How to Connect AI Assistants to Your API with MCP

AI assistants are transforming how users interact with applications. The Model Context Protocol (MCP) provides a standard way for AI systems to connect to your API and access data.

What is Model Context Protocol?

MCP is an open protocol that lets AI assistants connect to external tools and data sources. Think of it as a USB-C port for AI. Just as USB-C provides a standard connection for hardware, MCP provides a standard connection for AI to services.

Before MCP, each AI integration required custom code. Developers built unique connectors for every AI and every service. MCP solves this. One implementation connects any MCP-compatible AI to your API.

The protocol defines:

• How AI requests data from your service
• How your service responds with structured data
• How to handle authentication and permissions
• How to expose available capabilities

Why MCP Matters

AI assistants are only as useful as their data access. Without connection to real data, they provide generic responses. MCP enables meaningful AI integrations.

Standard integration:

Without MCP, building an AI integration requires custom development for each AI system. Claude wants one thing, ChatGPT another, and Gemini something else.

With MCP, you build once. Any MCP-compatible AI can connect.

Real-time data access:

AI queries your service directly. No data syncing. No stale copies. The assistant always works with current information.

Controlled access:

Your service defines what the AI can see and do. Permission controls apply. Sensitive data stays protected.

Structured responses:

The AI receives typed, structured data. No parsing HTML or extracting from unstructured text. Clean data, reliable processing.

How MCP Works

The protocol follows a request-response pattern optimized for AI interactions.

1. Server announces capabilities

Your service advertises what it can do:

{
  "name": "Pet Store API",
  "version": "1.0.0",
  "capabilities": {
    "resources": ["pets", "orders", "inventory"],
    "tools": ["get_pet", "create_order", "update_inventory"]
  }
}

2. AI requests information

The AI asks for data using the tool system:

{
  "method": "tools/call",
  "params": {
    "name": "get_pet",
    "arguments": { "id": "123" }
  }
}

3. Server responds

Your service returns structured data:

{
  "content": [
    {
      "type": "text",
      "text": "{\"id\": \"123\", \"name\": \"Buddy\", \"status\": \"available\"}"
    }
  ]
}

Implementing an MCP Server

Create an MCP server for the Pet Store API:

const { Server } = require('@modelcontextprotocol/sdk/server');
const { StdOutIO } = require('@modelcontextprotocol/sdk/io');

// Define your server
const server = new Server({
  name: 'petstore-api',
  version: '1.0.0'
}, {
  capabilities: {
    resources: {},
    tools: {}
  }
});

// Define tools the AI can use
server.setRequestHandler('tools/list', async () => {
  return {
    tools: [
      {
        name: 'get_pet',
        description: 'Get a pet by ID',
        inputSchema: {
          type: 'object',
          properties: {
            id: { type: 'string', description: 'Pet ID' }
          },
          required: ['id']
        }
      },
      {
        name: 'list_pets',
        description: 'List pets with optional filtering',
        inputSchema: {
          type: 'object',
          properties: {
            status: { type: 'string', enum: ['available', 'pending', 'sold'] },
            category: { type: 'string' },
            limit: { type: 'number', default: 10 }
          }
        }
      },
      {
        name: 'create_order',
        description: 'Create a new order',
        inputSchema: {
          type: 'object',
          properties: {
            petId: { type: 'string' },
            quantity: { type: 'number' }
          },
          required: ['petId', 'quantity']
        }
      }
    ]
  };
});

// Handle tool calls
server.setRequestHandler('tools/call', async (request) => {
  const { name, arguments: args } = request.params;

  switch (name) {
    case 'get_pet':
      return await handleGetPet(args.id);
    case 'list_pets':
      return await handleListPets(args);
    case 'create_order':
      return await handleCreateOrder(args);
    default:
      throw new Error(`Unknown tool: ${name}`);
  }
});

async function handleGetPet(id) {
  const response = await fetch(`https://api.petstoreapi.com/v1/pets/${id}`, {
  headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
  const pet = await response.json();

  return {
    content: [
      { type: 'text', text: JSON.stringify(pet) }
    ]
  };
}

// Run the server
const io = new StdOutIO();
server.run({ io });

This server exposes pet store operations to AI assistants. The AI can read pet data, list pets, and create orders.

Connecting an AI Assistant

Once your server runs, connect an AI to it.

Using Claude, for example:

Use the Pet Store API to get information about the pet with ID 123.

Claude recognizes the tool and calls your MCP server. The server fetches data and returns it. Claude incorporates the data into its response.

Use Cases

MCP enables powerful AI integrations.

Customer service:

"Show me all pending orders from today"

The AI calls your MCP server, retrieves the data, and presents it in a helpful format.

Inventory management:

"What's our current inventory of dog food?"

The AI queries your inventory system through MCP and provides a clear answer.

Order processing:

"Create an order for 3 units of the premium cat food"

The AI validates the request, calls your create_order tool, and confirms completion.

Analytics:

"What's our average order value this month?"

The AI aggregates data from your API and provides insights.

Authentication

Secure your MCP server:

server.setRequestHandler('tools/call', async (request) => {
  const authHeader = request.headers?.authorization;

  if (!authHeader) {
    throw new Error('Authentication required');
  }

  const token = authHeader.replace('Bearer ', '');
  const user = await validateToken(token);

  if (!user) {
    throw new Error('Invalid token');
  }

  // Continue with the request
  return handleToolCall(request, user);
});

Control what data each user can access. Apply the same permissions as your regular API.

Pet Store API and MCP

The Pet Store API supports MCP for AI integrations. Build AI-powered interfaces that interact with your pet store data.

The documentation at docs.petstoreapi.com includes MCP server implementation details. Configure authentication, define available tools, and start serving AI requests.

MCP opens new possibilities for AI-powered applications. Let users interact with your API through natural conversation. Build assistants that understand your domain and access real data.

The future of API interaction includes AI. MCP makes that future accessible today.