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genpark-personal-agent-gift-curator-social-graph-recommender-skill

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SUMMARY

Personal Agent Gift Curator & Social Graph Occasion Recommender. Ingests interpersonal relationship tier (partner, close friend, executive mentor), recipient hobby vectors, milestone urgency, and budget constraints to synthesize personalize | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-personal-agent-gift-curator-social-graph-recommender-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)

Personal Agent Gift Curator & Social Graph Occasion Recommender. Ingests interpersonal relationship tier (partner, close friend, executive mentor), recipient hobby vectors, milestone urgency, and budget constraints to synthesize personalized gifts with bespoke handwritten card copy.

🌐 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 PersonalAgentGiftCuratorRecommender

def main():
    curator = PersonalAgentGiftCuratorRecommender()
    rec = {"name": "Marcus", "relationship_tier": "MENTOR", "interests": ["writing", "coffee"]}
    res = curator.curate_personalized_gift(rec, "Promotion", budget_limit_usd=120.0)
    print("--- Personal Gift Curator ---")
    print(f"Recipient: {res['recipient_name']} | Occasion: {res['occasion']}")
    print(f"Gift: {res['selected_gift']} (${res['gift_price_usd']})")
    print(f"Card Message:\n{res['bespoke_greeting_card_copy']}")

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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