Interact Mcp

What is Interact Mcp

interact-MCP is a module designed to enhance AI assistants by ensuring they solicit user feedback before completing tasks. It aims to reduce unnecessary tool calls and improve resource efficiency.

Use cases

Use cases include enhancing user engagement in AI assistants, optimizing resource usage in tool calls, and improving the overall performance of interactive applications.

How to use

To use interact-MCP, integrate it into your AI assistant’s prompt by including the command ‘interactive_feedback’ to request user feedback during interactions. Ensure to call this command until the user provides no further feedback.

Key features

Key features include reducing the number of tool calls by consolidating user feedback requests, storing project-specific configurations using Qt’s QSettings, and allowing automatic command execution on startup.

Where to use

interact-MCP can be used in AI-driven applications, customer service bots, and any interactive systems where user feedback is crucial for task completion.

Prompt Engineering

For the best results, add the following to your custom prompt in your AI assistant, you should add it on a rule or directly in the prompt (e.g., 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 will ensure your AI assistant uses this MCP server to request user feedback before marking the task as completed.

💡 Why Use This?

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

Configuration

This MCP server uses Qt’s QSettings to store configuration on a per-project basis. This 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.

Installation (Cursor)

1. Prerequisites:
   • Python 3.11 or newer.
   • uv (Python package manager). Install it with:
     • 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 typically allows specifying custom MCP servers in its settings. You’ll need to point Cursor to this running server. The exact mechanism might vary, so consult 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 might use a server identifier like interactive-feedback-mcp when configuring it in Cursor.

For Cline / Windsurf

Similar setup principles apply. You would 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.

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.

Available tools

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>