MCP vs Function Calling – How They Actually Work Together

In the ongoing debate between Model Context Protocol (MCP) and function calling, the notion that developers must choose one over the other is misleading, as both serve distinct roles within AI system architectures. Function calling is the initial phase where models express their intents by returning structured requests for specific functions, which are then executed by applications. In contrast, MCP standardizes the execution infrastructure by defining how tools are discovered and managed across applications, transforming tool execution into a client-server model. This separation allows tools to be network-addressable capabilities, providing portability and consistency across different AI clients and providers, akin to the standardization USB-C brought to device connectivity. While function calling is sufficient for small-scale, single-provider setups, MCP offers significant advantages in larger, multi-provider environments by centralizing tool management and reducing integration complexity. However, MCP introduces its own challenges, such as increased architectural complexity and the need for robust governance and security measures, particularly as systems scale and multiple teams interact with shared resources. Ultimately, many production systems benefit from a hybrid approach, utilizing both function calling for immediate execution and MCP for broader infrastructure management.

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