A demonstration server that showcases how to collect user input dynamically using the Model Context Protocol (MCP) elicitation system across tools, resources, and prompts.
Enables creating and managing user profiles with interactive elicitation capabilities that prompt users for missing required information. Demonstrates MCP's elicitation feature by validating profile data and requesting additional details when fields are incomplete.
Provides an interactive user interface for asking questions with support for text inputs, single/multi-select options, and confirmation dialogs. It allows LLMs to gather structured input or feedback from users through a set of predefined input types.
Enables AI agents to prompt users for input directly within the chat UI using MCP's elicitation capabilities, allowing for interactive multi-turn conversations without leaving the conversation interface.
A hands-on demonstration project that teaches the Model Context Protocol (MCP) through Python code, allowing users to understand how AI models interact with their context through a provider-agent architecture.