Most digital assistants today can help users find information, yet they still cannot independently complete tasks such as organizing a trip or finalizing a booking. This gap exists because the majority of these systems are built on generative AI models that can produce answers but lack the technical ability to carry out real-world actions. That limitation is now beginning to shift as the Model Context Protocol, known as MCP, emerges as a foundational tool for enabling task-performing AI.
MCP functions as an intermediary layer that allows large language models to interact with external data sources and operational tools in a standardized way. Anthropic unveiled this protocol in late 2024, describing it as a shared method for linking AI assistants to the platforms where important information is stored, including business systems, content libraries and development environments.
The protocol uses a client-server approach. An AI model or application runs an MCP client. On the opposite side, travel companies or service providers deploy MCP servers that connect to their internal data systems, such as booking engines, rate databases, loyalty programs or customer profiles. The two sides exchange information through MCP’s uniform message format.
Before MCP, organizations had to create individual API integrations for each connection, which requi
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