Skillget

genpark-agentic-flight-delay-eu261-compensation-claimant-skill

mcp
Guvenlik Denetimi
Uyari
Health Uyari
  • License — License: NOASSERTION
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 7 GitHub stars
Code Gecti
  • Code scan — Scanned 4 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested

Bu listing icin henuz AI raporu yok.

SUMMARY

Automated Flight Delay & Cancellation EU261 / UK261 Regulatory Compensation Claimant. Computes flight route Great-Circle distances via Haversine geometry, applies statutory compensation tiers (up to EUR 600), screens extraordinary circumsta | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-agentic-flight-delay-eu261-compensation-claimant-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)

Automated Flight Delay & Cancellation EU261 / UK261 Regulatory Compensation Claimant. Computes flight route Great-Circle distances via Haversine geometry, applies statutory compensation tiers (up to EUR 600), screens extraordinary circumstance defenses, and drafts legally binding claim demand notices for airline passenger relations.

🌐 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 AgenticFlightDelayEU261CompensationClaimant

def main():
    claimant = AgenticFlightDelayEU261CompensationClaimant()
    flight = {
        "booking_reference": "BA9921",
        "carrier_code": "BA",
        "origin_airport": "LHR",
        "dest_airport": "FRA",
        "delay_arrival_hours": 3.5,
        "carrier_stated_reason": "maintenance_hydraulic_check",
        "passenger_name": "Marcus Aurelius"
    }
    claim = claimant.generate_claim(flight)
    print(f"Route: {claim['route']} | Distance: {claim['distance_km']} km")
    print(f"Statutory Compensation: EUR {claim['claim_verdict']['statutory_amount_eur']}")
    print(f"Verdict: {claim['claim_verdict']['reason']}")
    print("\n--- Formal Demand Notice Draft ---")
    print(claim['formal_demand_notice'])

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

Yorumlar (0)

Sonuc bulunamadi