Making sense of MCP: Why standardization matters in the AI supply chain

Standardization in the AI supply chain is crucial for ensuring secure and scalable enterprise adoption, with protocols like the Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) laying the groundwork for interoperability. Despite its potential, MCP's initial focus on user-to-LLM interactions and lack of security features in its current usage make it unsuitable for enterprise environments without proper management. However, employing remote MCP setups, controlled by organizations, can provide a safe and structured approach to integrating AI tools. The emergence of these protocols signifies the beginning of a standardized AI interoperability stack, which is essential for fostering innovation and building enterprise-grade systems. Companies like Tyk are actively working to develop tools that facilitate this secure and structured interoperability, aiming to shape a reliable foundation for future AI systems.