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mcp-mirage-brand-extract

by m2ai-mcp-servers
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

mcp-mirage-brand-extract

An MCP (Model Context Protocol) server that extracts brand identity from any website and recreates it programmatically.

Features

  • Extract Brand - Analyze any website to extract colors, typography, spacing, and button styles

  • Generate Replica - Create HTML/CSS components using extracted brand data

  • Replicate Website - One-step extraction and generation

  • Compare Brands - Compare brand identities between two websites

  • Apply to Template - Generate pre-built templates with extracted branding

Related MCP server: design-copier

Requirements

Installation

Using uv (recommended)

cd mcp-mirage-brand-extract
uv pip install -e .

Using pip

cd mcp-mirage-brand-extract
pip install -e .

Configuration

  1. Copy the environment template:

cp .env.example .env

  1. Add your API keys to .env:

FIRECRAWL_API_KEY=your_firecrawl_key
GOOGLE_API_KEY=your_google_key

Usage

Running the Server

# With uv
uv run python server.py

# Or directly
python server.py

Testing with MCP Inspector

mcp dev server.py

Adding to Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "mirage-brandextract": {
      "command": "python",
      "args": ["/path/to/mirage-brandextract-mcp/server.py"],
      "env": {
        "FIRECRAWL_API_KEY": "your_key",
        "GOOGLE_API_KEY": "your_key"
      }
    }
  }
}

Tools

extract_brand

Extract complete brand identity from a website.

# Parameters
url: str                      # Target website URL
include_screenshots: bool     # Include visual screenshots (default: False)

# Returns
{
    "colors": {
        "primary": "#FF5A5F",
        "secondary": "#00A699",
        "palette": [...]
    },
    "typography": {
        "headings": "Circular",
        "body": "Circular",
        "weights": [400, 500, 700]
    },
    "spacing": {...},
    "buttons": {...},
    "url": "original_url"
}

generate_replica

Generate HTML/CSS using extracted brand data.

# Parameters
brand_data: dict          # Output from extract_brand
component_type: str       # "landing_page", "email", "button", "card"
customization: str        # Additional instructions

# Returns
{
    "html": "<div>...</div>",
    "css": "/* styles */",
    "preview_url": "data:text/html;base64,..."
}

replicate_website

Complete workflow: extract brand + generate replica in one step.

# Parameters
url: str              # Target website URL
component_type: str   # What to generate
customization: str    # Additional instructions

# Returns
{
    "brand_data": {...},
    "generated": {
        "html": "...",
        "css": "..."
    }
}

compare_brands

Compare brand identities from two websites.

# Parameters
url1: str    # First website URL
url2: str    # Second website URL

# Returns
{
    "site1": {...brand_data...},
    "site2": {...brand_data...},
    "comparison": {
        "color_similarity": 0.85,
        "typography_match": true,
        "font_overlap": ["Inter"],
        "differences": [...]
    }
}

apply_brand_to_template

Apply brand to pre-built templates.

# Parameters
url: str              # Source website for branding
template_type: str    # "hero_section", "pricing_table", "feature_grid", "testimonial", "cta"

# Returns
{
    "html": "...",
    "css": "...",
    "preview_url": "...",
    "template_type": "hero_section"
}

Example Use Cases

1. Extract Airbnb branding and create a hero section

extract_brand(url="https://airbnb.com")
generate_replica(brand_data=result, component_type="landing_page", customization="Focus on hero section")

2. Compare competitor websites

compare_brands(url1="https://airbnb.com", url2="https://vrbo.com")

3. Quick template generation

apply_brand_to_template(url="https://stripe.com", template_type="pricing_table")

Development

Running Tests

pytest tests/ -v

Code Formatting

black src/ tests/
ruff check src/ tests/

License

MIT

Built autonomously by GRIMLOCK - Autonomous MCP Server Factory

This server cannot be installed

A
license - permissive license
-
quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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