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.
@r16academy
Basic information
Multi user Full Stack App with Chatbot and Agent in Javascript, FastAPI and PyMongo
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-server-beginner-app": {
"command": "python",
"args": [
"main.py"
]
}
}
}Tool list not available yet
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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.
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.
It integrates with OpenAI API or compatible LLM providers via the Agent and Chatbot interfaces, and uses MongoDB for data storage.
Python 3.10+, MongoDB (local or Atlas), and optionally Node.js for MCP server dependencies. Heroku deployment requires the Heroku CLI.
Conversation history is stored per user in MongoDB, with authentication tokens secured via cookies/localStorage and passwords hashed before storage.
Yes, it uses WebSocket-based instant messaging for real-time chat.
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.
MCP-native LLM councils for debates, juries, blind panels, voting, refinement, and custom multi-model deliberation.
Model Context Protocol (MCP) server for Hostinger API. If you don't want to install or run anything locally, connect directly to Hostinger's hosted MCP server: This opens a browser window to authorize via OAuth. Once app
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Build and edit landing pages and websites from your AI assistant, and pull how they are performing.
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