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Model Context Protocol (MCP) Server for Winston AI - the most accurate AI Detector.

Created 3 months ago

Model Context Protocol (MCP) Server for Winston AI - the most accurate AI Detector.

development documentation public AI detection plagiarism

What is Model Context Protocol (MCP) Server for Winston AI - the most accurate AI Detector.?

Winston AI MCP Server is designed to detect AI-generated content, plagiarism, and compare texts with ease. It offers features like AI text detection, image detection, plagiarism detection, and text comparison, supporting multiple languages and providing detailed reports.

Documentation

Winston AI MCP Server ⚡️

npm version License: MIT Node.js CI TypeScript

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

✨ Features# 🔍 AI Text Detection

  • 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

🖼️ AI Image Detection

  • 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

📝 Plagiarism Detection

  • 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

🔄 Text Comparison

  • 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

🚀 Quick Start# Prerequisites

🛠️ Development# Running with npx 🔋

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

Running the MCP Server locally via stdio 💻

Create a .env file in your project root:

WINSTONAI_API_KEY=your_actual_api_key_here
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

📦 Docker Support

Build and run with Docker:

docker build -t winston-ai-mcp .

# Run the container
docker run -e WINSTONAI_API_KEY=your_api_key winston-ai-mcp

📋 Available Scripts

  • 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

🔧 Configuration# For Claude Desktop

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"
      }
    }
  }
}

For Cursor IDE

Add to your Cursor configuration:

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

Accessing the MCP Server via API 🌐

Our MCP server is hosted at https://api.gowinston.ai/mcp/v1 and can be accessed via HTTPS requests.

Example: List tools

curl --location 'https://api.gowinston.ai/mcp/v1' \
- -header 'content-type: application/json' \
- -header 'accept: application/json' \
- -header 'jsonrpc: 2.0' \
- -data '{
  "jsonrpc": "2.0",
  "method": "tools/list",
  "id": 1
}'

Example: AI Text Detection

curl --location 'https://api.gowinston.ai/mcp/v1' \
- -header 'content-type: application/json' \
- -header 'accept: application/json' \
- -data '{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "ai-text-detection",
    "arguments": {
      "text": "Your text to analyze (minimum 300 characters)",
      "apiKey": "your-winston-ai-api-key"
    }
  }
}'

Example: AI Image Detection

curl --location 'https://api.gowinston.ai/mcp/v1' \
- -header 'content-type: application/json' \
- -header 'accept: application/json' \
- -data '{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "ai-image-detection",
    "arguments": {
      "url": "https://example.com/image.jpg",
      "apiKey": "your-winston-ai-api-key"
    }
  }
}'

Example: Plagiarism Detection

curl --location 'https://api.gowinston.ai/mcp/v1' \
- -header 'content-type: application/json' \
- -header 'accept: application/json' \
- -data '{
  "jsonrpc": "2.0",
  "id": 3,
  "method": "tools/call",
  "params": {
    "name": "plagiarism-detection",
    "arguments": {
      "text": "Text to check for plagiarism (minimum 100 characters)",
      "apiKey": "your-winston-ai-api-key"
    }
  }
}'

Example: Text Comparison

curl --location 'https://api.gowinston.ai/mcp/v1' \
- -header 'content-type: application/json' \
- -header 'accept: application/json' \
- -data '{
  "jsonrpc": "2.0",
  "id": 4,
  "method": "tools/call",
  "params": {
    "name": "text-compare",
    "arguments": {
      "first_text": "First text to compare",
      "second_text": "Second text to compare",
      "apiKey": "your-winston-ai-api-key"
    }
  }
}'

Note: Replace your-winston-ai-api-key with your actual Winston AI API key. You can get one at https://dev.gowinston.ai.

📋 API Reference# AI Text Detection

{
  "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."
}

AI Image Detection

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

Plagiarism Detection

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

Text Comparison

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

🤝 Contributing

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

📄 License

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

🔗 Links

⭐ Support

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


Made with ❤️ by the Winston AI Team

Server Config

{
  "mcpServers": {
    "model-context-protocol-(mcp)-server-for-winston-ai---the-most-accurate-ai-detector.-server": {
      "command": "npx",
      "args": [
        "model-context-protocol-(mcp)-server-for-winston-ai---the-most-accurate-ai-detector."
      ]
    }
  }
}

Links & Status

Repository: github.com
Hosted: Yes
Global: Yes
Official: Yes

Project Info

Hosted Featured
Created At: Aug 07, 2025
Updated At: Aug 07, 2025
Author: Winston AI Team
Category: AI Detection
License: MIT License
Tags:
development documentation public