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Multi-User MCP Server with Chatbot and Agent

@r16academy

代码仓库

基本信息

分类

AI 与智能体

许可证

MIT license

传输方式

stdio

作者

r16academy

关于 Multi-User MCP Server with Chatbot and Agent

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

Multi-User MCP Server with Chatbot and Agent 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Multi-User MCP Server with Chatbot and Agent 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

常见问题

1

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.

2

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.

3

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.

4

Does it support real-time communication?

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

5

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.

评论

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