mcp-test2

This capability allows users to define functions in a schema format that can be called by various models. It uses a registry pattern to manage function definitions and dynamically routes calls to the appropriate model provider based on the schema. This design enables seamless integration with multiple AI models, enhancing flexibility and reducing the need for custom code.

This capability orchestrates interactions between multiple AI models based on the context of the conversation or task. It employs a context management system that tracks user inputs and model outputs, ensuring that the most relevant model is invoked at each step. This approach enhances the coherence and relevance of responses across different models.

This capability enables the dynamic integration of various APIs into the MCP framework, allowing users to extend functionality without modifying the core system. It employs a plugin architecture that allows developers to create and register new API integrations easily, fostering a modular approach to system expansion.

This capability processes incoming data streams in real-time, enabling immediate responses based on user interactions or external events. It utilizes event-driven architecture to handle data asynchronously, ensuring that the system remains responsive and can scale effectively with demand.