Winston AI MCP Server — AI & Plagiarism Detection

Overview

Winston AI MCP Server is a lightweight API service designed to detect AI-generated content (text and images) and flag potential plagiarism. It implements the Model Context Protocol (MCP) style endpoints for integrating content integrity checks into publishing pipelines, learning platforms, moderation systems, and editorial workflows. The service returns detection scores, verdicts, and contextual metadata that help automation systems and human reviewers make informed decisions.

For developers, the MCP server is useful because it centralizes content analysis into a single, programmatic interface. You can submit text or image inputs and receive structured results (confidence scores, fingerprints, metadata) that are easy to process, log, or forward to other systems (webhooks, queues, dashboards). Typical integration points include content ingestion, pre-publish checks, API middleware, and automated moderation.

Features

• Detect AI-generated text with a confidence score and optional explanations
• Detect AI-generated or manipulated images (image attribution/fingerprint)
• Plagiarism / similarity checks against known content or fingerprints
• Structured, machine-readable responses suitable for automation
• Simple HTTP API compatible with common MCP client patterns
• Configurable thresholds and webhook/notification hooks for alerts
• Docker-ready for easy deployment and local testing

Installation / Configuration

Clone the repository and run locally or in Docker. The examples below assume a typical Node-based project layout; adapt commands to your environment.

Clone repository:

git clone https://github.com/gowinston-ai/winston-ai-mcp-server.git
cd winston-ai-mcp-server

Run with Docker Compose (recommended for quick start):

# Start the service and dependent components
docker compose up --build

Run locally (Node/npm example):

# Install dependencies and start
npm install
npm run build
npm start

Environment configuration (.env example):

# Server
PORT=8080

# API / auth
API_KEY=your-api-key-here

# Detection tuning
TEXT_CONFIDENCE_THRESHOLD=0.7
IMAGE_CONFIDENCE_THRESHOLD=0.6

# Storage / optional DB
DATABASE_URL=postgres://user:pass@localhost:5432/winston

Notes:

• Secure the API with an API key or other auth mechanism in production.
• Tune confidence thresholds to match your risk tolerance and workflow.
• If you use persistence (database or object storage), configure DATABASE_URL and any storage credentials.

Available Resources

The MCP server exposes a small set of HTTP endpoints for detection, health checks, and metadata. Example endpoints (adapt to your deployed base path):

Typical request/response shapes:

POST /detect/text (request)

{
  "id": "item-123",
  "content": "Submitted text to analyze",
  "source": "editor-ui",
  "metadata": { "userId": "u-456" }
}

POST /detect/text (response)

{
  "id": "item-123",
  "verdict": "ai-generated",
  "confidence": 0.92,
  "plagiarismMatch": { "score": 0.03, "matchedId": null },
  "explanation": "High probability of synthetic phrasing and token patterns",
  "fingerprint": "sha256:..."
}

POST /detect/image (request)

{
  "id": "img-789",
  "imageUrl": "https://example.com/photo.jpg",
  "metadata": {}
}

POST /detect/image (response)

{
  "id": "img-789",
  "verdict": "synthetic",
  "confidence": 0.85,
  "attribution": { "model": "diffusion-x", "confidence": 0.68 },
  "fingerprint": "sha256:..."
}

Use Cases

1. Publishing platform — Pre-publish checks:

   • When an author submits an article, send the body to /detect/text. If the confidence exceeds your threshold, flag the submission for manual review before publishing.

2. Learning management system (LMS) — Assignment integrity:

   • Scan student submissions via /detect/text and check for high similarity to known sources (plagiarismMatch). Automatically notify instructors for suspicious cases.

3. Social network moderation — Image abuse detection:

   • Run images uploaded by users through /detect/image to detect synthetic images or manipulated media and apply temporary holds for human review.

4. Search engine / crawler hygiene:

   • As part of an indexing pipeline, analyze and tag content that appears generated so ranking or filtering logic can adjust for content quality.

5. Legal / compliance — Evidence logging:

   • Store detection fingerprints and response payloads to create an audit trail for compliance reviews or takedown requests.

Getting Started Tips

• Start with conservative thresholds and collect example false positives/negatives to tune settings.
• Log full detection responses for a period to analyze patterns; this helps improve rules and reviewer training.
• Combine automated verdicts with human review for high-risk scenarios (academic dishonesty, legal content).
• Use the metadata and fingerprint fields to correlate items across systems and maintain an audit trail.

For full implementation details and any project-specific scripts, refer to the repository README and configuration files in the GitHub project.