Interact MCP

🚀 Prompt Engineering

Prompt Engineering is a technique that fine-tunes the prompts given to AI assistants to optimize their output. This README provides guidance on integrating the interactive_feedback MCP (Multi-Command Processor) into your AI assistant to enhance user interaction and reduce resource consumption.

🚀 Quick Start

To achieve the best results, add the following rules to your custom prompt in your AI assistant. You can either incorporate them as rules or directly include them in the prompt (e.g., for Cursor):

Whenever you want to ask a question, always call the MCP interactive_feedback.
Whenever you’re about to complete a user request, call the MCP interactive_feedback instead of simply ending the process. Keep calling MCP until the user’s feedback is empty, then end the request.

This ensures that your AI assistant uses the MCP server to request user feedback before marking the task as completed.

✨ Features

💡 Why Use This?

By guiding the assistant to check in with the user instead of making speculative, high-cost tool calls, this module can significantly reduce the number of premium requests (e.g., OpenAI tool invocations) on platforms like Cursor. In some cases, it can consolidate up to 25 tool calls into a single, feedback-aware request, saving resources and improving performance.

📦 Installation (Cursor)

1. Prerequisites

• Python 3.11 or newer.
• uv (Python package manager). Install it using the following commands:
  • Windows: pip install uv
  • Linux/Mac: curl -LsSf https://astral.sh/uv/install.sh | sh

2. Get the code

• Clone this repository: git clone https://github.com/noopstudios/interactive-feedback-mcp.git
• Or download the source code.

3. Navigate to the directory

cd path/to/interactive-feedback-mcp

4. Install dependencies

uv sync (This creates a virtual environment and installs packages)

5. Run the MCP Server

uv run server.py

6. Configure in Cursor

• Cursor usually allows you to specify custom MCP servers in its settings. You need to point Cursor to the running server. The exact configuration method may vary, so refer to Cursor's documentation for adding custom MCPs.

• Manual Configuration (e.g., via mcp.json) Remember to change the /Users/fabioferreira/Dev/scripts/interactive-feedback-mcp path to the actual path where you cloned the repository on your system.

  {
    "mcpServers": {
      "interactive-feedback-mcp": {
        "command": "uv",
        "args": [
          "--directory",
          "/Users/fabioferreira/Dev/scripts/interactive-feedback-mcp",
          "run",
          "server.py"
        ],
        "timeout": 600,
        "autoApprove": [
          "interactive_feedback"
        ]
      }
    }
  }
  

• You can use a server identifier like interactive-feedback-mcp when configuring it in Cursor.

For Cline / Windsurf

Similar setup principles apply. You need to configure the server command (e.g., uv run server.py with the correct --directory argument pointing to the project directory) in the respective tool's MCP settings, using interactive_feedback-mcp as the server identifier.

📚 Documentation

Configuration

This MCP server uses Qt's QSettings to store configuration on a per-project basis. The configuration includes:

• The command to run.
• Whether to execute the command automatically on the next startup for that project (see "Execute automatically on next run" checkbox).
• The visibility state (shown/hidden) of the command section (this is saved immediately when toggled).
• Window geometry and state (general UI preferences).

These settings are typically stored in platform-specific locations (e.g., registry on Windows, plist files on macOS, configuration files in ~/.config or ~/.local/share on Linux) under an organization name "FabioFerreira" and application name "InteractiveFeedbackMCP", with a unique group for each project directory.

The "Save Configuration" button in the UI primarily saves the current command typed into the command input field and the state of the "Execute automatically on next run" checkbox for the active project. The visibility of the command section is saved automatically when you toggle it. General window size and position are saved when the application closes.

Development

To run the server in development mode with a web interface for testing:

uv run fastmcp dev server.py

This will open a web interface and allow you to interact with the MCP tools for testing.

💻 Usage Examples

Basic Usage

Here's an example of how the AI assistant would call the interactive_feedback tool:

<use_mcp_tool>
  <server_name>interactive-feedback-mcp</server_name>
  <tool_name>interactive_feedback</tool_name>
  <arguments>
    {
      "project_directory": "/path/to/your/project",
      "summary": "I've implemented the changes you requested and refactored the main module."
    }
  </arguments>
</use_mcp_tool>