Imported from reason-machines/mcp-skills (skills/lanhu-mcp-collaboration/SKILL.md). Install upstream with npx skills add reason-machines/mcp-skills --skill lanhu-mcp-collaboration. Copyright stays with the author.

Lanhu MCP Collaboration

Skill by ara.so — MCP Skills collection.

Overview

Lanhu MCP Server is a Model Context Protocol server that connects AI coding assistants to the Lanhu (蓝湖) design collaboration platform. It enables automated requirements analysis, UI design extraction, team knowledge sharing, and design-to-code conversion with vision-capable AI models.

Core capabilities:

• Requirements Analysis: Automatic Axure prototype extraction with 3 analysis modes (Development/Testing/Exploration)
• UI Design Support: Design spec extraction with precise parameters (spacing, colors, fonts) + HTML/CSS code generation
• Team Message Board: Shared knowledge base across all AI assistants, breaking IDE silos
• Smart Asset Export: Automatic design slice extraction with semantic naming
• Performance: Version-based caching, incremental updates, concurrent processing

Supported AI Clients: Cursor, Windsurf, Claude Code, OpenClaw, ClawBot, Trae, Cline, and any MCP-compatible tool

Installation

Prerequisites

• Python 3.10+
• Vision-capable AI model (Claude, GPT-4V, Gemini, Kimi, Qwen, DeepSeek)
• Lanhu account with valid cookie authentication

Quick Install (Recommended)

Simply ask your AI assistant:

"Help me clone and install https://github.com/dsphper/lanhu-mcp"

The AI will guide you through cloning, dependency installation, cookie configuration, and server startup.

Manual Installation

Option 1: Docker (Recommended)

# Clone repository
git clone https://github.com/dsphper/lanhu-mcp.git
cd lanhu-mcp

# Configure environment (interactive cookie setup)
bash setup-env.sh  # Linux/Mac
# or
setup-env.bat      # Windows

# Start service
docker-compose up -d

Option 2: Source Code

# Clone repository
git clone https://github.com/dsphper/lanhu-mcp.git
cd lanhu-mcp

# One-click installation (includes cookie setup)
bash easy-install.sh  # Linux/Mac
# or
easy-install.bat      # Windows

Manual dependency installation:

pip install -r requirements.txt
playwright install chromium

Configuration

Required: Lanhu Cookie

Export your Lanhu cookie (obtained from browser DevTools after logging into lanhuapp.com):

export LANHU_COOKIE="your_lanhu_cookie_here"

Optional: Feishu Webhook

For team notifications and @mentions:

export FEISHU_WEBHOOK_URL="https://open.feishu.cn/open-apis/bot/v2/hook/your-webhook-url"

Or edit lanhu_mcp_server.py:

DEFAULT_FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/your-webhook-url"

Optional: Server Configuration

export SERVER_HOST="0.0.0.0"
export SERVER_PORT=8000
export DATA_DIR="./data"
export HTTP_TIMEOUT=30
export VIEWPORT_WIDTH=1920
export VIEWPORT_HEIGHT=1080
export DEBUG="false"

Start Server

Source code:

python lanhu_mcp_server.py

Docker:

docker-compose up -d
docker-compose logs -f  # View logs
docker-compose down     # Stop

Server runs at http://localhost:8000/mcp

AI Client Configuration

Claude Code

In claude_desktop_config.json:

{
  "mcpServers": {
    "lanhu": {
      "type": "http",
      "url": "http://localhost:8000/mcp?role=Developer&name=YourName"
    }
  }
}

Cursor / Windsurf / Others

In MCP settings:

{
  "mcpServers": {
    "lanhu": {
      "url": "http://localhost:8000/mcp?role=Developer&name=YourName"
    }
  }
}

URL Parameters:

• role: User role (Developer/Frontend/Backend/Tester/Product)
• name: Username for collaboration tracking and @mentions (use English to avoid encoding issues)

Key Tools & Commands

1. Requirements Analysis

Tool: analyze_requirements_document

Analyze Axure prototypes with AI-powered extraction:

# User prompt example:
"Please analyze this requirements document using MCP:
https://lanhuapp.com/web/#/item/project/product?tid=xxx&pid=xxx&docId=xxx"

# The AI will call:
analyze_requirements_document(
    url="https://lanhuapp.com/web/#/item/project/product?tid=xxx&pid=xxx&docId=xxx",
    mode="development"  # Options: development | testing | exploration
)

Analysis Modes:

• development: Detailed field rules, business logic, global flowcharts
• testing: Test scenarios, test cases, boundary values, validation rules
• exploration: Core feature overview, module dependencies, review points

Four-Stage Workflow:

1. Global text scanning (establish overall understanding)
2. Grouped detailed analysis (based on selected mode)
3. Reverse validation (ensure zero omissions)
4. Generate deliverables (requirements doc/test plan/review slides)

2. UI Design Analysis

Tool: view_design_document

Extract design specs with precise parameters and generated code:

# User prompt:
"Please view this design document using MCP:
https://lanhuapp.com/web/#/item/project/stage?tid=xxx&pid=xxx"

# Returns:
# - Design image previews
# - Precise parameters (dimensions, spacing, colors, fonts)
# - HTML + CSS code conversion

Output includes:

• Component dimensions and spacing
• Color values (HEX/RGB)
• Font sizes and weights
• Auto-generated HTML/CSS code matching Lanhu's native export

3. Design Asset Export

Tool: export_design_slices

Download design slices with semantic naming:

# User prompt:
"Export all design slices from this Lanhu page"

export_design_slices(
    design_url="https://lanhuapp.com/web/#/item/project/stage?tid=xxx&pid=xxx",
    output_dir="./assets"
)

Features:

• Automatic slice detection
• Semantic file naming based on layer paths
• Organized folder structure
• Supports PNG, SVG, and other formats

4. Team Message Board

Tools: create_message, list_messages, search_messages

Share knowledge and context across all team AI assistants:

# Create knowledge entry
create_message(
    project_url="https://lanhuapp.com/web/#/item/project/...",
    content="User authentication requires OAuth2 flow with refresh token rotation",
    message_type="knowledge",  # Options: knowledge | task | question | experience
    tags=["auth", "security", "backend"]
)

# Search team knowledge
search_messages(
    project_url="https://lanhuapp.com/web/#/item/project/...",
    keyword="authentication",
    message_type="knowledge"
)

# @mention team member (triggers Feishu notification)
create_message(
    project_url="https://lanhuapp.com/web/#/item/project/...",
    content="@zhangsan Please review the API error handling logic",
    message_type="task",
    mentioned_users=["zhangsan"]
)

Message Types:

• knowledge: Permanent knowledge base entries (pitfalls, best practices)
• task: Task assignments with @mention support
• question: Questions for team discussion
• experience: Lessons learned and implementation notes

Common Patterns

Pattern 1: Full Requirements Analysis Workflow

# Step 1: User provides Lanhu URL
user: "Analyze requirements: https://lanhuapp.com/web/#/item/project/product?tid=123&pid=456&docId=789"

# Step 2: AI calls analyze_requirements_document
result = analyze_requirements_document(
    url="https://lanhuapp.com/web/#/item/project/product?tid=123&pid=456&docId=789",
    mode="development"
)

# Step 3: AI processes four-stage analysis
# - Stage 1: Scans all pages and extracts text
# - Stage 2: Groups pages and analyzes by business modules
# - Stage 3: Reverse validates for missing items
# - Stage 4: Generates structured requirements document

# Step 4: Save insights to team knowledge base
create_message(
    project_url="https://lanhuapp.com/web/#/item/project/product?tid=123&pid=456",
    content="Key finding: User role permissions require cascading delete logic",
    message_type="knowledge",
    tags=["permissions", "database"]
)

Pattern 2: Design-to-Code Implementation

# Step 1: View design and get parameters
user: "Implement this design: https://lanhuapp.com/web/#/item/project/stage?tid=123&pid=456"

design_data = view_design_document(
    url="https://lanhuapp.com/web/#/item/project/stage?tid=123&pid=456"
)

# Step 2: Extract design parameters
# Returns:
# {
#   "preview_image": "base64_image_data",
#   "parameters": {
#     "width": "375px",
#     "height": "812px",
#     "spacing": {"top": "20px", "left": "16px"},
#     "colors": {"primary": "#1677FF", "text": "#333333"},
#     "fonts": {"title": "16px/bold", "body": "14px/regular"}
#   },
#   "html_css": "<div class='container'>...</div>\n<style>...</style>"
# }

# Step 3: Export required assets
export_design_slices(
    design_url="https://lanhuapp.com/web/#/item/project/stage?tid=123&pid=456",
    output_dir="./src/assets/images"
)

# Step 4: AI generates implementation code using parameters + HTML/CSS reference

Pattern 3: Team Collaboration Tracking

# Developer A's AI analyzes requirements
analyze_requirements_document(
    url="https://lanhuapp.com/web/#/item/project/product?tid=123&pid=456&docId=789",
    mode="development"
)

# Save analysis results
create_message(
    project_url="https://lanhuapp.com/web/#/item/project/product?tid=123&pid=456",
    content="Requirements analysis complete. 5 core modules identified: User, Product, Order, Payment, Notification",
    message_type="knowledge",
    tags=["requirements", "architecture"]
)

# Tester B's AI searches team knowledge
messages = search_messages(
    project_url="https://lanhuapp.com/web/#/item/project/product?tid=123&pid=456",
    keyword="requirements analysis",
    message_type="knowledge"
)
# Returns Developer A's analysis — no duplicate work!

# Tester B's AI now performs test-focused analysis
analyze_requirements_document(
    url="https://lanhuapp.com/web/#/item/project/product?tid=123&pid=456&docId=789",
    mode="testing"
)

Pattern 4: Environment Variable Best Practices

# Never hardcode secrets
# ❌ BAD:
LANHU_COOKIE = "abc123..."
FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/xxx"

# ✅ GOOD: Use environment variables
import os

LANHU_COOKIE = os.getenv("LANHU_COOKIE")
FEISHU_WEBHOOK = os.getenv("FEISHU_WEBHOOK_URL")

# Validate required config
if not LANHU_COOKIE:
    raise ValueError("LANHU_COOKIE environment variable is required")

Troubleshooting

Issue: "No vision-capable model detected"

Cause: Using text-only AI model (e.g., GPT-3.5, Claude Instant)

Solution: Switch to vision-capable model:

• Claude 3+ (Sonnet, Opus)
• GPT-4V, GPT-4o
• Gemini Pro Vision
• Kimi, Qwen-VL, DeepSeek-VL

Issue: "Cookie authentication failed"

Cause: Invalid or expired Lanhu cookie

Solution:

1. Login to https://lanhuapp.com in browser
2. Open DevTools → Network tab
3. Find any API request to lanhuapp.com
4. Copy full Cookie header value
5. Update LANHU_COOKIE environment variable
6. Restart server

Issue: "Design-to-code conversion unavailable"

Cause: Design file uploaded with outdated Lanhu plugin

Solution:

1. Ask UI designer to update Lanhu plugin (Figma/Sketch/Adobe XD)
2. Re-upload design file
3. Retry design analysis

Issue: "Message board not syncing across AI assistants"

Cause: Different MCP server instances or cache issues

Solution:

1. Ensure all AI clients connect to same MCP server URL
2. Verify project_url is identical across calls
3. Clear cache: rm -rf ./data/cache/*
4. Restart MCP server

Issue: Docker container fails to start

Cause: Port conflict or missing environment variables

Solution:

# Check port availability
lsof -i :8000

# Verify environment variables
docker-compose config

# Check logs
docker-compose logs lanhu-mcp

# Restart with clean state
docker-compose down -v
docker-compose up -d

Issue: Slow requirements analysis

Cause: Large prototype with many pages, no caching

Solution:

1. Enable version-based caching (automatic)
2. Use exploration mode for quick overview
3. Increase concurrent processing:

   export HTTP_TIMEOUT=60
   export VIEWPORT_WIDTH=1920
   export VIEWPORT_HEIGHT=1080

4. Subsequent analyses will use cached data (much faster)

Advanced Usage

Custom Analysis Modes

Modify lanhu_mcp_server.py to add custom analysis perspectives:

ANALYSIS_MODES = {
    "development": "Developer perspective with detailed field rules",
    "testing": "QA perspective with test cases and validation",
    "exploration": "Quick overview for stakeholder review",
    "security": "Security-focused analysis for audit"  # Custom mode
}

Feishu User ID Mapping

Enable @mention notifications by updating FEISHU_USER_ID_MAP:

FEISHU_USER_ID_MAP = {
    "zhangsan": "ou_1234567890abcdef",
    "lisi": "ou_abcdef1234567890",
    # Add your team members
}

Performance Tuning

# Increase concurrent downloads
export HTTP_TIMEOUT=60

# Larger viewport for high-res screenshots
export VIEWPORT_WIDTH=2560
export VIEWPORT_HEIGHT=1440

# Enable debug logging
export DEBUG="true"

Integration Examples

Cursor AI Integration

// In Cursor, add to .cursor/mcp.json
{
  "mcpServers": {
    "lanhu": {
      "url": "http://localhost:8000/mcp?role=Frontend&name=Alice"
    }
  }
}

// Then prompt:
// "Using Lanhu MCP, analyze the design at https://lanhuapp.com/... 
//  and generate React components with Tailwind CSS"

Windsurf Cascade Integration

// In Windsurf settings
{
  "mcp": {
    "servers": {
      "lanhu": {
        "url": "http://localhost:8000/mcp?role=Fullstack&name=Bob"
      }
    }
  }
}

Claude Code Integration

// In claude_desktop_config.json
{
  "mcpServers": {
    "lanhu": {
      "type": "http",
      "url": "http://localhost:8000/mcp?role=Backend&name=Charlie"
    }
  }
}

Additional Resources

• Cookie Setup Guide: GET-COOKIE-TUTORIAL.md
• AI Installation Guide: ai-install-guide.md
• Docker Deployment: DEPLOY.md
• Contributing: CONTRIBUTING.md
• GitHub: https://github.com/dsphper/lanhu-mcp
• MCP Protocol: https://modelcontextprotocol.io/

License

MIT License - See LICENSE file for details.