Model Context Protocol (MCP): Building Interconnected AI Agent Systems

The Model Context Protocol (MCP) has emerged as a game-changing standard for connecting AI models to external tools, data sources, and services. Originally introduced by Anthropic, MCP is quickly becoming the universal language for AI agent integration.

AI Network Visualization Photo by JJ Ying on Unsplash

What is MCP?

MCP is an open protocol that standardizes how AI applications connect to external data sources and tools. Think of it as USB for AI—a universal connector that eliminates the need for custom integrations.

Key Benefits

Standardization: One protocol for all tool integrations
Security: Built-in permission and scope management
Interoperability: Works across different AI models and platforms
Simplicity: Clean JSON-RPC based communication

Architecture Overview

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   AI Model      │────▶│   MCP Client    │────▶│   MCP Server    │
│  (Claude/GPT)   │◀────│   (Host App)    │◀────│   (Your Tools)  │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Building an MCP Server

Let’s create a simple MCP server that provides weather data:

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server(
  {
    name: "weather-server",
    version: "1.0.0",
  },
  {
    capabilities: {
      tools: {},
    },
  }
);

// Define the weather tool
server.setRequestHandler("tools/list", async () => ({
  tools: [
    {
      name: "get_weather",
      description: "Get current weather for a location",
      inputSchema: {
        type: "object",
        properties: {
          location: { type: "string", description: "City name" },
        },
        required: ["location"],
      },
    },
  ],
}));

// Handle tool execution
server.setRequestHandler("tools/call", async (request) => {
  if (request.params.name === "get_weather") {
    const location = request.params.arguments.location;
    // Fetch real weather data here
    return {
      content: [
        {
          type: "text",
          text: `Weather in ${location}: 72°F, Sunny`,
        },
      ],
    };
  }
});

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

Server Code Photo by Mohammad Rahmani on Unsplash

MCP Resources: Exposing Data

Beyond tools, MCP supports resources—structured data that AI can access:

server.setRequestHandler("resources/list", async () => ({
  resources: [
    {
      uri: "weather://forecast/weekly",
      name: "Weekly Forecast",
      mimeType: "application/json",
    },
  ],
}));

server.setRequestHandler("resources/read", async (request) => {
  if (request.params.uri === "weather://forecast/weekly") {
    return {
      contents: [
        {
          uri: request.params.uri,
          mimeType: "application/json",
          text: JSON.stringify({
            forecast: [
              { day: "Monday", high: 75, low: 58 },
              { day: "Tuesday", high: 72, low: 55 },
              // ... more days
            ],
          }),
        },
      ],
    };
  }
});

Prompts: Pre-defined Interactions

MCP also supports prompts—templates that guide AI behavior:

server.setRequestHandler("prompts/list", async () => ({
  prompts: [
    {
      name: "weather_report",
      description: "Generate a weather report for a location",
      arguments: [
        {
          name: "location",
          description: "The city to report on",
          required: true,
        },
      ],
    },
  ],
}));

Real-World Use Cases

1. Database Integration

// MCP server connecting to PostgreSQL
const dbServer = new MCPServer({
  tools: [
    {
      name: "query_database",
      handler: async ({ query }) => {
        const result = await pool.query(query);
        return { rows: result.rows };
      },
    },
  ],
});

2. API Gateway

Connect your existing REST APIs through MCP:

const apiServer = new MCPServer({
  tools: [
    {
      name: "create_ticket",
      handler: async ({ title, description }) => {
        const response = await fetch("https://api.jira.com/tickets", {
          method: "POST",
          body: JSON.stringify({ title, description }),
        });
        return response.json();
      },
    },
  ],
});

3. File System Access

const fsServer = new MCPServer({
  resources: [
    {
      uri: "file:///project/README.md",
      handler: async () => fs.readFile("./README.md", "utf-8"),
    },
  ],
});

Security Best Practices

Scope Limitations: Define exactly what each MCP server can access
Authentication: Use secure transport with proper auth
Input Validation: Always validate tool inputs
Audit Logging: Log all tool invocations
Rate Limiting: Prevent abuse through throttling

const secureServer = new MCPServer({
  auth: {
    required: true,
    validate: async (token) => verifyJWT(token),
  },
  rateLimit: {
    maxRequests: 100,
    windowMs: 60000,
  },
});

Ecosystem Growth

The MCP ecosystem is expanding rapidly:

Official SDKs: TypeScript, Python, Rust
Community Servers: GitHub, Slack, Notion, databases
IDE Integration: VS Code, JetBrains, Cursor
Framework Support: LangChain, LlamaIndex, AutoGen

Getting Started

Install the SDK:
```
npm install @modelcontextprotocol/sdk
```
Configure your AI host to connect to MCP servers
Build your server with tools, resources, and prompts
Test and deploy your integration

Conclusion

MCP represents a fundamental shift in how we build AI applications. Instead of brittle, custom integrations, we now have a standardized protocol that makes AI truly extensible. As the ecosystem matures, expect MCP to become as ubiquitous as REST APIs are today.

The future of AI isn’t isolated models—it’s interconnected agents working together through protocols like MCP.

Found this useful? Share with your team and start building your own MCP integrations!

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