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This capability enables the orchestration of context across multiple model providers using the Model Context Protocol (MCP). It employs a flexible architecture that allows for dynamic integration of various AI models, facilitating seamless context sharing and management. The design leverages a registry pattern to maintain and switch between different model contexts efficiently, ensuring that user interactions are coherent and contextually relevant.

This capability allows for the retrieval of relevant data based on the current context maintained by the MCP server. It uses a combination of semantic search algorithms and context-aware indexing to ensure that the most pertinent information is fetched quickly and accurately. The architecture supports efficient querying against a variety of data sources, making it adaptable to different application needs.

This capability allows the system to update the context in real-time as user interactions occur. It employs event-driven architecture to listen for changes in user input or system state, automatically adjusting the context accordingly. This ensures that the AI models are always operating with the most current information, enhancing the overall user experience.