Interactive Choice MCP

An MCP Server that enables AI to provide options and launch an interactive interface for user selection when facing choice problems, then return the results. Inspired by mcp-feedback-enhanced, built with FastMCP.

• Showcase:

Similar Projects

• imhuso/cunzhi - A Rust + Tauri desktop application that intercepts AI from ending conversations prematurely. Features memory management for project-specific preferences and includes code search capabilities. More comprehensive but heavier than this project.

• paulp-o/ask-user-questions-mcp - A lightweight TypeScript + Node.js MCP server focused on CLI-based interaction. Designed for multi-agent parallel coding workflows with question queuing and SSH support. More lightweight but only supports terminal interface.

• fhyfhy17/panel-feedback - Panel Feedback brings AI interaction directly into your IDE's sidebar - seamlessly integrated, always accessible, never intrusive.

Differences: This project provides dual interface support (Web + Terminal) with balanced complexity, focusing on interactive selection scenarios.

(I discovered these projects after completing this one. I hope these excellent projects receive more visibility.)

📋 Table of Contents

• ✨ Key Features

• 📦 Installation

• 🤝 Contributing

• 📍 Local Development Environment Setup

• 💖 Acknowledgments

Related MCP server: AskMeMCP

✨ Key Features

🎯 Core Capabilities

• Interactive Choice Interface: AI presents options, users make selections through intuitive interfaces

• Dual Interface Support: Web-based UI and Terminal UI (experimental)

• Selection Modes: Single-select and multi-select modes

• Option Annotations: Users can add annotations to options to provide correct feedback to AI

• Automation Ready: AI can mark recommended options with timeout auto-submit

📦 Installation

Prerequisites

• Python 3.12 or higher

• uv package manager (recommended) or pip

🚀 Quick Start

Add the following configuration:

{
  "mcpServers": {
    "interactive-choice": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Sighthesia/interactive-choice-mcp",
        "interactive-choice-mcp"
      ]
    }
  }
}

• This will automatically clone the project repository and install dependencies.

• For best results, it is recommended to add the following content to your global prompt (still being adjusted, prompts are from cunzhi, currently focusing on optimization for pay-per-use AI assistants, suggestions are welcome):

  - When the requirements are not clear, use `provide_choice` to ask for clarification and provide predefined options.
  - When there are multiple solutions, use `provide_choice` to ask instead of making assumptions on your own.
  - When there is a need to update a solution or strategy, use `provide_choice` to ask instead of making assumptions on your own.
  - Before completing a request, you must call `provide_choice` to request feedback.
  - Without a clear confirmation through the use of `provide_choice` to ask and receive permission to complete the task or end the conversation, it is prohibited to end the dialogue or request on your own initiative.

Environment Variables (Optional)

You can override saved configurations by adding the following environment variables to the env section in your MCP configuration:

Configuration Example

Here is a complete MCP configuration example with environment variables:

{
  "mcpServers": {
    "interactive-choice": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Sighthesia/interactive-choice-mcp",
        "interactive-choice-mcp"
      ],
      "env": {
        "CHOICE_WEB_HOST": "127.0.0.1",
        "CHOICE_WEB_PORT": "8080",
        "CHOICE_LANG": "en",
        "CHOICE_LOG_LEVEL": "DEBUG",
        "CHOICE_LOG_FILE": "~/.mcp-data/interactive-choice.log",
        "CHOICE_MCP_DATA_DIR": "~/.mcp-data/interactive-choice"
      }
    }
  }
}

🤝 Contributing

Contributions are welcome! Whether it's reporting issues, requesting features, or submitting PRs, it's all greatly appreciated!

For AI-driven development, refer to AGENTS.md and openspec.

📍 Local Development Environment Setup

# Clone the repository
git clone https://github.com/Sighthesia/interactive-choice-mcp.git
cd interactive-choice-mcp

# Install dependencies
uv sync

# Verify installation
uv run pytest

• You can configure to use a local development environment to run the MCP Server:

  {
    "mcpServers": {
      "interactive-choice": {
        "command": "uv",
        "args": [
          "--directory",
          "/path/to/interactive-choice-mcp",
          "run",
          "server.py"
        ]
      }
    }
  }

  Tip: Replace /path/to/interactive-choice-mcp with the actual path, such as ~/interactive-choice-mcp.

🧪 Testing

For detailed testing information, please refer to tests/README.md.

The following are common test commands for development and debugging:

Running Interactive Tests

Temporarily run the Web server for interactive testing to verify user-side interaction effects:

1. Open Web interaction interface and test the default single-select mode

uv run pytest tests/integration/test_interaction_web.py::TestWebInteractionManual::test_web_e2e_manual_interaction --interactive -v -s

2. Open terminal interaction interface and test the default single-select mode

uv run pytest tests/integration/test_interaction_terminal.py::TestTerminalInteractionManual::test_terminal_e2e_manual_interaction --interactive -v -s

Running MCP Server Debugging

Run MCP Inspector to verify MCP Server tool input/output effects:

uv run mcp dev server.py

🏗️ Project Architecture

src/
├── core/                    # Core orchestration and business logic
│   ├── models.py           # Data models and schemas
│   ├── orchestrator.py     # Main orchestration logic
│   ├── validation.py       # Input validation
│   └── response.py         # Response generation
├── mcp/                    # MCP tool bindings
│   ├── tools.py           # MCP tool definitions
│   └── response_formatter.py
├── web/                    # Web interface
│   ├── server.py          # FastAPI web server
│   ├── bundler.py         # Asset bundling
│   └── templates.py       # HTML templates
├── terminal/               # Terminal interface
│   ├── ui.py              # Questionary-based UI
│   └── session.py         # Terminal session management
├── store/                  # Data persistence
│   └── interaction_store.py
└── infra/                  # Infrastructure
    ├── logging.py         # Logging configuration
    ├── i18n.py            # Internationalization
    └── storage.py         # File system operations

Future Considerations

• Since various AI IDEs and CLIs tend to silently run AI commands, the terminal mode interaction experience may be limited and requires further consideration for feasibility

💖 Acknowledgments

• Minidoracat - mcp-feedback-enhanced - Project reference and inspiration source. If you like this project, consider supporting them!

📄 License

MIT License.