Model Context Protocol (MCP) Server for Winston AI - the most accurate AI Detector. Detect AI-generated content, plagiarism, and compare texts with ease.

• Human vs AI Classification: Determine if text was written by a human or AI
• Confidence Scoring: Get percentage-based confidence scores
• Sentence-level Analysis: Identify the most AI-like sentences in your text
• Multi-language Support: Works with text in various languages
• Credit cost: 1 credit per word

• Image Analysis: Detect AI-generated images using advanced ML models
• Metadata Verification: Analyze image metadata and EXIF data
• Watermark Detection: Identify AI watermarks and their issuers
• Multiple Formats: Supports JPG, JPEG, PNG, and WEBP formats
• Credit cost: 300 credits per image

• Internet-wide Scanning: Check against billions of web pages
• Source Identification: Find and list original sources
• Detailed Reports: Get comprehensive plagiarism analysis
• Academic & Professional Use: Perfect for content verification
• Credit cost: 2 credits per word

• Similarity Analysis: Compare two texts for similarities
• Word-level Matching: Detailed breakdown of matching content
• Percentage Scoring: Get precise similarity percentages
• Bidirectional Analysis: Compare both directions
• Credit cost: 1/2 credit per total words found in both texts

• Node.js 18+
• Winston AI API Key (Get one here)

env WINSTONAI_API_KEY=your-api-key npx -y winston-ai-mcp

# Clone the repository
git clone https://github.com/gowinston-ai/winston-ai-mcp-server.git
cd winston-ai-mcp-server
# Install dependencies
npm install
# Build the project and start the server
npm run mcp-start

Create a .env file in your project root:

WINSTONAI_API_KEY=your_actual_api_key_here

Build and run with Docker:

# Build the image
docker build -t winston-ai-mcp .
# Run the container
docker run -e WINSTONAI_API_KEY=your_api_key winston-ai-mcp

• npm run build - Compile TypeScript to JavaScript
• npm start - Start the MCP server
• npm run mcp-start - Compile TypeScript to JavaScript and Start the MCP server
• npm run lint - Run ESLint for code quality
• npm run format - Format code with Prettier

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "winston-ai-mcp": {
      "command": "npx",
      "args": ["-y", "winston-ai-mcp"],
      "env": {
        "WINSTONAI_API_KEY": "your-api-key"
      }
    }
  }
}

Add to your Cursor configuration:

{
  "mcpServers": {
    "winston-ai-mcp": {
      "command": "npx",
      "args": ["-y", "winston-ai-mcp"],
      "env": {
        "WINSTONAI_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "text": "Your text to analyze (600+ characters recommended)",
  "file": "(optional) A file to scan. If you supply a file, the API will scan the content of the file. The file must be in plain .pdf, .doc or .docx format.",
  "website": "(optional) A website URL to scan. If you supply a website, the API will fetch the content of the website and scan it. The website must be publicly accessible."
}

{
  "url": "https://example.com/image.jpg"
}

{
  "text": "Text to check for plagiarism",
  "language": "en", // optional, default: "en"
  "country": "us"   // optional, default: "us"
}

{
  "first_text": "First text to compare",
  "second_text": "Second text to compare"
}

We welcome contributions!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

• Winston AI MCP NPM Package: https://www.npmjs.com/package/winston-ai-mcp
• Winston AI Website: https://gowinston.ai
• API Documentation: https://dev.gowinston.ai
• MCP Protocol: https://modelcontextprotocol.io
• GitHub Repository: https://github.com/gowinston-ai/winston-ai-mcp-server

If you find this project helpful, please give it a star on GitHub!

Made with ❤️ by the Winston AI Team