Multi-User MCP Server with Chatbot and Agent

@r16academy

About Multi-User MCP Server with Chatbot and Agent

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

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Multi-User MCP Server with Chatbot and Agent?

A full-stack web application implementing the Model Context Protocol (MCP) that provides two AI assistant experiences: a conversational Chatbot and a tool-using Agent. It includes user authentication, persistent chat history, real-time WebSocket messaging, and file upload for document analysis. Designed for developers and teams wanting a production-ready, multi-user AI chat platform deployable on Heroku.

How to use Multi-User MCP Server with Chatbot and Agent?

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.

Key features of Multi-User MCP Server with Chatbot and Agent

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.

Use cases of Multi-User MCP Server with Chatbot and Agent

Deploy a multi-user AI chat platform with account management.
Provide a chatbot for general Q&A and an agent for tool-driven tasks.
Enable document analysis through file uploads in conversations.
Host a production-ready MCP server integration on Heroku.

FAQ from Multi-User MCP Server with Chatbot and Agent

What platforms does it integrate with?

It integrates with OpenAI API or compatible LLM providers via the Agent and Chatbot interfaces, and uses MongoDB for data storage.

What are the runtime requirements?

Python 3.10+, MongoDB (local or Atlas), and optionally Node.js for MCP server dependencies. Heroku deployment requires the Heroku CLI.

How is data stored and persisted?

Conversation history is stored per user in MongoDB, with authentication tokens secured via cookies/localStorage and passwords hashed before storage.

Does it support real-time communication?

Yes, it uses WebSocket-based instant messaging for real-time chat.

What transports and authentication are available?

The server uses HTTP for REST endpoints and WebSocket for real-time chat. Authentication is handled via registration and login with email verification, and tokens stored securely in cookies/localStorage.

Frequently asked questions

What platforms does it integrate with?

It integrates with OpenAI API or compatible LLM providers via the Agent and Chatbot interfaces, and uses MongoDB for data storage.

What are the runtime requirements?

Python 3.10+, MongoDB (local or Atlas), and optionally Node.js for MCP server dependencies. Heroku deployment requires the Heroku CLI.

How is data stored and persisted?

Conversation history is stored per user in MongoDB, with authentication tokens secured via cookies/localStorage and passwords hashed before storage.

Does it support real-time communication?

Yes, it uses WebSocket-based instant messaging for real-time chat.

What transports and authentication are available?

Comments

Basic information

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