chapter seven

7 Tool Integration and MCP

 

This chapter covers

  • Model Context Protocol (MCP) and how it solves the N×M integration challenge in AI systems
  • Building your first MCP tool with a product catalog
  • Teaching AI models to discover and use MCP tools automatically

A customer texts: "Do you have waterproof hiking boots under $150?"

Your AI assistant needs to search your product database, check availability, and respond—all in seconds. But connecting AI to external systems has always been a nightmare of custom integrations.

Every new API means more glue code. Every new app means duplicating that code. Before long, you're drowning in 500-line Stripe integrations and 300-line Slack webhooks—all failing differently.

This chapter introduces the Model Context Protocol (MCP)—a standard that transforms how AI systems connect to the real world. Instead of writing custom integration code for every API, MCP gives you plug-and-play connectivity. One protocol, endless possibilities.

In practice, real-world AI needs to interact with databases, payment gateways, inventory systems, and external APIs. MCP makes these connections simple, secure, and standardized.

To ground these concepts in reality, you'll build a practical product search tool—the foundation of any ecommerce AI assistant. You'll create an MCP server, expose it to language models, and see how AI can automatically discover and use your tools.

7.1 What is MCP?

7.1.1 The hidden complexity: The N×M problem

7.1.2 The solution: Model Context Protocol (MCP)

7.2 Building your first real MCP tool with a CSV-powered product catalog

7.2.1 Loading your product catalog

7.2.2 Setting up the MCP server

7.2.3 Running and testing your server

7.3 Teaching your AI to use MCP tools

7.3.1 How models learn what tools they can use

7.3.2 A complete example: model → MCP → answer

7.3.3 What you didn’t have to write

7.3.4 Tool design becomes interface design

7.4 Adding a second tool: Inventory status

7.4.1 How models chain tools

7.4.2 Tool descriptions guide model behavior

7.5 Handling failures gracefully

7.5.1 Catching and communicating errors

7.5.2 Why structured responses matter

7.5.3 Handling empty results vs. errors

7.5.4 Timeout and rate limiting

7.6 Summary

7.7 References