MCP? It's APIs All the Way Down

The Model Context Protocol (MCP) is rapidly transforming integration, automation, and AI-driven systems, but its early adoption phase prioritizes speed over quality, performance, and security. MCP serves as an abstraction layer between large language models and APIs, facilitating seamless integration by allowing AI agents to interact with real-world data without needing to handle the intricacies of API calls directly. However, as MCP adoption grows, so do the challenges associated with API dependencies, such as integration complexity, testing difficulties, and the potential for increased API consumption. These challenges mirror those faced in integration-heavy software environments, highlighting the need for robust API simulation tools to maintain development velocity and quality. Companies like WireMock advocate for scalable API simulation to decouple development from API dependencies, supporting complex workflows and ensuring that AI agents can be effectively developed and tested. As MCP use increases, investment in realistic and scalable mocking infrastructure will be crucial for success in AI-native architectures.