Modular Tooling and Adaptive Spatial MLOps: An MCP-Enabled Agentic Delivery of Natural Hazards Insights and Predictions to Multidisciplinary Stakeholders

Extreme weather events expose human and economic frailty, of which is reliant upon a stable physical environment. To minimize the societal risk incurred from these natural bouts of instability, multi-disciplinary resilience efforts, spanning science, engineering, policymaking, and more, are essential. The issue, however, arises when the needed collaborative efforts are impeded by the inability to derive findings outside of one’s own expertise or to discern the outputs and outcomes of another. Hence, this work novelly proposes a shared space solution, an adaptable, flexible and automated agentic-enabled application, to facilitate collaboration among experts and stakeholders by the provision of practical climate resilience content. The application presently affords physical and social characteristics, extreme-event risk information, interactive and downloadable maps, predictive models, and resilience-planning tools, along a modular architecture designed for the scalable incorporation of additional capabilities. Operationally, via the interface, the end-user may either directly access an instrument or select an agent to orchestrate instruments via a Model Context Protocol (MCP) tool layer, where the Large Language Model (LLM) interprets requests, selects and invokes tools, and synthesizes outputs for the end-user. Further, two implemented capabilities are highlighted in this paper. First, a flood susceptibility prediction model, running via a Machine Learning Operations (MLOps) lifecycle, is demonstrated. The automation includes the weekly consumption of new radar and sensor observations, triggering the retraining and evaluations of graph convolutional neural network and logistic models. Models and sensors are then dynamically selected based on predefined performance criteria. At the daily scale, same- and next-day precipitation forecasts are ingested, producing localized 1km x 1km predictions. A second highlight is the LLM-enabled multi-risk tool, which integrates physical, extreme-event risk, and sociodemographic information, allowing users to identify areas exhibiting combinations of vulnerabilities relevant to their particular resilience inquiries. Ultimately, by providing the stakeholder facing application, the immediate aim is to assist New Yorkers in preparing for and mitigating the consequences of extreme weather events, while providing a foundational framework that may be extended to other urban environments.