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A Model Context Protocol (MCP) server for Figma integration

Project description

Figma MCP Server

English: README.en.md | 中文: README.zh-CN.md

English 中文

🌍 Language Selection / 语言选择

🇺🇸 English

For English documentation, please visit: README.en.md

🇨🇳 中文

对于中文文档,请访问:README.zh-CN.md


🚀 Quick Overview / 快速概览

A specialized MCP server designed for developers to systematically extract and organize Figma design files at the page level.

一个专门为程序员设计的MCP服务器,用于系统性地按页面级别提取和组织Figma设计文件。

🎯 Main Feature: Complete Node Data Extraction / 主要功能:完整节点数据提取

get_complete_node_data ⭐ Primary Tool / 主要工具

Get complete node data (tree structure + images) and organize into folders

获取完整的节点数据(树结构 + 图片)并组织到文件夹中

Workflow / 工作流程:

graph TD
    A[Input: file_key + node_ids] --> B[Extract Tree Structure]
    B --> C[Get Node Names]
    C --> D[Download Images]
    D --> E[Organize Files to Folder]
    E --> F[Create Summary Info]
    F --> G[Output Complete Data Package]
  • Parameters / 参数:
    • file_key: Figma file unique identifier / Figma 文件唯一标识符
    • node_ids: Node IDs, comma-separated / 节点 ID,逗号分隔
    • image_format: Image format (default: png) / 图片格式(默认:png)
    • image_scale: Image scale factor (default: 1.0) / 图片缩放因子(默认:1.0)
    • tree_depth: Tree depth (default: 4) / 树深度(默认:4)

Output Structure / 输出结构:

your_node_name_your_node_id_here/
├── nodesinfo.json    # Complete tree structure data (core) / 完整树结构数据(核心)
└── your_node_id_here.png  # Downloaded image file / 下载的图片文件

🧠 Why This Structure for AI Understanding / 为什么这种结构适合 AI 理解

This output structure is specifically designed to help AI understand design comprehensively:

这种输出结构专门设计用于帮助 AI 全面理解设计:

Multi-modal Information Fusion / 多模态信息融合

  • Structured Data (nodesinfo.json): Contains precise positioning, styling, constraints, and hierarchical relationships
  • 结构化数据 (nodesinfo.json): 包含精确的定位、样式、约束和层次关系
  • Visual Data (.png): Provides actual rendered appearance for visual verification
  • 视觉数据 (.png): 提供实际渲染外观用于视觉验证
  • Contextual Information: File naming and organization provide design context
  • 上下文信息: 文件命名和组织提供设计上下文

AI-Friendly Design / AI 友好设计

  • Complete Context: AI can understand both the logical structure and visual appearance
  • 完整上下文: AI 可以理解逻辑结构和视觉外观
  • Relationship Clarity: Hierarchical relationships and constraints are explicitly defined
  • 关系清晰: 层次关系和约束被明确定义
  • Parseable Format: JSON structure allows AI to easily extract and process information
  • 可解析格式: JSON 结构允许 AI 轻松提取和处理信息

Real-world AI Use Cases / 实际 AI 使用场景

  • Design Analysis: "This page has 12 frames, 18 text elements, uses white background..."
  • 设计分析: "此页面有 12 个框架,18 个文本元素,使用白色背景..."
  • Code Generation: "Based on layout constraints, generate React component with 375px width..."
  • 代码生成: "基于布局约束,生成宽度为 375px 的 React 组件..."
  • Design Suggestions: "45 nodes detected, consider componentization to reduce complexity..."
  • 设计建议: "检测到 45 个节点,考虑组件化以减少复杂性..."
  • Responsive Adaptation: "Header uses SCALE constraint, needs mobile layout adjustment..."
  • 响应式适配: "头部使用 SCALE 约束,需要移动端布局调整..."

Optimized Structure / 优化结构

  • Essential Only: Contains only the most important files for AI understanding
  • 仅包含必要: 仅包含 AI 理解所需的最重要文件
  • Efficient Data: Compact output structure while maintaining all necessary information
  • 高效数据: 紧凑的输出结构,同时保持所有必要信息
  • Cost Effective: Minimizes API token usage while maximizing AI understanding
  • 成本效益: 最小化 API 令牌使用,同时最大化 AI 理解

✨ Key Features / 主要功能

  • 📋 Node Listing (list_nodes_depth2) / 节点列表
  • 🔍 Tree Structure Extraction (extract_figma_tree) / 树结构提取
  • 🖼️ Image Download (download_figma_images) / 图片下载
  • 🔧 Complete Data Export (get_complete_node_data) / 完整数据导出
  • 🖼️ Frame Extraction (extract_frame_nodes) / 框架提取
  • 🌐 Cross-platform / 跨平台支持
  • 💡 AI-Optimized Structure / AI优化结构

🛠️ Quick Installation / 快速安装

Option 1: Install from PyPI (Recommended) / 从 PyPI 安装(推荐)

pip install figma-mcp-tools

Option 2: Install from source / 从源码安装

# Clone the repository
git clone https://github.com/DRX-1877/figma-mcp-server.git
cd figma-mcp-server

# Run installation script (automatically configures environment)
./install.sh  # macOS/Linux
# or
install.bat   # Windows

That's it! The command will be available globally after installation.

就这么简单! 安装后命令即可全局使用。

🌍 Global Command Availability / 全局命令可用性

After installation, the figma-mcp-tools command will be available globally from any directory:

安装完成后,figma-mcp-tools 命令将在任何目录中全局可用:

macOS/Linux

# Use from any directory
figma-mcp-tools --help

# If command is not available, run the fix script
./fix-command.sh

# Or manually reload shell configuration
source ~/.zshrc  # for zsh
# or
source ~/.bashrc # for bash

Windows

# Use from any directory
figma-mcp-tools --help

# If command is not available, run the fix script
fix-command.bat

# Or reopen command prompt to reload environment variables

🔧 Automatic Environment Configuration / 自动环境配置

The installation script automatically configures your environment:

安装脚本会自动配置您的环境:

  • PATH Configuration: Adds virtual environment and local bin directories to PATH
  • PATH 配置: 自动将虚拟环境和本地 bin 目录添加到 PATH
  • Global Symlinks: Creates symbolic links in system directories
  • 全局符号链接: 在系统目录中创建符号链接
  • Shell Integration: Updates shell configuration files (.zshrc, .bashrc)
  • Shell 集成: 更新 shell 配置文件 (.zshrc, .bashrc)

No manual configuration required! 🎉

无需手动配置! 🎉

🔍 Troubleshooting / 故障排除

If the command is not available after installation:

如果安装后命令不可用:

macOS/Linux

# Option 1: Run the fix script
./fix-command.sh

# Option 2: Manually reload shell configuration
source ~/.zshrc  # for zsh
# or
source ~/.bashrc # for bash

# Option 3: Open a new terminal window

Windows

# Option 1: Run the fix script
fix-command.bat

# Option 2: Reopen command prompt

# Option 3: Restart computer

📚 Documentation / 文档


📖 Choose Your Language / 选择您的语言

🇺🇸 English Documentation

Complete English documentation with detailed installation instructions, usage examples, and API reference.

🇨🇳 中文文档

完整的中文文档,包含详细的安装说明、使用示例和API参考。

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