How to Implement MCP Endpoints for AI-Ready Commerce

Why Your Business Needs MCP Endpoints

Model Context Protocol (MCP) is an open standard that defines how AI agents communicate with external services. Think of it as a standardized API layer specifically designed for AI-to-service interaction. When an AI purchasing agent wants to check your product availability, compare pricing, or request a quote, MCP provides the structured interface for that interaction. Without MCP endpoints, AI agents must resort to web scraping—unreliable, slow, and often blocked by bot protection.

The business case is straightforward. McKinsey projects $3-5 trillion in agentic commerce by 2030. Salesforce reported that 1 in 5 Cyber Week 2025 orders involved AI agents. As AI purchasing agents become the primary discovery and evaluation channel for B2B procurement, having MCP endpoints is the equivalent of having a website in 1999—not optional for long.

What Is the Model Context Protocol?

MCP defines a client-server architecture where AI agents (MCP clients) connect to your business services (MCP servers) through a standardized protocol. The server exposes tools, resources, and prompts that the AI agent can use to interact with your business data.

Core MCP Concepts

How MCP Differs from REST APIs

You might ask: why not just expose a REST API? MCP provides several advantages for AI agent interaction:

• Semantic discovery — MCP tools include natural language descriptions that AI agents use to understand capabilities without documentation. A REST API requires the agent to parse OpenAPI specs; MCP tools are self-describing.
• Contextual interaction — MCP supports multi-turn interactions where the server can maintain context across requests. A product comparison that requires multiple queries can be handled as a coherent session.
• Standardized auth — MCP defines standard authentication patterns for agent-to-server interaction, including delegation chains where an agent acts on behalf of a verified human.
• Prompt templates — MCP servers can expose pre-built interaction patterns that guide AI agents toward productive queries, reducing misunderstanding and improving response quality.

MCP for Commerce: What to Expose

Not everything on your website should be accessible via MCP. Focus on the data and actions that AI purchasing agents need:

Essential MCP Tools for Commerce

Essential MCP Resources

• catalog://categories — Product category hierarchy
• catalog://products/{id} — Individual product data
• catalog://pricing-tiers — Pricing structure and volume discounts
• company://certifications — Compliance certifications, security audits
• company://sla — Service level agreements
• support://integration-guide — Technical integration documentation

Implementation: Building an MCP Server

Architecture Overview

An MCP server sits between AI agents and your existing business systems. It doesn’t replace your website, API, or database—it provides an AI-optimized interface to them:

AI Agent (MCP Client)
    │
    │  MCP Protocol (JSON-RPC over stdio/SSE/HTTP)
    ▼
MCP Server (your implementation)
    │
    ├── Product Database
    ├── Pricing Engine
    ├── Inventory System
    └── CRM / Quote System

Basic MCP Server in TypeScript

The MCP SDK provides the framework for building servers. Here’s a minimal commerce server:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "your-commerce-server",
  version: "1.0.0",
});

// Tool: Search products
server.tool(
  "search_products",
  "Search the product catalog by keywords, category, or specifications",
  {
    query: z.string().describe("Search keywords"),
    category: z.string().optional().describe("Product category filter"),
    max_price: z.number().optional().describe("Maximum price in USD"),
    limit: z.number().default(10).describe("Max results to return"),
  },
  async ({ query, category, max_price, limit }) => {
    // Query your product database
    const products = await searchProducts(query, { category, max_price, limit });
    return {
      content: [{
        type: "text",
        text: JSON.stringify(products, null, 2),
      }],
    };
  }
);

// Tool: Get product details
server.tool(
  "get_product_details",
  "Get complete details for a specific product including specs, pricing, and availability",
  {
    product_id: z.string().describe("The product identifier"),
  },
  async ({ product_id }) => {
    const product = await getProduct(product_id);
    return {
      content: [{
        type: "text",
        text: JSON.stringify(product, null, 2),
      }],
    };
  }
);

// Tool: Request quote
server.tool(
  "request_quote",
  "Submit a quote request for one or more products. Returns a quote ID for tracking.",
  {
    items: z.array(z.object({
      product_id: z.string(),
      quantity: z.number(),
    })).describe("Products and quantities"),
    company_name: z.string().describe("Requesting company"),
    contact_email: z.string().email().describe("Contact email for quote delivery"),
  },
  async ({ items, company_name, contact_email }) => {
    const quote = await createQuoteRequest(items, company_name, contact_email);
    return {
      content: [{
        type: "text",
        text: `Quote request ${quote.id} submitted. Estimated response time: ${quote.eta}`,
      }],
    };
  }
);

// Resource: Product catalog categories
server.resource(
  "catalog://categories",
  "catalog://categories",
  async (uri) => ({
    contents: [{
      uri: uri.href,
      mimeType: "application/json",
      text: JSON.stringify(await getCategories()),
    }],
  })
);

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

MCP Server in Python

from mcp.server import Server
from mcp.types import Tool, TextContent
import json

server = Server("your-commerce-server")

@server.tool()
async def search_products(
    query: str,
    category: str | None = None,
    max_price: float | None = None,
    limit: int = 10
) -> list[TextContent]:
    """Search the product catalog by keywords, category, or specifications."""
    products = await db.search_products(query, category=category, 
                                         max_price=max_price, limit=limit)
    return [TextContent(type="text", text=json.dumps(products, default=str))]

@server.tool()
async def get_product_details(product_id: str) -> list[TextContent]:
    """Get complete details for a specific product."""
    product = await db.get_product(product_id)
    return [TextContent(type="text", text=json.dumps(product, default=str))]

@server.tool()
async def request_quote(
    items: list[dict],
    company_name: str,
    contact_email: str
) -> list[TextContent]:
    """Submit a quote request. Returns a quote ID for tracking."""
    quote = await crm.create_quote(items, company_name, contact_email)
    return [TextContent(
        type="text", 
        text=f"Quote {quote.id} submitted. ETA: {quote.eta}"
    )]

WordPress-Specific MCP Implementation

For WordPress-based businesses, MCP integration can leverage existing WordPress infrastructure:

Approach 1: WordPress REST API + MCP Wrapper

Build an MCP server that wraps your WordPress REST API. This approach requires no WordPress plugin changes—the MCP server acts as a translator between the MCP protocol and WP REST endpoints:

// MCP server that wraps WordPress REST API
server.tool(
  "search_products",
  "Search products in the WordPress catalog",
  { query: z.string(), category: z.string().optional() },
  async ({ query, category }) => {
    // Call WordPress REST API
    const params = new URLSearchParams({ search: query });
    if (category) params.set("product_cat", category);
    
    const response = await fetch(
      `${WP_URL}/wp-json/wc/v3/products?${params}`,
      { headers: { Authorization: `Bearer ${WC_API_KEY}` } }
    );
    const products = await response.json();
    
    // Transform to agent-friendly format
    return {
      content: [{
        type: "text",
        text: JSON.stringify(products.map(p => ({
          id: p.id,
          name: p.name,
          price: p.price,
          description: p.short_description,
          categories: p.categories.map(c => c.name),
          in_stock: p.in_stock,
          url: p.permalink,
        }))),
      }],
    };
  }
);

Approach 2: WordPress MCP Plugin

A WordPress plugin that registers MCP abilities directly within WordPress, exposing content, products, and business data as MCP tools. This is the approach used by QAIL AI’s WordPress integration—the MCP Abilities API registers WordPress capabilities as MCP-accessible tools that AI agents can discover and invoke.

The WordPress MCP architecture uses three components:

1. Abilities API — Core plugin providing wp_register_ability() for registering capabilities
2. MCP Adapter — Bridges the Abilities API to the MCP protocol, handling JSON-RPC communication
3. Capability plugins — Register specific abilities (product search, content access, form handling) as MCP tools

Approach 3: External MCP Server with WP Data

For more complex deployments, run an MCP server as a separate service that reads from your WordPress database or APIs. This provides better performance isolation, custom caching, and the ability to aggregate data from multiple sources (WordPress + ERP + inventory system).

Security Considerations

Authentication and Authorization

MCP endpoints expose business data to AI agents—security is non-negotiable:

• API key authentication — Require API keys for tool access. Issue keys per agent/organization with specific scope limits.
• Rate limiting — Implement per-key rate limits to prevent abuse. AI agents can generate high request volumes—protect your systems.
• Data scoping — Only expose data that should be publicly accessible to purchasing agents. Internal pricing, cost data, and customer lists should never be MCP-accessible.
• Audit logging — Log every MCP tool invocation with agent identity, parameters, and response data. This creates the audit trail needed for dispute resolution and fraud detection.
• Input validation — Validate all MCP tool parameters strictly. AI agents can generate unexpected inputs—sanitize everything.

Know Your Agent Integration

MCP endpoints should integrate with your Know Your Agent verification layer. Before processing an MCP request:

1. Verify the agent’s identity (API key + agent metadata)
2. Check the agent’s authorization scope (what tools can it call?)
3. Validate the principal (who does this agent represent?)
4. Log the interaction for audit purposes

Testing Your MCP Implementation

Using the MCP Inspector

The MCP project provides an inspector tool for testing servers:

# Install the MCP inspector
npx @modelcontextprotocol/inspector

# Connect to your server
npx @modelcontextprotocol/inspector node your-server.js

The inspector lets you browse available tools, invoke them with test parameters, and verify response formats—all without needing an actual AI agent.

Testing with AI Agents

Once your server passes inspector testing, connect it to an actual AI agent:

1. Claude Desktop — Add your MCP server to Claude’s configuration file and test tool discovery and invocation through natural language queries
2. Custom agent — Build a simple test agent using the MCP client SDK that exercises each tool with realistic parameters
3. Load testing — Simulate multiple concurrent agent connections to verify your server handles load correctly

Deployment Patterns

Pattern 1: Embedded (WordPress Plugin)

MCP server runs within WordPress. Simplest deployment, but shares WordPress’s PHP process. Best for low-traffic sites and initial testing.

Pattern 2: Sidecar

MCP server runs as a separate process on the same machine. Accesses WordPress data via REST API or shared database. Better performance isolation.

Pattern 3: Microservice

MCP server runs as an independent service (Docker container, cloud function). Connects to your data sources via APIs. Best for high-traffic, multi-source deployments. Supports horizontal scaling.

Deployment Comparison

Implementation Checklist

1. Identify your commerce data — What product data, pricing, availability, and business information should AI agents access?
2. Choose your architecture — Embedded plugin, sidecar service, or microservice based on your traffic and complexity needs
3. Implement core tools — Start with search_products, get_product_details, and check_availability—the minimum viable MCP commerce server
4. Add authentication — API key management, rate limiting, and audit logging
5. Integrate KYA — Connect to your Know Your Agent verification for agent identity and authorization checking
6. Test with inspector — Validate all tools work correctly with the MCP inspector before exposing to real agents
7. Deploy and monitor — Track tool invocations, error rates, and agent patterns to optimize your MCP interface
8. Iterate on tools — Add comparison, quote, and advanced query tools based on how agents actually use your server

Frequently Asked Questions

Is MCP the only protocol for AI agent commerce?

No, but it’s currently the most widely adopted. The Agent Communication Protocol (ACP) is emerging for multi-agent negotiation scenarios. Traditional REST APIs with OpenAPI specs also work—but MCP’s semantic tool descriptions and contextual interaction model are better suited for AI agent use cases. Plan for MCP as your primary interface with REST API fallback.

Do I need WooCommerce or a specific e-commerce platform?

No. MCP endpoints can wrap any data source—WooCommerce, Shopify, custom databases, ERP systems, or even spreadsheets. The MCP server is the translation layer between your data and AI agents. The implementation examples above use generic database queries that work with any backend.

How do I prevent competitors from using my MCP endpoints?

API key authentication with organization verification. Issue keys to legitimate AI agent platforms and purchasing organizations. Monitor usage patterns for competitive intelligence extraction (systematic pricing pulls, feature matrix scraping) and rate-limit or restrict suspicious patterns.

What about pricing? Should I expose real-time pricing via MCP?

It depends on your pricing model. Published pricing (standard SaaS tiers, listed product prices) should be exposed—AI agents will find it anyway. Custom/negotiated pricing should be handled through the quote request flow, not direct exposure. The request_quote tool gives you control over pricing for enterprise and volume deals.

How does this integrate with QAIL AI?

QAIL AI provides the Know Your Agent verification layer that authenticates and authorizes AI agents before they access your MCP endpoints. It also provides AI bot traffic analytics that show you which agents discover your MCP server, how they use it, and whether traffic patterns suggest legitimate purchasing behavior or spam/scraping. QAIL AI + MCP = the complete agent commerce stack.

Ready to make your business AI-agent-ready? Talk to QAIL AI about implementing MCP endpoints with Know Your Agent verification, or explore the platform to see the intelligence layer for agentic commerce.