interact-mcp
An interactive MCP (Model Context Protocol) server that enables real-time communication between AI assistants and users through a web-based chat interface.
Documentation
Interact MCP
An interactive MCP (Model Context Protocol) server that enables real-time communication between AI assistants and users through a web-based chat interface.
Overview
This project provides an MCP server with an `interact` tool that allows AI assistants to:
- Ask questions and receive responses from users
- Request confirmation for actions
- Seek guidance or clarification
- Share information in real-time
The server features a Gradio-based web interface that provides a chat UI for seamless user interaction.
Features
- Real-time Interaction: Bidirectional communication between AI and users
- Web-based Chat Interface: Clean, modern UI powered by Gradio
- Multiple Transport Options: Supports both stdio and SSE (Server-Sent Events) transport
- Timeout Handling: Automatic timeout protection for user responses
- Chat History: Maintains conversation history during sessions
Installation
1. Clone this repository:
git clone
cd interact-mcp2. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate3. Install dependencies:
pip install fastmcp gradioUsage
Running the Server
Option 1: SSE Transport (Default)
python server.pyThis will start the server on `127.0.0.1:3033` and automatically open the chat interface in your browser.
Option 2: Custom Port
python server.py sse 8080Specify a custom port for the SSE transport.
Option 3: Stdio Transport
python server.py --stdioUse stdio transport for direct MCP communication.
Using the Chat Interface
1. The web interface will open automatically in your browser
2. Wait for AI assistant messages to appear
3. Respond to questions or prompts in the text input field
4. Your responses will be sent back to the AI assistant through the MCP protocol
Adding to Cursor IDE
To use this MCP server with Cursor IDE, you'll need to configure it in the MCP configuration file.
1. Open Cursor IDE and go to Cursor Settings > Tools & Integrations and click "New MCP Server"
2. This will open the `~/.cursor/mcp.json` file for editing
3. Add the following configuration:
{
"mcpServers": {
"interact": {
"command": "path/to/interact-mcp/venv/bin/python3",
"args": ["path/to/interact-mcp/server.py", "--stdio"]
}
}
}4. Replace `path/to/interact-mcp` with the actual path to your project directory
5. The Gradio UI should open automatically in your browser
MCP Tool
`interact(message: str) -> str`
The main tool provided by this server allows AI assistants to interact with users.
Parameters:
- `message` (str): The message to send to the user
Returns:
- `str`: The user's response to the message
Timeout:
- Default timeout is 5 minutes (300 seconds)
- Returns timeout message if no response is received
Building Executable
A PyInstaller spec file is included for creating standalone executables:
pyinstaller server.specThis will create a `dist/server` executable that includes all necessary dependencies.
Technical Details
- Built with FastMCP framework
- Uses Gradio for the web interface
- Implements threading for concurrent web UI and MCP server operation
- Supports real-time updates with periodic refresh mechanism
Contributing
Contributions are welcome! Please feel free to submit issues or pull requests.
Frequently asked questions
What is interact-mcp?
interact-mcp is An interactive MCP (Model Context Protocol) server that enables real-time communication between AI assistants and users through a web-based chat interface.
How do I install interact-mcp?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is interact-mcp open source?
Yes — it is hosted on GitHub at https://github.com/Marqasa/interact-mcp.
Related MCP tools
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
Automate browser based workflows with AI
Hindsight: Agent Memory That Learns
A privacy-first app that strips AI watermarks from content you own.
Agent framework and applications built upon Qwen>=3.0, featuring Function Calling, MCP, Code Interpreter, RAG, Chrome extension, etc.
The power of Claude Code / GeminiCLI / CodexCLI + [Gemini / OpenAI / OpenRouter / Azure / Grok / Ollama / Custom Model / All Of The Above] working as one.
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP