Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AI Agentic Commerce Review Hijacking & Authenticity Sentinel audit these reviews for review hijacking and give adjusted authentic rating"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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: genpark-study-methodology-bias-detector-skill
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.
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()
Start standard JSON-RPC 2.0 server over stdio:
python mcp_server.pyExecute embedded test harness:
python mcp_server.py --testInspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.
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
This server cannot be deployed
Detect fake or manipulated product reviews from Amazon, Trustpilot, Google and Etsy.
Walmart & Amazon review insights: complaints, topic sentiment, new-review alerts. Pay per result.
Analyze product reviews: pain points, feature requests, sentiment and competitive gaps for sellers.
Know what you're buying: brand, white-label, price, fake-discount, review and seller checks
