AI assistants are powerful, but they need access to real data to be truly useful. Model Context Protocol provides a standard way for AI systems to connect to your services and tools.
Building AI integrations has been messy. Every AI system requires custom code to connect to your services.
Claude wants one interface. ChatGPT plugins use another. Gemini has its own approach. Custom connectors for each AI multiply development work.
Even when you build a connector, it only works with that one AI system. A Claude integration doesn't help ChatGPT users.
MCP solves this. It's a universal adapter. Build once, connect any MCP-compatible AI.
MCP defines a standardized protocol for:
Tool definitions - Describe what operations your service supports. Each tool has a name, description, and input schema.
Request handling - Process AI requests for data or actions. Return structured results the AI can understand.
Resource access - Let AI read data from your services. Files, database records, API responses.
Authentication - Secure connections. Control what the AI can access.
The protocol is open. Anyone can implement it. The goal is interoperability.
The architecture involves three parties:
MCP Host - The AI application the user interacts with. Claude, ChatGPT, or another AI assistant.
MCP Client - The component that connects to your server. It's part of the AI application.
MCP Server - Your service that exposes data and tools. You implement this.
The client and server communicate through MCP. The AI can access your service through this connection.
Tools are the primary way AI interacts with external services. Define what the AI can do.
Tool definition example:
{
"name": "get_pet",
"description": "Get detailed information about a specific pet",
"inputSchema": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "The unique identifier of the pet"
}
},
"required": ["id"]
}
}
This tells the AI: there's a tool called get_pet that takes an ID parameter and returns pet information.
Tool call example:
{
"method": "tools/call",
"params": {
"name": "get_pet",
"arguments": { "id": "12345" }
}
}
The AI decides to call this tool. It sends the request through MCP to your server.
Tool response example:
{
"content": [
{
"type": "text",
"text": "{\"id\": \"12345\", \"name\": \"Buddy\", \"species\": \"dog\", \"status\": \"available\"}"
}
]
}
Your server responds with structured data. The AI parses this and incorporates it into its response.
Resources are data the AI can read. Unlike tools, reading is the primary operation.
Resource definition:
{
"uri": "petstore://pets",
"name": "All Pets",
"description": "Complete list of pets in the store",
"mimeType": "application/json"
}
Resource request:
{
"method": "resources/read",
"params": {
"uri": "petstore://pets"
}
}
Resources are useful for providing context. The AI can read reference data before answering questions.
A user asks an AI assistant:
"What's the status of order #12345?"
Here's what happens:
The user gets their answer. The AI has real-time data. Your system was accessed through MCP.
Build an MCP server in Node.js:
const { Server } = require('@modelcontextprotocol/sdk/server');
const { CallToolRequestSchema, ListToolsRequestSchema } = require('@modelcontextprotocol/sdk/types');
const server = new Server({
name: 'petstore-mcp',
version: '1.0.0'
}, {
capabilities: {
tools: {}
}
});
// List available tools
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [
{
name: 'list_pets',
description: 'List all pets in the store',
inputSchema: {
type: 'object',
properties: {
status: { type: 'string', description: 'Filter by status' }
}
}
},
{
name: 'get_order',
description: 'Get order details',
inputSchema: {
type: 'object',
properties: {
orderId: { type: 'string', description: 'Order ID' }
},
required: ['orderId']
}
}
]
};
});
// Handle tool calls
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
if (name === 'list_pets') {
const response = await fetch(`https://api.petstoreapi.com/v1/pets?status=${args.status}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
const pets = await response.json();
return {
content: [{ type: 'text', text: JSON.stringify(pets) }]
};
}
if (name === 'get_order') {
const response = await fetch(`https://api.petstoreapi.com/v1/orders/${args.orderId}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
const order = await response.json();
return {
content: [{ type: 'text', text: JSON.stringify(order) }]
};
}
throw new Error(`Unknown tool: ${name}`);
});
// Start the server
server.start();
This server exposes two tools. Any MCP-compatible AI can connect and use them.
MCP simplifies AI integration significantly.
Build once, use everywhere - One MCP server works with any MCP client. No per-AI customization.
Standard approach - No more exploring custom APIs for each AI. Follow the protocol.
Rich capabilities - Tools, resources, and prompts provide comprehensive access patterns.
Growing ecosystem - More AI applications add MCP support. Your implementation works with new systems.
MCP is ideal when:
Consider alternatives when:
The Pet Store API supports MCP for AI integrations. Build powerful AI assistants that interact with your pet store.
The documentation at docs.petstoreapi.com shows how to implement an MCP server. Configure tools, handle requests, and enable AI access.
MCP transforms how users interact with your API. Instead of making API calls directly, they ask an AI assistant. The assistant uses MCP to get real data and provide helpful responses.
