mcp-server-test

This capability enables seamless integration with multiple AI models by implementing the Model Context Protocol (MCP) for standardized communication. It utilizes a modular architecture that allows developers to easily add or swap models while maintaining consistent input and output formats, facilitating flexible experimentation and deployment. The server manages context and state, ensuring that interactions with different models are coherent and contextually aware.

This capability processes incoming requests by maintaining a context state that is updated with each interaction. It employs a context management system that tracks user interactions and model responses, allowing for more relevant and personalized outputs. The server can handle multiple concurrent sessions, ensuring that context is preserved for each user independently.

This capability allows the server to dynamically select which AI model to invoke based on the context of the request. It analyzes the input data and previous interactions to determine the most suitable model, optimizing response relevance and accuracy. The implementation leverages decision trees and heuristics to evaluate context and make real-time selections.

This capability aggregates responses from multiple AI models to provide a comprehensive answer to user queries. It collects outputs from different models and employs a ranking system to determine the most relevant response based on predefined criteria. The server can return a single best response or a list of ranked options, enhancing user experience through diverse perspectives.

This capability manages state across user sessions, allowing the server to retain information about previous interactions and user preferences. It employs a session store that can persist data across requests, enabling a more coherent user experience. The architecture supports both in-memory and persistent storage options, catering to different application needs.