Last week I joined Geospatial FM, the podcast hosted by Wilfred Waters, to talk about AI agents and the Azure Maps MCP server I had created and bloged about.

We touched on how Bing Maps is the familiar public-facing mapping service, while Azure Maps is the developer platform for bringing mapping, routing, traffic, and spatial analytics into enterprise and IoT apps. The heart of our conversation was about Model Context Protocol (MCP) and why it matters. MCP lets AI agents use tools and pull fresh data from APIs, so instead of guessing about roads, traffic, or places, an agent can call Azure Maps in real time.

AI is no longer an abstract promise of the future. It’s here, embedded into enterprise workflows, products, and decision-making processes. From Microsoft Copilot to ChatGPT and domain-specific assistants, businesses are adopting AI at an unprecedented pace. But too often, “AI” is used as a catch-all term for a wide range of technologies. To lead the next transformation wave, organizations must move beyond generic AI adoption and toward agentic AI, a more autonomous, goal-driven form of AI that’s ready to take on real work.

In today’s AI era, you’ve likely interacted with Microsoft Copilot, ChatGPT, or Claude.ai, tools powered by advanced Large Language Models (LLMs). These models excel at understanding and generating human-like text based on vast amounts of training data. However, while LLMs are impressive at reasoning and answering general questions, they fall short when it comes to performing real-world tasks or retrieving live, domain-specific information.

This is where the Model Context Protocol (MCP) comes in.