genpark-agentic-commerce-counterfeit-product-review-synthesizer-skill

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

AI Agentic Commerce Review Hijacking & Authenticity Sentinel. Audits product review streams across review-velocity clustering, sentiment dissonance, repetitive incentivized phrases, and review hijacking to output verified true customer ratings.

🌐 Official GenPark AI Ecosystem Links

• GenPark Official Website: https://www.genpark.ai

• LinkedIn Official Page: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Related MCP server: Amazon All-in-One Scrape MCP

🌟 Key Features

• 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.

• Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.

• Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.

• Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import AgenticCommerceReviewAuthenticitySentinel

def main():
    sentinel = AgenticCommerceReviewAuthenticitySentinel()
    stream = [
        {"rating": 5, "text": "Amazing product! Item arrived fast great seller", "verified_purchase": False},
        {"rating": 4, "text": "Works as described, sturdy aluminium construction.", "verified_purchase": True},
        {"rating": 2, "text": "Cable started fraying after two weeks of normal desk use.", "verified_purchase": True}
    ]
    res = sentinel.audit_product_reviews("Magnetic Desk Charger", stream)
    print("--- E-Commerce Review Authenticity Sentinel ---")
    print(f"Product: {res['product_title']} | Grade: {res['authenticity_grade']}")
    print(f"Raw Rating: {res['raw_average_rating']} / 5.0 -> Authentic Adjusted: {res['adjusted_authentic_rating']} / 5.0")
    print(f"Authenticity Index: {res['authenticity_index']}%")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI. Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550