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Stratalize Real Estate

get_climate_risk_score

Read-only

Use when pricing physical climate risk for a location — real estate acquisition, commercial property underwriting, construction site selection, or climate-related financial disclosure. Returns a composite risk score across six perils (flood, hurricane, tornado, wildfire, extreme heat, freeze) using the same risk factors embedded in FEMA's National Risk Index. Example: Miami-Dade FL — EXTREME overall, top 2% hurricane exposure, Zone AE flood designation across 40% of commercial parcels, 94 days above 95°F annually — commercial property insurance costs 3.2x national median. Source: NOAA Climate Normals, FEMA National Risk Index, USGS Natural Hazards composite.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYesUS city and state (e.g. Miami FL or Houston Texas)

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare safe read-only behavior. The description adds context beyond that by detailing the six perils, giving a rich example with specific metrics, and citing data sources. It does not contradict annotations and provides behavioral transparency about the output's nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the usage context and purpose, followed by a helpful example and sources. It is somewhat lengthy but every section serves a purpose, making it effective rather than wasteful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Since there is no output schema, the description compensates with a detailed example of the return value (EXTREME overall, hurricane percentile, flood zone, heat days). It also explains the data sources. While it lacks a precise return structure, the example sufficiently conveys what the agent can expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the parameter description, so the baseline is 3. The description adds little about the 'location' parameter beyond the example 'Miami-Dade FL', which is consistent with the schema's 'US city and state' format but not a substantive addition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Returns a composite risk score across six perils' for physical climate risk. It lists specific use cases and distinguishes itself from siblings by emphasizing the composite score built on FEMA's National Risk Index, setting it apart from tools like get_storm_event_history.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description opens with 'Use when pricing physical climate risk for a location' and lists concrete scenarios (real estate acquisition, underwriting, construction site selection). However, it does not explicitly mention alternatives or when not to use this tool, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation5/5

Each tool targets a specific real estate metric or data source, with detailed descriptions and examples that clearly differentiate them. Even the four climate-related tools serve distinct purposes: composite risk, macroeconomic losses, historical storm tally, and short-term weather scheduling risk. The only potential overlap is between get_noaa_disaster_economics and get_storm_event_history, but their descriptions clarify different use cases.

Naming Consistency5/5

All 19 tools follow the exact same 'get_' prefix with lowercase snake_case descriptive suffixes. No mixed conventions, no irregular verbs, completely predictable pattern. This makes the tool names easy to learn and reliably distinguishable.

Tool Count4/5

19 tools is on the higher end for a data retrieval server, but the domain encompasses pricing, rents, costs, debt, climate, development, and market metrics, justifying a broad catalog. The four climate tools could arguably be consolidated, but each has a distinct use case and data source, making the count reasonable for a comprehensive real estate benchmark server.

Completeness4/5

The server covers the core real estate lifecycle: acquisition (cap rates, climate risk), development (construction costs, pro forma), financing (debt benchmarks, mortgage rates), operation (property operating, tax), and market analysis (supply, rents, residential, REITs, NCREIF). Minor gaps exist such as transaction volume data or sub-market specific leasing indicators, but the coverage is extensive for benchmark-oriented use cases.

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