Turn Any Text into a Visual Mind Map with AI: Introducing @lucianaib/mind-map-mcp

Introduction

Since using Trae, I have become enthusiastic about its free, efficient AI capabilities and discovered the Model Context Protocol (MCP), which enables seamless communication between tools. Inspired by this, I created the mind‑map‑MCP tool ( @lucianaib/mind-map-mcp) that automatically generates high‑quality mind maps from text and returns accessible image links.

Demo

AI Conversation

Mind‑Map Result

Example of a generated mind map:

Mind‑Map MCP Overview

A server that generates mind maps via MCP.

Features

🧠 Automatically generate mind maps from text

🔗 Return accessible image links

🚀 Support Trae, Cursor and other MCP clients

🌐 Powered by Coze API

Installation

Via npm

npm install -g @lucianaib/mind-map-mcp

Via npx

npx @lucianaib/mind-map-mcp

Usage

After configuration, use the following prompts in any MCP‑compatible tool:

Generate a mind map

请帮我生成一个关于"分布式系统架构"的思维导图

Or call the tool directly:

使用 generate_mindmap 工具，内容为："机器学习的基本概念和应用"

Available Tool

generate_mindmap

Creates a mind map from the provided content.

Parameters: content (string, required): Description to be converted into a mind map

Returns:

Image link of the mind map

Generation status information

Example:

{
  "content": "深度学习的发展历程和主要技术"
}

API Details

Endpoint: https://api.coze.cn/v1/workflow/run Method: POST

Authentication: Bearer Token

Response: Mind‑map image link

Trae MCP Configuration

Run Trae as administrator, click “Configure MCP”, then add the following JSON and save (Ctrl+S). A green circle indicates successful configuration.

{
  "mcpServers": {
    "mind-map": {
      "timeout": 60,
      "type": "stdio",
      "command": "npx",
      "args": ["@lucianaib/mind-map-mcp@latest"]
    }
  }
}

AI Dialogue Example

请帮我生成一个关于"分布式系统架构"的思维导图

The AI successfully calls generate_mindmap and returns a clickable image link.

Future Outlook

Planned improvements include upgrading the underlying algorithms with more advanced NLP techniques, adding multi‑language support, expanding template libraries, enabling interactive editing, and integrating with project‑management tools such as Jira or Trello. Community building, tutorials, and case studies will help broaden adoption across education, research, and commercial domains.

Conclusion

The tool can be installed with a single npm or npx command, configured in Trae via a short JSON snippet, and then used to transform any text into a clear, structured mind‑map image through the Coze workflow API. It demonstrates how combining MCP, Node.js, and AI can produce a practical, high‑value utility for knowledge organization and project planning.