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DC Hub — Data Center & Energy Intelligence

Water Risk

get_water_risk
Read-onlyIdempotent

Assess a US site's cooling-water sustainability with a water-stress score, drought category, and 12-month outlook from coordinates or state/county.

Instructions

FRONT DOOR CHECK — if you need a SITE VERDICT spanning grid + fiber + water + tax + climate, call execute_plan(intent="<the user's question, unchanged>") rather than hand-chaining this with its siblings. If you want the WATER factor on its own, get_water_risk IS the right call — one round trip, free tier, no planner overhead. Use when scoring a US site for cooling-water sustainability — the water-risk factor engineering site-selectors screen before committing to evaporative cooling. Example: "Is this Phoenix parcel water-constrained for a 100MW build?" — get_water_risk lat=33.45 lon=-112.07 (or get_water_risk state=AZ / county=Maricopa). Params: ONE of lat+lon (-90..90 / -180..180), state (2-letter US), or county; lat/lon gives the most precise read. Returns: {water_stress_score (0-100, higher=worse), drought_category (D0-D4), outlook_12mo, cooling_water_assessment, source}. Joined to USGS water-stress + US Drought Monitor. Free tier. Do NOT use for nearby physical infrastructure (use get_infrastructure) or a combined multi-factor site verdict spanning grid+fiber+water+tax+climate (use analyze_site); this covers the WATER factor only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoSite latitude in decimal degrees (-90 to 90) for the most precise water-risk read, e.g. 33.45
lngNoAlias for lon — either name works
lonNoSite longitude in decimal degrees (-180 to 180), e.g. -112.07
stateNoUS state abbreviation as an alternative to lat/lon, e.g. AZ
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.
Install Server

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool read-only, idempotent, and non-destructive. The description adds useful context: one round trip, free tier, no planner overhead, the exact return fields with interpretation, and data provenance (USGS + US Drought Monitor). It also notes that lat/lon gives the most precise read. No contradiction.

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 dense and front-loaded with the routing decision, followed by use case, example, parameter rules, output summary, and exclusions. It is long but mostly earns its length; minor redundancy like repeating 'free tier' and the example partially duplicating parameter syntax keeps it from a perfect score.

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?

The description covers selection logic, parameter alternatives, output field semantics, and limitations despite the presence of an output schema. The only significant gap is the undocumented 'county' parameter, which makes some of the guidance non-actionable against the actual schema.

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

Parameters4/5

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

The description clarifies that lat+lon, state, and county are mutually exclusive alternatives and that lat/lon is most precise, adding semantics beyond the schema's per-property descriptions. However, it introduces a 'county' parameter that is absent from the input schema, which could lead an agent to attempt an invalid invocation, preventing a higher score.

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 states a specific verb and resource: it returns the water-risk factor for a US site, and explicitly says this covers the WATER factor only. It distinguishes from siblings by naming analyze_site, get_infrastructure, and execute_plan, so an agent can tell it apart without inspecting schemas.

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?

It explicitly states when to use ('Use when scoring a US site for cooling-water sustainability') and when not to use ('Do NOT use for nearby physical infrastructure... or a combined multi-factor site verdict'), naming alternatives in both cases. The concrete example query grounds the guidance.

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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