementation Example

Basic example of setting up an MCP server

// Basic MCP Server Example
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';

const server = new Server(
  {
    name: 'file-server',
    version: '0.1.0',
  },
  {
    capabilities: {
      resources: {},
      tools: {},
    },
  }
);

// Define available resources
server.setRequestHandler('resources/list', async () => {
  return {
    resources: [
      {
        uri: 'file://documents/',
        name: 'Documents',
        description: 'Access to document files',
        mimeType: 'application/json',
      },
    ],
  };
});

// Handle resource requests
server.setRequestHandler('resources/read', async (request) => {
  const { uri } = request.params;
  
  if (uri.startsWith('file://documents/')) {
    const filePath = uri.replace('file://documents/', '');
    const content = await readFile(filePath);
    
    return {
      contents: [
        {
          uri,
          mimeType: 'text/plain',
          text: content,
        },
      ],
    };
  }
  
  throw new Error('Resource not found');
});

// Start the server
const transport = new StdioServerTransport();
await server.connect(transport);

Note: This example shows a basic file server that allows AI assistants to read documents through the MCP protocol with proper security and permission controls.

Benefits of MCP