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., "@PersonalAgentLayoverItinerarySynthesizer I have a 5-hour layover at AMS—can I safely explore the city?"
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)
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.
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: sg-mobility-mcp
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 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()
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
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