Multi-User MCP Server with Chatbot and Agent

by r16academy

151 downloads

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About

Multi user Full Stack App with Chatbot and Agent in Javascript, FastAPI and PyMongo

Details

Author: r16academy
Downloads: 151
Categories: AI

- User authentication with registration, login, and password recovery.
- Dual AI interfaces: Chatbot for Q&A, Agent for tool usage.
- Real-time WebSocket-based chat messaging.
- Persistent, user-specific conversation history.
- File upload for document analysis during chat.
- Responsive, mobile-friendly HTML/CSS/JS frontend.

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Multi-User MCP Server with Chatbot and Agent

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

From the repository

Clone the repository, install Python dependencies with pip install -r requirements.txt, set environment variables (OpenAI key, MongoDB URI, email credentials) in a .env file, optionally install MCP server dependencies with npm install, then run python main.py. The app becomes available at http://localhost:5000/. Use the /register, /login, /chatbot, and /agent endpoints for respective features.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "multi-user mcp server with chatbot and agent": {
            "mcp-server-beginner-app": {
                "command": "python",
                "args": [
                    "main.py"
                ]
            }
        }
    }
}

McpServers

{
    "mcp-server-beginner-app": {
        "command": "python",
        "args": [
            "main.py"
        ]
    }
}

Multi-User MCP Server with Chatbot and Agent

---

🚀 Overview

A full-stack web application implementing the Model Context Protocol (MCP) to provide two AI assistant experiences: - Chatbot: Conversational Q&A - Agent: Tool-using AI agent

Features include user authentication, persistent chat history, real-time WebSocket messaging, and file upload for document analysis. The app is production-ready and deployable on Heroku.

---

✨ Features

- User Authentication: Registration, login, password recovery, email verification - Dual AI Interfaces: Chatbot and Agent, each with unique capabilities - Real-Time Chat: WebSocket-based instant messaging - Conversation History: Persistent, user-specific chat logs - File Upload: Analyze documents in chat - MCP Server Integration: Connects to multiple MCP servers for tool execution - Responsive UI: Clean, mobile-friendly HTML/CSS/JS frontend - Heroku Deployment: Ready for cloud deployment

---

🛠️ Tech Stack

- Backend: Python 3.10+, FastAPI, MongoDB, Uvicorn - Frontend: HTML, CSS, Vanilla JavaScript (see static/) - AI Integration: OpenAI API or compatible LLM providers - Real-Time: WebSocket - Deployment: Heroku (Procfile included)

---

📁 Project Structure

.
├── main.py                # FastAPI app entry point
├── routes/                # API route handlers (auth, chatbot, agent, feedback)
├── static/                # Frontend HTML, CSS, JS for all pages
│   ├── chatbot/           # Chatbot interface
│   ├── agent/             # Agent interface
│   ├── login/             # Login page
│   ├── register/          # Registration page
│   └── ...                # Other static assets
├── tools/                 # MCP server integration logic
├── email_config.py        # Email settings for verification
├── config.py              # General configuration
├── servers_config.json    # MCP server connection details
├── requirements.txt       # Python dependencies
├── Procfile               # Heroku process file
└── README.md              # This file

---

⚡ Getting Started

Prerequisites

- Python 3.10+ - MongoDB (local or Atlas) - Node.js (for MCP server dependencies) - Heroku CLI (for deployment)

Installation

1. Clone the repository:

   git clone <your-repo-url>
   cd <repo-folder>

2. Install Python dependencies:

   pip install -r requirements.txt

3. Set up environment variables: - Copy .env.example to .env and fill in your secrets (OpenAI key, MongoDB URI, email credentials). 4. Install MCP server dependencies (if needed):

   npm install

5. Run the server locally:

   python main.py

The app will be available at http://localhost:5000/.

---

💡 Usage

- Register: /register to create a new account - Login: /login to sign in - Chatbot: /chatbot for general Q&A - Agent: /agent for advanced tool-using AI - File Upload: Use the file input in chat to upload documents - Conversation History: View and revisit past conversations in the sidebar

---

☁️ Deployment

- Heroku: Ready for Heroku deployment. Use the included Procfile and set environment variables in the Heroku dashboard. - Static Files: All frontend assets are served from the static/ directory.

---

🔒 Security & Best Practices

- Authentication tokens stored securely (cookies/localStorage) - Passwords hashed before storage - Email verification required for new accounts - CORS enabled for cross-origin requests

---

🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.

---

📄 License

This project is licensed under the MIT License.

---

🙏 Acknowledgements

- FastAPI - MongoDB - OpenAI - Heroku

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About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Multi-User MCP Server with Chatbot and Agent

🚀 Overview

✨ Features

🛠️ Tech Stack

📁 Project Structure

⚡ Getting Started

Prerequisites

Installation

💡 Usage

☁️ Deployment

🔒 Security & Best Practices

🤝 Contributing

📄 License

🙏 Acknowledgements

Install

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