User Feedback MCP Server

The User Feedback MCP is a lightweight server that bridges AI assistants and human users, enabling a seamless “human‑in‑the‑loop” workflow. In many desktop or web applications, an AI may generate code, design elements, or workflows that still require a developer’s judgment before final deployment. This MCP allows the AI to pause, present its output to a real user, and collect explicit feedback before marking a task as complete. By embedding this step directly into the AI’s execution flow, developers can iterate faster and reduce the risk of shipping incomplete or incorrect features.

At its core, the server exposes a single tool called . When invoked, it launches an intuitive UI where the user can review a summary or preview of the AI’s work and provide comments, approvals, or rejection. The tool then returns structured feedback that the AI can use to refine its next attempt or to trigger downstream processes. This tight coupling eliminates manual hand‑offs, streamlines testing cycles, and ensures that every change passes a human sanity check before integration into production code.

Key capabilities include:

• Configurable execution – A file lets developers specify a command (e.g., ) that can be automatically executed once feedback is received, eliminating manual clicks during testing.
• Auto‑approval hooks – MCP servers can declare which tools are auto‑approved, allowing the server to bypass confirmation prompts for trusted operations such as .
• Cross‑platform integration – Designed to work out of the box with popular AI assistants like Cline and Cursor, the MCP can be added via a simple JSON configuration without modifying the assistant’s core code.
• Extensible tooling – The server follows standard MCP conventions, so additional tools or prompts can be added later without breaking existing workflows.

Typical use cases span from rapid prototyping of desktop applications to continuous integration pipelines that require human validation before merging. For instance, a developer building an Electron app can let the AI generate UI components, then present them to a stakeholder through the feedback UI. The stakeholder’s input can immediately trigger a rebuild or prompt the AI to adjust styling, all within the same session.

By integrating human judgment directly into the AI’s task loop, the User Feedback MCP reduces friction in iterative development, enhances code quality, and ensures that AI‑generated outputs meet real‑world expectations before they reach end users.