Most of us think of agent tools as vending machines - you put in the right input, you get the right output. Put in the wrong input, and you either get the wrong output or nothing at all.

This breaks for complex workflows where context is incomplete or intent is ambiguous. The tool either guesses wrong or fails outright. No back-and-forth, no clarification. Good AI tools built for conversation should explain what they need, what they'll do, and provide clear options.

In this session, we’ll look at how MCP elicitation works in practice across different workflows: covering ambiguous input, missing context, risky operations, and auth flows. Attendees will leave with concrete patterns for designing tools that go beyond transactional APIs to collaborative multi-turn interactions.