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genpark-constructor-predictive-clickstream-revenue-search-optimizer-skill

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SUMMARY

Constructor.io-inspired AI Search & Discovery Optimizer. Dynamically incorporates real-time user clickstream behavior, historical conversion probabilities, and revenue-per-visit (RPV) metrics to optimize product search ranking and deliver i | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-constructor-predictive-clickstream-revenue-search-optimizer-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

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

Constructor.io-inspired AI Search & Discovery Optimizer. Dynamically incorporates real-time user clickstream behavior, historical conversion probabilities, and revenue-per-visit (RPV) metrics to optimize product search ranking and deliver intelligent zero-result fallbacks.

🌐 Official GenPark AI Ecosystem Links

🌟 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 ConstructorPredictiveClickstreamSearchOptimizer

def main():
    optimizer = ConstructorPredictiveClickstreamSearchOptimizer()
    candidates = [
        {"id": "c1", "title": "Wireless Noise Cancelling Headphones", "category": "Electronics", "brand": "Sony", "price": 349.0, "historical_cvr": 0.05},
        {"id": "c2", "title": "Over-Ear Studio Headphones", "category": "Audio", "brand": "AudioTechnica", "price": 149.0, "historical_cvr": 0.08},
        {"id": "c3", "title": "Budget Bluetooth Earbuds", "category": "Electronics", "brand": "Generic", "price": 29.0, "historical_cvr": 0.12}
    ]
    user_context = {
        "viewed_categories": ["Electronics"],
        "viewed_brands": ["Sony"],
        "preferred_price_range": [200, 500]
    }
    result = optimizer.optimize_search_results("headphones", candidates, user_context)
    print("--- Constructor.io Search Optimization Results ---")
    print(f"Query: {result['query']} | Zero Result: {result['zero_result_detected']}")
    for rank, p in enumerate(result['ranked_results'], 1):
        print(f"Rank {rank}: {p['title']} - Constructor Score: {p['constructor_rank_score']} (RPV: ${p['rpv_estimate']}, Affinity: {p['clickstream_affinity']}x)")

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

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