DarkLens – Dark Pattern Detection MCP Server

A production-grade Model Context Protocol (MCP) server that enables AI agents to detect, classify, explain, and ethically redesign dark patterns in websites and UI flows.

Overview

DarkLens analyzes UI elements, consent flows, pricing structures, and interaction friction to identify manipulative design patterns. It provides structured JSON outputs suitable for reasoning and compliance assessment.

Related MCP server: websight

Features

• Pattern Detection: Rule-based + NLP heuristics for identifying 9+ dark pattern categories

• Classification: Categorizes patterns with cognitive bias analysis and severity levels

• Ethical Explanations: Plain-English explanations of manipulation tactics and harm potential

• Compliance Assessment: Risk scoring under GDPR, FTC, and DPDP regulations

• Ethical Alternatives: Suggests redesigned UI flows and copy

Supported Dark Patterns

• Confirmshaming

• Forced Consent

• Roach Motel

• Hidden Costs

• Sneak Into Basket

• Fake Urgency

• Visual Manipulation

• Default Bias Exploitation

• Nagging / Repeated Interruptions

• Social Proof Manipulation (Enhanced with Kaggle dataset)

Data Sources

The detection system is enhanced with real-world examples from the Kaggle Dark Patterns Dataset, providing 2,000+ labeled examples of dark patterns across e-commerce and web interfaces.

Installation

Prerequisites

• Python 3.10 or higher

• uv package manager (recommended) or pip

Install with uv (Recommended)

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone or navigate to the project directory
cd /path/to/DarkLens-MCP-Server

# Install dependencies
uv sync

Install with pip

# Install dependencies
pip install -e .

Project Structure

DarkLens-MCP-Server/
├── darklens_mcp_server/
│   └── server.py          # Main MCP server implementation
├── data/
│   └── dark_patterns.json # Pattern taxonomy database
├── demo/                  # Example usage scripts
├── pyproject.toml         # Project configuration
├── requirements.txt       # Alternative dependencies
├── README.md             # This file
└── uv.lock               # Lock file for uv

Server Components

Resources

• dark_patterns://taxonomy: Complete dark patterns taxonomy

• ui_text://{url}: Extracted UI text elements from webpages

Tools

• detect_dark_patterns: Analyze HTML/text/URL for dark patterns

• classify_pattern: Get detailed classification of a pattern

• explain_manipulation: Explain psychological manipulation

• risk_score: Assess legal/compliance risk

• suggest_ethical_alternative: Get ethical redesign suggestions

Prompts

• audit_website: Comprehensive website audit template

• explain_ui_to_user: UI explanation for non-technical users

• compliance_report: UX ethics compliance report template

• rewrite_cta: Ethical CTA rewrite template

• assess_gdpr_risk: GDPR risk assessment template

Usage

Running the MCP Server

# With uv
uv run darklens_mcp_server

# With Python
python -m darklens_mcp_server.server

The server communicates via stdio for MCP protocol.

MCP Client Integration

Connect using any MCP-compatible client.

Example Tool Call

{
  "method": "tools/call",
  "params": {
    "name": "detect_dark_patterns",
    "arguments": {
      "input_type": "url",
      "content": "https://example.com"
    }
  }
}

Example Response

{
  "result": [
    {
      "pattern_id": "confirmshaming",
      "pattern_type": "Confirmshaming",
      "confidence": 0.9,
      "evidence": ["No thanks, I don't want to save money"]
    }
  ]
}

API Reference

detect_dark_patterns

Input:

• input_type: "html" | "text" | "url"

• content: String content to analyze

Output: Array of detected patterns with ID, type, confidence, and evidence.

classify_pattern

Input: pattern_id: String Output: Category, cognitive bias, severity level.

explain_manipulation

Input: pattern_id: String, user_type: "child" | "elderly" | "average user" Output: Plain explanation, psychological principle, harm potential.

risk_score

Input: pattern_id: String, region: "EU" | "US" | "India" Output: Risk score (0-100), violated regulations, enforcement likelihood.

suggest_ethical_alternative

Input: pattern_id: String Output: Rewritten UI copy, redesigned flow, ethical justification.

Ethical Considerations

This tool is designed to promote ethical UX design and regulatory compliance. Use responsibly to:

• Audit websites for manipulative patterns

• Educate designers on ethical alternatives

• Ensure compliance with privacy and consumer protection laws

• Improve user trust and experience

Disclaimer: This tool provides analysis based on established dark pattern research but should not be considered legal advice. Always consult with legal experts for compliance matters.

Contributing

Contributions welcome! Please ensure code follows the established patterns and includes appropriate tests.

License

MIT License

Resources

Resources provide read-only access to data. They are like GET endpoints in REST APIs.

Static Resources

@mcp.resource("users://list")
def get_users_list() -> str:
    """Get a list of all users."""
    return json.dumps(SAMPLE_USERS, indent=2)

Dynamic Resources with Parameters

@mcp.resource("users://{user_id}")
def get_user_by_id(user_id: str) -> str:
    """Get a specific user by ID."""
    # Implementation...

External API Resources

@mcp.resource("api://external/{endpoint}")
async def get_external_api_data(endpoint: str) -> str:
    """Fetch data from an external API endpoint."""
    # Implementation...

Tools

Tools are executable functions that can perform computations, API calls, or other actions. They may require user approval before execution.

Synchronous Tools

@mcp.tool()
def calculate_sum(numbers: List[float]) -> float:
    """Calculate the sum of a list of numbers."""
    return sum(numbers)

Asynchronous Tools

@mcp.tool()
async def fetch_user_posts(user_id: int) -> str:
    """Fetch posts for a specific user from external API."""
    async with httpx.AsyncClient() as client:
        response = await client.get(f"{API_BASE_URL}/posts?userId={user_id}")
        return json.dumps(response.json(), indent=2)

Tools with Complex Logic

@mcp.tool()
def analyze_text(text: str) -> Dict[str, Any]:
    """Analyze text and return statistics."""
    words = text.split()
    return {
        "word_count": len(words),
        "character_count": len(text),
        # ... more analysis
    }

Prompts

Prompts are reusable templates that help LLMs interact effectively with your server. They define expected inputs and interaction patterns.

Simple Prompts

@mcp.prompt()
def summarize_content(content: str, max_length: int = 100) -> str:
    """Create a prompt to summarize content."""
    return f"Please summarize the following content in {max_length} words or less:\n\n{content}"

Complex Prompts

@mcp.prompt()
def create_study_plan(subject: str, hours_per_week: int, weeks: int) -> str:
    """Create a study plan prompt."""
    return f"""Create a detailed study plan for learning {subject}.

Available time: {hours_per_week} hours per week
Duration: {weeks} weeks

Please include:
1. Weekly breakdown of topics
2. Daily study schedule
3. Recommended resources
4. Assessment milestones
5. Tips for effective learning"""

Running the Server

Using uv

uv run darklens-server

Using Python directly

python -m darklens_mcp_server.server

Using the script

darklens-server

The server will start and listen for MCP protocol messages over stdio.

Testing the Server

Using MCP Inspector

The easiest way to test your MCP server is using the MCP Inspector:

# Install MCP Inspector globally
npm install -g @modelcontextprotocol/inspector

# Run the inspector with your server
mcp-inspector uv run darklens-server

This will open a web interface where you can interact with your server's resources, tools, and prompts.

Manual Testing with Client

You can also create a simple test client:

import asyncio
from mcp.client.session import ClientSession
from mcp.client.stdio import StdioServerParameters, stdio_client

async def test_server():
    async with stdio_client(
        StdioServerParameters(command="uv", args=["run", "darklens-server"])
    ) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List resources
            resources = await session.list_resources()
            print("Available resources:", [r.uri for r in resources.resources])

            # List tools
            tools = await session.list_tools()
            print("Available tools:", [t.name for t in tools.tools])

            # List prompts
            prompts = await session.list_prompts()
            print("Available prompts:", [p.name for p in prompts.prompts])

            # Test a resource
            resource_content = await session.read_resource("users://list")
            print("Users resource content:", resource_content.contents[0].text)

            # Test a tool
            result = await session.call_tool("calculate_sum", {"numbers": [1, 2, 3, 4, 5]})
            print("Sum tool result:", result.content[0].text)

            # Test a prompt
            prompt_result = await session.get_prompt("summarize_content", {
                "content": "This is a sample text to summarize.",
                "max_length": 50
            })
            print("Prompt result:", prompt_result.messages[0].content)

asyncio.run(test_server())

Integration with Claude Desktop

To use this server with Claude Desktop:

1. Configure Claude Desktop:

   • Open ~/Library/Application Support/Claude/claude_desktop_config.json

   • Add your server configuration:

{
  "mcpServers": {
    "darklens": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/DarkLens-MCP-Server",
        "run",
        "darklens-server"
      ]
    }
  }
}

2. Restart Claude Desktop

3. Test in Claude:

   • Ask Claude to "list available resources" or "use the calculate_sum tool"

   • Try prompts like "create a study plan for learning Python with 10 hours per week for 8 weeks"

Available Resources

• users://list - List all sample users

• users://{user_id} - Get specific user by ID

• posts://list - List all sample posts

• api://external/{endpoint} - Fetch data from JSONPlaceholder API

Available Tools

• calculate_sum - Sum a list of numbers

• find_max - Find maximum value in a list

• reverse_string - Reverse a string

• fetch_user_posts - Fetch posts for a user from external API

• analyze_text - Analyze text statistics

Available Prompts

• summarize_content - Create a summarization prompt

• analyze_sentiment - Create a sentiment analysis prompt

• generate_code - Create a code generation prompt

• create_study_plan - Create a study plan prompt

Best Practices

Resources

• Keep resource functions lightweight - avoid heavy computation

• Use appropriate MIME types for different content types

• Handle errors gracefully and return meaningful error messages

• Use URI templates for dynamic resources

Tools

• Include clear docstrings with parameter descriptions

• Validate input parameters

• Handle errors appropriately

• Use async functions for I/O operations

• Keep tool names descriptive and consistent

Prompts

• Make prompts flexible with optional parameters

• Include clear instructions for the LLM

• Structure prompts for consistent output

• Use descriptive names and descriptions

General

• Use logging instead of print statements (MCP uses stdio for communication)

• Test your server thoroughly before deployment

• Handle edge cases and invalid inputs

• Follow Python type hints for better tooling support

Troubleshooting

Server won't start

• Check Python version (must be 3.10+)

• Verify all dependencies are installed

• Check for syntax errors in your code

Tools/resources not appearing

• Ensure decorators are applied correctly (@mcp.tool(), not @mcp.tool)

• Check that functions have proper type hints

• Verify server initialization

Claude Desktop integration issues

• Check the path to your server in the config file

• Ensure the server starts without errors

• Restart Claude Desktop after config changes

• Check Claude Desktop logs for errors

Common Errors

• "print() breaks MCP": Never use print() in production - it corrupts the JSON-RPC protocol

• "Async function required": Use async def for functions that perform I/O

• "Invalid URI": Ensure resource URIs follow proper format

Next Steps

• Explore the MCP documentation for advanced features

• Check out the Python SDK examples

• Learn about building MCP clients

• Discover other MCP servers

License

This project is provided as an educational example. Feel free to modify and extend it for your own MCP server implementations.