iflow-mcp_andrei-cb-feedback-term 1.0.0

An interactive feedback system for MCP (Model Context Protocol) that allows agents to request user feedback during task execution.

MCP Feedback Server

An interactive feedback system for MCP (Model Context Protocol) that allows agents to request user feedback during task execution.

Overview

This system consists of:

• feedback_server.py: The main MCP server that runs in your terminal and handles feedback requests
• feedback_client.py: A test client script demonstrating how agents connect to request feedback

Installation

1. Install the required dependencies:

pip install mcp

Usage

Step 1: Start the Feedback Server

Run the server in a terminal:

python feedback_server.py

The server will:

• Start on localhost:9876
• Display a message when ready
• Show agent requests and allow you to provide feedback interactively

Step 2: Configure Your MCP Agent

Add the server to your MCP configuration file (e.g., mcp_config.json):

{
  "mcpServers": {
    "feedback-server": {
      "command": "python",
      "args": [
        "/<path_to_script>/feedback_client.py"
      ]
    }
  }
}

Step 3: Agent Prompt

Use this prompt with your agent:

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.

How It Works

1. Agent calls the tool: When an agent needs feedback, it calls the interactive_feedback tool with:

   • work_summary: Summary of work completed so far
   • question (optional): Specific question for the user

2. Server displays request: The feedback server shows:

   • Timestamp of the request
   • Work summary from the agent
   • Any specific questions

3. User provides feedback: In the terminal running the server:

   • Type feedback and press Enter to send it back to the agent
   • Press Enter with empty input to approve and let the agent continue

4. Agent receives response: The agent gets either:

   • User feedback to act upon
   • Approval to continue (when feedback is empty)

Example Interaction

In the server terminal:

🚀 Feedback Server started on localhost:9876
Waiting for agent connections...

==============================================================
📝 AGENT REQUEST - 2025-01-15 14:30:45
==============================================================

Work Summary:
I have completed the following tasks:
1. Created the user authentication system
2. Set up the database models
3. Implemented the API endpoints

Agent's Question:
Should I proceed with adding the frontend components?

==============================================================
📌 Your Feedback (press Enter with empty input to approve and continue):
> Yes, but make sure to use React with TypeScript
✅ Feedback sent to agent: 'Yes, but make sure to use React with TypeScript'

Features

• ✅ Real-time interactive feedback
• ✅ Socket-based communication (no polling)
• ✅ Clear visual feedback in terminal
• ✅ Support for both general feedback and specific questions
• ✅ Simple approval mechanism (empty input = continue)
• ✅ Error handling and connection management

Troubleshooting

• Connection refused: Make sure the feedback server is running before the agent tries to connect
• Port already in use: The server uses port 9876 by default. Make sure no other process is using this port
• MCP not found: Install the MCP package using pip install mcp

Architecture

The system uses a dual-server architecture:

1. MCP Server: Handles the MCP protocol and tool definitions
2. Socket Server: Manages the interactive feedback loop in the terminal

This design allows for real-time interaction while maintaining compatibility with the MCP protocol.