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.

🛠️

For Product Managers & Builders

Why MCP Matters for Your Product

Model Context Protocol solves the "integration hell" problem for AI products. Instead of building custom integrations for every data source, MCP provides a standardized way to connect AI agents to your existing tools, databases, and APIs. This means faster development, easier maintenance, and more powerful AI features.

When to Use MCP

✅ Perfect For:

• AI agents that need access to multiple data sources (Slack, GitHub, databases)
• Building AI features that integrate with existing enterprise systems
• Creating development tools where AI needs codebase awareness
• Customer support bots accessing internal knowledge bases and CRMs
• Any AI product where data security and user control are critical

⚠️ Consider Alternatives When:

• You only need simple API calls (direct integration might be simpler)
• Building consumer apps with no enterprise integrations
• Your AI doesn't need external data access
• Latency is critical and you can't afford the MCP overhead

Getting Started

Start by identifying what data sources your AI needs to access. Common starting points:

1. Choose MCP servers: Browse available MCP servers for Slack, GitHub, Google Drive, databases, etc.
2. Configure permissions: Set up what data your AI can access and what actions it can take
3. Test locally: Use Claude Desktop or other MCP clients to verify connections
4. Deploy: Roll out to production with proper security and monitoring

Pro Tip: MCP is rapidly evolving with new servers being released weekly. The ecosystem grew from dozens to hundreds of MCP servers in 2025. Check the official MCP registry for pre-built integrations before building custom ones.

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