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genpark-personal-agent-flight-layover-micro-itinerary-synthesizer-skill

mcp
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SUMMARY

Personal Agent Flight Layover Micro-Itinerary Synthesizer. Evaluates airport layover time budgets (3-8 hours), factors immigration clearance buffers, round-trip metro/taxi latencies, and curates timed sightseeing/culinary micro-tours with z | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-personal-agent-flight-layover-micro-itinerary-synthesizer-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 Flight Layover Micro-Itinerary Synthesizer. Evaluates airport layover time budgets (3-8 hours), factors immigration clearance buffers, round-trip metro/taxi latencies, and curates timed sightseeing/culinary micro-tours with zero connection risk.

🌐 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 PersonalAgentLayoverItinerarySynthesizer

def main():
    synth = PersonalAgentLayoverItinerarySynthesizer()
    res = synth.synthesize_layover_tour("AMS", 420)
    print("--- Flight Layover Concierge ---")
    print(f"Airport: {res['airport']} ({res['city']}) | Total Layover: {res['total_layover_minutes']}m")
    print(f"Net City Time: {res['net_usable_exploration_minutes']}m | Status: {res['itinerary_status']}")
    print(f"Advice: {res['safety_advice']}")
    for st in res['recommended_stops']:
        print(f"* Step {st.get('step', 1)}: {st['attraction_name']} ({st['allocated_minutes']}m)")

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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