In Part 4 of this series, we added tool calling to our AI agent, allowing it to access real-time information like weather forecasts and current dates. However, we discovered another limitation: when asked to book flights or hotels, the agent couldn’t access those systems because we’d need to hardcode every API integration into our application.
Hardcoding API integrations creates maintenance challenges. Every new service requires code changes, recompilation, and redeployment. As your organization adds more systems (booking platforms, inventory systems, CRM tools), your AI agent becomes increasingly difficult to maintain and extend.
In this post, we’ll add Model Context Protocol (MCP) to our AI agent, allowing it to dynamically discover and use tools from external services without code changes or redeployment. You’ll learn how MCP enables you to expose your legacy systems, enterprise applications, or microservices to AI agents without rewriting them.
