Developers
Connect AI to Global Disaster & Impact Intelligence.
Give AI applications access to real-time hazards, historical catastrophes, geographic intelligence, and the context behind major events.
AI applications are transforming how organizations research, monitor, and respond to disasters. But the quality of their insights depends on the intelligence they can access.
DisasterAWARE enables developers to connect AI applications with structured disaster and severe weather intelligence through Model Context Protocol (MCP) servers and complementary enterprise APIs.
From exploring decades of historical catastrophe activity to investigating developing hazards, DisasterAWARE provides a foundation for AI-powered risk analysis, event research, geographic exposure assessment, and operational decision support.
Built on Pacific Disaster Center technology and enriched global disaster intelligence, our integrations help developers move beyond general-purpose AI knowledge toward answers grounded in specialized hazard data.
Connect your AI. Access the intelligence. Build more informed applications.
Two Intelligence Foundations. One AI-Native Integration Approach.
Historical Catastrophe Intelligence MCP Server
Ask Questions Across Decades of Disaster History.
DisasterAWARE's historical intelligence provides AI applications with access to an enriched repository of approximately 680,000 disaster events across more than 30 hazard types.
Through supported MCP capabilities, developers can enable AI applications to query and investigate historical catastrophe information without building every analytical workflow around custom API calls.
Explore historical intelligence involving:
The ability to answer specific questions depends on the MCP tools, geographic coverage, historical records, and query operations available in the connected service.
- •Earthquakes: Magnitude, depth, geographic shaking intensity, and available PGA footprints.
- •Hurricanes & Tropical Cyclones: Historical tracks, storm intensity, wind radii, and event evolution.
- •Severe Convective Storms: Hail swaths, estimated hail sizes, damaging wind events, and tornado tracks.
- •Wildfires: Historical incidents, geographic footprints, and available event progression.
- •Floods: Historical flood events, geographic inundation information, and available severity characteristics.
- •Other Hazards: Additional disaster types and event records represented within DisasterAWARE's historical database.
- •Associated Impacts: Available information about fatalities, injuries, displacement, damage, and other reported consequences.
- •"What significant earthquakes have occurred near this location over the past 30 years?"
- •"Which historical hurricanes passed within 100 kilometers of these facilities?"
- •"What major hail events affected this region during the past decade?"
- •"How has wildfire activity changed in this geographic area?"
- •"What historical disasters have caused significant reported impacts in this country?"
- Example questions an AI application could investigate:
Featured Capability: AI-Accessible Historical Disaster Intelligence
Developers can connect supported historical disaster intelligence to AI workflows for event discovery, research, geographic analysis, and catastrophe investigation.
Live Disaster Intelligence MCP Server
Connect AI Applications to Developing Global Hazards.
Disasters are constantly evolving. Static training data cannot reliably describe what is happening right now.
DisasterAWARE's live intelligence integration approach enables supported AI applications to retrieve current hazard information from its continuously updated event intelligence environment.
Depending on the available MCP tools and connected datasets, applications can investigate:
Active Natural Disasters
Earthquakes, wildfires, floods, tropical cyclones, and other supported natural hazards.
Severe Weather
Available warnings, storm reports, hail intelligence, damaging wind information, and other meteorological threats.
Geographic Hazard Locations
Event coordinates, warning areas, available hazard footprints, and geographic context.
Event Characteristics
Available severity, timing, source, classification, and supporting hazard attributes.
Developing Situations
Updated information about active incidents as new observations and reports become available.
Available Exposure Context
Geographic information that can help identify potentially affected locations when supported by the connected tools and data.
Live intelligence availability and query functionality depend on the supported MCP integration and the underlying data sources.
- •"What significant natural disasters are currently affecting Japan?"
- •"Are there active wildfire incidents near these facilities?"
- •"What severe weather hazards have been reported in this region?"
- •"Which recent earthquakes have produced significant ground shaking?"
- •"What active hazards should an operations team investigate today?"
- Example questions an AI application could investigate:
Featured Capability: Current Hazard Awareness for AI
Enable AI applications to retrieve supported live disaster information rather than relying solely on static model knowledge.
AI-Native Geographic & Exposure Intelligence
Make Disaster Intelligence Relevant to Locations That Matter.
A disaster event becomes more actionable when an application can relate it to a specific location, portfolio, facility, or geographic area.
DisasterAWARE connects hazard intelligence with geographic information, enabling developers to build applications that investigate the relationship between events and important locations.
Potential applications include:
- •Searching historical disaster activity around geographic coordinates.
- •Investigating available hazard footprints and intensity information.
- •Comparing historical catastrophe activity across locations.
- •Connecting events with customer-supplied property or facility information.
- •Exploring geographic exposure to supported hazards.
- •Incorporating available infrastructure, population, and vulnerability context.
- •Linking significant events with reported impacts.
From Hazard Data to Location-Aware Intelligence
An AI application could combine customer-provided asset locations with supported DisasterAWARE event and geographic data to help identify relevant hazards.
The necessary geographic operations, exposure calculations, and asset matching may be performed through supported DisasterAWARE tools, traditional APIs, or the developer's own application.
Connected Historical, Live & Impact Intelligence
Give AI More Context About What Happened—and What It Could Mean.
DisasterAWARE's long-term intelligence strategy connects historical events, current hazards, geographic footprints, exposed assets, and associated consequences.
This creates a foundation for AI applications that move beyond simple event retrieval.
Historical Context
Understand what has happened previously in a location or region.
Current Conditions
Retrieve supported information about developing disasters and severe weather.
Geographic Relevance
Connect hazard locations and footprints with important assets or geographic areas.
Associated Impacts
Incorporate available reports of casualties, displacement, damage, and other consequences.
Expanding Impact Relationships
As DisasterAWARE expands its event-associated impact repository, additional information about insured losses, infrastructure damage, outages, and operational disruptions can enrich future AI applications.
Future Decision Intelligence
Our longer-term vision is to help AI applications answer increasingly sophisticated questions:
"What happened?"
"What was exposed?"
"What consequences have been reported?"
"What matters most?"
"What actions should be considered next?"
These more advanced decision-support capabilities represent the direction of DisasterAWARE's evolving intelligence platform rather than a guarantee of functionality available in every current MCP integration.
Why Build With Disasteraware Mcp?
Specialized Disaster Intelligence for AI Applications.
Purpose-Built for Disaster & Hazard Intelligence
Access specialized catastrophe information rather than depending exclusively on general-purpose AI knowledge.
Historical & Live Intelligence
Support applications that investigate historical disaster activity and retrieve current hazard information through the appropriate integrations.
Geographic Depth
Incorporate available event coordinates, tracks, swaths, footprints, intensity surfaces, and geographic context.
Enriched Event Information
Use structured hazard intelligence that connects available event attributes, supporting observations, geographic information, and associated impacts.
Multi-Hazard Coverage
Work across earthquakes, hurricanes, severe convective storms, wildfires, floods, and additional supported hazard types.
Built on Trusted Disaster Science
Leverage intelligence grounded in Pacific Disaster Center technology, authoritative scientific sources, and established disaster management expertise.
Designed for Enterprise Integration
Combine MCP-enabled workflows with DisasterAWARE's traditional APIs, webhooks, historical data delivery, and geospatial services.
How Mcp Integration Works
Connect Your AI Application to DisasterAWARE Intelligence.
Model Context Protocol provides a standardized way for compatible AI applications to discover and invoke tools made available by connected services.
DisasterAWARE's MCP integrations are designed to make supported disaster intelligence accessible within AI-powered workflows.
Connect — Configure Your MCP Client
Connect a compatible MCP client or AI application to the appropriate DisasterAWARE MCP service using the provided connection and authentication instructions.
Discover — Access Available Intelligence Tools
Your application discovers the supported tools exposed by the connected MCP server.
These tools define the operations and datasets available to the application.
Query — Retrieve Relevant Disaster Information
The AI application invokes supported tools to retrieve historical catastrophe records, live hazard information, or other available intelligence.
Analyze — Incorporate Results into Your Workflow
Your application uses the returned structured information to support research, geographic analysis, summaries, or other user-facing functionality.
The AI model and application remain responsible for interpreting the results, handling uncertainty, and presenting appropriate source and data-quality context.
Featured Workflow: Investigating Historical & Current Disaster Exposure
Imagine a risk intelligence platform that helps users investigate disaster activity around a portfolio of facilities.
Step 1 — User Question
"Which of our facilities have experienced significant disaster activity historically, and are any facing active hazards today?"
Step 2 — Historical Intelligence
The application queries supported historical disaster tools for events near the relevant facility locations.
Step 3 — Live Intelligence
The application retrieves supported active hazard information for those locations.
Step 4 — Geographic Analysis
Available event locations and footprints are compared with the supplied facilities using supported geographic operations.
Step 5 — AI-Powered Summary
The application presents relevant events, geographic context, and available severity information, while distinguishing historical exposure from current threats.
The result: A more informed disaster intelligence workflow that connects AI with specialized historical and live hazard information.
Mcp Vs. Rest Apis
Choose the Integration Approach That Fits Your Application.
MCP servers and traditional APIs serve complementary purposes.
Neither replaces the other in every use case.
MCP Servers
Best suited for AI-native applications and tool-driven workflows.
MCP enables compatible AI applications to discover and invoke supported disaster intelligence tools.
Potential advantages include:
- •Standardized tool integration with compatible AI clients.
- •AI-assisted exploration of supported disaster datasets.
- •Natural-language-driven analytical workflows.
- •Flexible investigation across available intelligence tools.
- •Reduced need to build a separate custom tool interface for every AI workflow.
REST APIs & Webhooks
Best suited for deterministic application integrations and operational data pipelines.
DisasterAWARE's enterprise APIs provide structured access to supported hazard information, while webhooks can deliver hazard updates directly to configured customer endpoints.
Potential advantages include:
- •Direct application-to-application integration.
- •Programmatic event retrieval and processing.
- •Webhook-driven event delivery.
- •Predictable request and response handling.
- •Integration with existing enterprise systems and workflows.
Historical Bulk Data
Best suited for large-scale analytical processing and data engineering.
Supported historical catastrophe datasets can be delivered through bulk formats such as Parquet via S3.
This approach supports data warehouses, catastrophe modeling pipelines, large-scale research, and analytical applications.
Geospatial Services
Best suited for mapping and spatial applications.
Supported raster, vector, and other geographic intelligence services enable developers to incorporate hazard and supporting geospatial information into mapping and GIS environments.
Our Recommendation
Use MCP for AI-native interaction with supported intelligence tools, REST APIs and webhooks for operational integrations, bulk delivery for large historical datasets, and geospatial services for mapping and spatial analysis.
These approaches can also be combined within a single application.
Nine Ways Developers Can Use Disasteraware Mcp
Build AI Applications Grounded in Disaster Intelligence.
AI-Powered Risk Intelligence Assistants
Build assistants that retrieve supported historical and live hazard information for geographic locations and operational areas.
Insurance & Reinsurance Research
Enable AI-assisted investigation of historical catastrophes, geographic footprints, storm intensity, and available reported impacts.
Catastrophe Event Investigation
Explore significant historical events, compare available hazard characteristics, and retrieve supporting geographic intelligence.
Property & Portfolio Risk Analysis
Connect customer-provided property locations with supported historical disaster records and geographic hazard information.
Supply Chain Risk Intelligence
Investigate historical and current disasters near suppliers, manufacturing facilities, distribution centers, and transportation networks.
Corporate Security & GSOC Applications
Support AI-assisted awareness of natural disasters, severe weather, and supported physical threats affecting operational locations.
Business Continuity & Resilience
Build applications that retrieve hazard information to support incident awareness, preparedness, and post-event review.
Disaster Research & Geospatial Analytics
Use AI-assisted tools to explore disaster histories, event characteristics, geographic patterns, and supporting scientific information.
AI-Native Decision Intelligence
Develop applications that combine available hazard intelligence, geographic exposure, and reported impacts to support increasingly sophisticated analytical and decision-support workflows.
Developer Resources & Related Capabilities
Everything You Need to Start Building.
Historical Intelligence MCP
Explore the available historical catastrophe intelligence tools, supported query operations, and integration requirements.
Live Intelligence MCP
Learn about available live disaster intelligence capabilities, supported tools, and connection requirements.
API Documentation
Explore the production DisasterAWARE Enterprise V2 API, including supported hazard endpoints, response structures, and webhook delivery.
Getting Started & Tutorials
Follow integration guidance for authentication, initial queries, data retrieval, and supported developer workflows.
Sample API Responses
Review representative hazard response structures, geographic fields, and available event intelligence.
Data Samples
Preview supported historical catastrophe datasets, geographic footprints, and geospatial intelligence layers.
Related Data Capabilities
Historical Disaster Database
Explore approximately 680,000 enriched historical events across more than 30 hazard types.
Observed Hazard Intelligence
Access supported observations and geographic evidence of hail, damaging winds, tornadoes, wildfires, floods, and other hazards.
Exposure & Impact Intelligence
Connect disaster events with geographic exposure and available reported consequences.
Geospatial Data Layers
Explore more than 300 geographic intelligence layers spanning hazards, weather, population, vulnerability, and infrastructure.
Give Your AI Applications a Deeper Understanding of Disasters.
AI applications are only as useful as the intelligence they can access. DisasterAWARE connects AI developers with specialized historical catastrophe information, live hazard intelligence, geographic context, and available associated impacts. Through MCP servers, enterprise APIs, webhooks, bulk datasets, and geospatial services, developers can build applications that investigate disaster history, monitor developing threats, and better understand geographic exposure. Our long-term vision is to make connected disaster and impact intelligence increasingly accessible to AI systems—helping applications progress from answering what happened toward understanding what matters and supporting more informed decisions.