Every insurer evaluating property risk today is running the same math: more perils, more models, more data sources, and not enough hours in the day to reconcile them. In any insurer’s portfolio, most properties face some degree of risk from multiple hazards; a single home can easily be vulnerable to hail, flooding, and wildfire all at once. While insurance technology providers have developed sophisticated predictive and probabilistic models to assess these risks, getting a unified, 360-degree view of a property’s full risk profile hasn’t historically been easy.

Imagine evaluating a property without having to navigate separate hazard models, cross-reference disparate datasets, or translate conflicting risk scores. What if, instead of viewing risks in silos, you could see the entire picture—instantly, intuitively, and all in one place?

Enter Model Context Protocols (MCPs)—software standards that connect datasets within complex solutions into a single, unified AI-native framework. By giving AI agents a structured way to absorb and translate complex information to users, MCPs allow carriers to query their entire footprint of hazard and property risk data simultaneously.

Cotality’s new MCP servers allow a user’s AI agents to connect directly to our hazard risk models. This setup allows underwriters to interact with all their risk models at once through standard conversational AI interfaces to uncover deep, multi-layered property insights on demand for any address in question.

For example, when an insurer points its AI agent to Cotality’s MCP environment, it can pose a single prompt to access the entire suite of Cotality risk models. With this access, the AI agent has the opportunity to deliver an aggregated risk profile for a property.

While this all depends on the AI agent, and Cotality doesn’t control the user’s agent of choice, this does open a world of possibilities for carriers in evaluating property- and portfolio-level vulnerability to hurricanes, flooding, severe storms, hail, and local crime—all in one unified response.

What are MCPs and how do they work?

Model Context Protocols are sets of software standards that describe complex data to AI models in a way that these solutions can understand, reason through, and translate to human users.

An MCP is like a universal USB port for artificial intelligence. By eliminating the need for developers to write custom code for every database or hazard tool, an MCP creates a standardized environment that allows an AI to plug directly into complex data solutions, allowing users to interact with tools using plain language.

In simple terms, the MCP is the vehicle that connects the AI to the data solution, giving AI the means and language to pull data seamlessly from the underlying system. With an MCP environment, hazard risk solution users can interact with risk data across any of their models in new ways, all through the AI tool that they like to interact with every day.

When an AI agent is connected to hazard tools through MCP environments, users can:

• Query multiple endpoints at once: Pull data across multiple categories and hazard models simultaneously.
• Understand context between related information: Analyze how individual property characteristics (like roof age) interact with environmental threats (like hurricane-force winds).
• Process natural language: Execute follow-up queries and synthesize complex analytical results in plain English, operating like a conversational assistant.

Expanding solutions means a growing need for MCP environments

Seemingly every day, the market develops more risk models to evaluate different perils and nuanced weather events. And while having access to all this rich data becomes a huge competitive advantage for underwriters, the need to jump between separate portals or API endpoints creates a major operational bottleneck.

Evaluating every relevant risk on a property can quickly become too time-consuming for underwriters working at scale.

MCPs solve this fragmentation problem directly by giving AI direct access to a carrier’s entire risk ecosystem in a language native to the AI tool. These environments allow users to run complex, multi-step queries in plain English—all powered seamlessly by the AI agents they use for everything else.

This connection allows users to ask follow-up questions and analyze complex interactions, which is especially valuable for professionals with limited meteorological or peril knowledge.

The Cotality difference

Any time you connect AI with data technology, the output is only as reliable as the underpinning information. The true power of Cotality’s MCP environment stems from the proprietary, high-quality data that forms its foundation. Cotality fuels risk solutions with accurate, granular property intelligence and broad, highly detailed peril coverage that analyzes historical and predictive patterns.

By pairing Cotality’s highly accurate hazard models and comprehensive property characteristics with an open MCP architecture, carriers get the best of both worlds: unparalleled risk and property data alongside the most intuitive, reliable way to interact with that intelligence.

“Our goal is to move underwriters from being data gatherers, often dependent on other professionals, to risk interpreters,” says Tim Taylor, Senior Director of Product Management at Cotality. “By unifying access to our comprehensive hazard data through MCP, we are empowering them to make faster, more holistic risk decisions.”

By pairing Cotality’s highly accurate hazard models and comprehensive property characteristics with an open MCP architecture, carriers get the best of both worlds: unparalleled property risk data and the simplest, most intuitive way to interact with it.

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