The user-feedback-mcp is an MCP Server designed to integrate human-in-the-loop workflows into AI tools like Cline and Cursor, particularly beneficial for desktop application development requiring complex user interactions. It allows AI models to request and incorporate user feedback before completing tasks, enhancing the quality and relevance of the AI's output. By adding a simple prompt to your AI model, you can trigger the user_feedback tool, prompting the user for input. Configuration is managed through a .user-feedback.json file, allowing customization of command execution. This server streamlines the feedback loop, enabling developers to build more responsive and user-centric AI applications. Installation instructions are provided for Cline, detailing the steps to configure the MCP server and integrate it into your workflow. This MCP server fosters a collaborative environment between AI and human users, leading to improved AI model performance and user satisfaction.

Human-in-the-Loop Integration

The user-feedback-mcp server facilitates a human-in-the-loop (HITL) workflow, allowing AI models to request and incorporate user feedback during task execution. This is achieved by pausing the AI's process and prompting a user for input before proceeding. The server acts as an intermediary, relaying the AI's request to the user and then feeding the user's response back to the AI. This is particularly valuable in scenarios where AI models require nuanced understanding or validation that is difficult to achieve through automated means alone. For example, in a code generation task, the AI could use user-feedback-mcp to ask the user if the generated code meets the specified requirements before finalizing the task. This ensures higher quality output and reduces the need for extensive post-generation debugging. The server is configured via a .user-feedback.json file, allowing customization of the user interaction process.

Dynamic Feedback Requests

This MCP server enables AI models to dynamically request specific feedback from users based on the context of the task. Instead of relying on pre-defined feedback loops, the AI can formulate targeted questions or requests for clarification. This is achieved through the <use_mcp_tool> tag, which allows the AI to specify the user_feedback tool and provide arguments such as a summary or project_directory. For instance, an AI model assisting with UI design could use this feature to ask the user for feedback on the placement of specific elements or the overall aesthetic appeal. The AI can then use this feedback to refine the design in real-time, leading to a more iterative and user-centered development process. This dynamic interaction enhances the AI's ability to adapt to user preferences and deliver more personalized results.

Seamless Cline and Cursor Integration

The user-feedback-mcp server is designed for seamless integration with AI tools like Cline and Cursor, enhancing their capabilities with human input. The provided installation instructions detail how to configure Cline to recognize and utilize the server. By adding the server to Cline's MCP Servers configuration, developers can enable AI models within Cline to request user feedback as part of their workflow. This integration is facilitated by the autoApprove setting, which allows specific tools like user_feedback to be automatically approved, streamlining the interaction process. This tight integration allows developers to leverage human expertise to guide and refine the AI's actions, leading to more accurate and reliable outcomes. For example, a developer using Cline to automate code refactoring can use user-feedback-mcp to validate the changes before they are committed.