The Model Context Protocol (MCP) is transforming the way AI agents interact with enterprise systems and APIs. While traditional APIs are primarily designed for human developers, MCP creates a bridge that integrates AI models as fully-fledged actors within enterprise landscapes. This allows them to access systems directly and become part of the system architecture. In this presentation, we address the central question of how MCP differs from classical APIs, where parallels exist, and what implications this has for design, integration, and governance. Through a hands-on showcase, we demonstrate a concrete implementation: we show how existing APIs can be made accessible via MCP servers and present a comprehensive MCP blueprint. Additionally, we explore how Azure cloud services can be leveraged to deploy, manage, and secure an MCP server. The talk provides practical insights from the development and operational deployment of MCP servers, including new challenges, solution approaches, and lessons learned.

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The Model Context Protocol (MCP) is transforming the way AI agents interact with enterprise systems and APIs. While traditional APIs are primarily designed for human developers, MCP creates a bridge that integrates AI models as fully-fledged actors within enterprise landscapes. This allows them to access systems directly and become part of the system architecture. In this presentation, we address the central question of how MCP differs from classical APIs, where parallels exist, and what implications this has for design, integration, and governance. Through a hands-on showcase, we demonstrate a concrete implementation: we show how existing APIs can be made accessible via MCP servers and present a comprehensive MCP blueprint. Additionally, we explore how Azure cloud services can be leveraged to deploy, manage, and secure an MCP server. The talk provides practical insights from the development and operational deployment of MCP servers, including new challenges, solution approaches, and lessons learned.

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