MCP Elicitation Enhances AI Tool Interactions

GitHub is advancing AI tool interactions by implementing Model Context Protocol (MCP) elicitation, which improves user experiences by gathering essential information upfront, reducing friction, and enhancing AI-driven application functionality.

The core of MCP elicitation involves AI pausing to request necessary details from users before proceeding with tasks, preventing reliance on default assumptions. This feature is currently supported by GitHub Copilot within Visual Studio Code, though availability may vary across different AI applications.

During a recent session, GitHub's Chris Reddington highlighted challenges in implementing elicitation in an MCP server for a turn-based game. Initially, the server had duplicative tools for different game types, causing confusion and incorrect tool selection by AI agents. The solution involved consolidating tools and ensuring distinct naming conventions.

The refined approach allows users to initiate games with personalized settings rather than default parameters. For example, when a user requests a game of tic-tac-toe, the system identifies missing details like difficulty level or player name, prompting the user for this information to tailor the game setup.

Implementing elicitation within the MCP server involves steps such as checking for required parameters, identifying missing optional arguments, initiating elicitation to gather missing information, presenting schema-driven prompts, and completing the original request once all necessary data is collected.

Reddington's development session underscored the importance of clear tool naming and iterative development. By refining tool names and consolidating functionality, complexity was reduced, and user experience improved. Parsing initial user requests to elicit only missing information was crucial in refining the elicitation process.

As AI-driven tools evolve, MCP elicitation offers a promising avenue for enhancing user interactions. This approach simplifies user experiences and aligns AI operations with user preferences, paving the way for more intuitive and responsive applications.