Interactive Feedback
Enables human-in-the-loop workflows in AI-assisted development by facilitating real-time clarification between AI agents and users.
About
This Model Context Protocol (MCP) server addresses the inefficiencies of AI models making assumptions or generating incorrect output due to vague instructions, which often leads to wasted premium API calls. It introduces a clever workaround by allowing the AI model to pause and request clarification directly from the user through an interactive feedback window. By leveraging tool calls, which typically do not count as separate premium interactions, this mechanism enables multiple feedback cycles within a single request. This empowers AI assistants to ask for clarification instead of guessing, resulting in more accurate responses, reduced API usage, faster development cycles, and improved collaborative dialogue between the user and the AI.
Key Features
- Improves accuracy and reduces errors in AI-generated output
- Reduced premium API call consumption for AI interactions
- Facilitates interactive feedback loops within a single AI request
- 0 GitHub stars
- Enables AI to request clarification before finalizing tasks
- Supports presenting predefined options for user choices
Use Cases
- Guiding AI assistants when initial requirements or instructions are unclear
- Obtaining user confirmation or preferences before AI task completion
- Iteratively refining AI-generated code or content without wasting API credits
