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DC Hub — Data Center & Energy Intelligence

Get Disaster Risk

get_disaster_risk
Read-onlyIdempotent

Get disaster risk for any US lat/lon using FEMA National Risk Index: returns composite score, hazard ratings (flood, wildfire, hurricane, earthquake, etc.), and top hazards.

Instructions

Use when a user wants the natural-hazard / disaster risk for a lat/lon — flood, wildfire, hurricane, earthquake, heat, drought, tornado, etc. Grounded in the FEMA National Risk Index (NRI), the authoritative US county-level hazard dataset (live query, never estimated; points outside US NRI coverage return coverage=unavailable). Example: get_disaster_risk lat=33.45 lon=-112.07. Returns {disaster_risk:{composite_score (0-100, higher=worse), rating (Very Low..Very High), national_percentile}, hazards:{Wildfire, Hurricane, Earthquake, Heat Wave, ...: rating}, top_hazards:[{hazard, rating}], coverage (validated|unavailable), source, caveats}. County-level resolution. For chronic water stress use get_water_risk; for one blended site verdict use get_composite_site_score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoSite latitude in decimal degrees (-90 to 90, required), e.g. 33.45
lngNoAlias for lon — either name works
lonNoSite longitude in decimal degrees (-180 to 180, required), e.g. -112.07
latitudeNoAlias for lat — either name works
longitudeNoAlias for lon — either name works

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
quotaNoCaller quota state (remaining calls, tier) when available.
_entityNoPayload class discriminator (e.g. facility|market|iso_grid|queue_results|deal|report|response) — branch on this before parsing the rest.
citationNoMachine-readable citation: how to attribute DC Hub (dchub.cloud) for this payload. Normally an OBJECT {source, url, license, cite_as, retrieved_at}; a bare string is accepted and carries the attribution line itself.
provenanceNoCollection-level provenance block: {source, method, as_of, verification_counts, cite_url_template, license, cite_as}. Quote the verification level when citing.
_front_doorNoIn-band front-door hint (first workflow-entry tool of a session): call plan_query(intent) first for the ordered multi-step plan.
_return_loopNoSuggested next-session delta call (get_changes since=24h) so you pull only what changed.
site_evaluation_handoffNoPre-built follow-up calls (analyze_site / get_water_risk args) when the payload carries coordinates — an array of {tool, parameters, why} entries.
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description goes beyond by disclosing the data source (FEMA NRI), the live-query nature ('never estimated'), county-level resolution, and the coverage behavior for non-US points. This adds considerable behavioral context with no contradictions.

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

Conciseness5/5

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

The description is well-structured and front-loaded with the use case, followed by data source, example, return shape, limitations, and alternatives. Each sentence contributes unique value, and despite its length, it remains efficient for the tool's complexity.

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

Completeness5/5

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

The description covers purpose, when to use, behavior, output structure, limitations, and alternative tools. With an output schema present, it still provides a detailed return shape and caveats, making it fully self-sufficient for an agent to invoke correctly.

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?

Schema description coverage is 100%, and each parameter already includes a description and example. The description provides a usage example (lat=33.45 lon=-112.07) but adds no new semantic information beyond what the schema already contains. Baseline 3 is appropriate.

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?

Description clearly states the tool's purpose: natural-hazard/disaster risk for a lat/lon, listing specific hazard types (flood, wildfire, hurricane, etc.). It explicitly distinguishes itself from sibling tools like get_water_risk and get_composite_site_score, making the tool's scope unambiguous.

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

Usage Guidelines5/5

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

Provides an explicit 'Use when' clause, a concrete example with lat/lon values, and clearly names alternatives for related but different queries (water stress, composite site score). Also explains the coverage limitation (returns 'unavailable' outside US NRI coverage), giving clear usage context.

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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