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azmartone67

DC Hub — Data Center & Energy Intelligence

Water Risk

get_water_risk
Read-onlyIdempotent

Assess water risk for any US site. Get water stress score, drought category, and cooling-water outlook to determine if evaporative cooling is viable for a data-center build.

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.
Behavior5/5

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

Description adds context beyond annotations: free tier, one round trip, no planner overhead, return fields, data source (USGS + US Drought Monitor), and precision difference between lat/lon and state. No contradiction with read-only/idempotent hints.

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?

Description is long but well-organized, front-loaded with the decision of when to use the tool. Includes example, params, return shape, and exclusions. Minor redundancy (free tier mentioned twice) but overall each sentence earns its place.

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?

Despite the county param issue, the description is highly complete: provides usage context, comparisons to alternatives, example, parameter guidance, return schema summary, and data sources. Given the tool's moderate complexity and rich output schema, it leaves little unresolved.

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

Parameters2/5

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

Schema has 100% description coverage for its 6 properties, but the description introduces 'county' as a valid parameter, which is not in the schema. This is misleading and could cause the agent to pass an invalid parameter. It also says 'lat+lon' as a pair, which is acceptable but doesn't clarify mutual exclusivity with state.

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 gets the water risk factor for a US site, with a specific verb ('get') and resource. It distinguishes itself from siblings by noting it handles the WATER factor alone versus combined site verdicts via execute_plan or analyze_site.

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?

Explicitly says when to use (water factor alone, cooling-water sustainability screening) and when not to (use get_infrastructure for physical infrastructure, analyze_site for multi-factor verdict). Names alternatives directly and provides a concrete example.

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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