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DC Hub — Data Center & Power Intelligence

by cloud.dchub

Water Risk

get_water_risk
Read-onlyIdempotent

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.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark it read-only, idempotent, and non-destructive, and the description adds further context: it is a free, one-round-trip call with no planner overhead, returns a specific JSON shape, and joins USGS water-stress and Drought Monitor data. No contradiction with annotations.

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?

Though lengthy, the description is well-structured: it front-loads the routing decision, then gives usage, an example, param guidance, return shape, and exclusions. Every sentence is load-bearing and there is no fluff.

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 routing, usage, return format, data sources, and exclusion zones, which is excellent. The only gap is the mismatch between the described 'county' option and the schema, which leaves the agent with ambiguous param semantics. Otherwise it is fully complete for a tool with an output schema and rich annotations.

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 already describes all six parameters, so the baseline is 3. The description adds useful constraints (ONE of lat+lon, state, or county; lat/lon most precise) that go beyond the schema. However, it references a 'county' parameter that is not defined in the input schema, which is misleading and could lead an agent to pass an invalid parameter.

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 this tool provides the water-risk factor for cooling-water sustainability, with a specific example and a precise verb-resource mapping. It explicitly distinguishes itself from siblings like analyze_site and get_infrastructure, so an agent can immediately tell what it does and does not do.

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?

The description gives explicit when-to-use guidance ('If you want the WATER factor on its own'), when-not-to-use guidance ('Do NOT use for nearby physical infrastructure... or a combined multi-factor site verdict'), and names the alternatives (get_infrastructure, analyze_site). It also includes a concrete example and notes the free tier and one-call efficiency.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes despite some thematic overlap, and each description includes explicit 'Do NOT use' guidance to prevent misselection. However, a few pairs like search_intelligence vs semantic_search are nearly identical in function, and the sheer number of tools increases the chance of selecting the wrong one without careful reading.

Naming Consistency4/5

The vast majority of tools follow a predictable 'get_*' prefix for data reads, and many others use verb_noun patterns (analyze_*, rank_*, save_*, set_*). There are a handful of outliers like ai_capacity_index, grid_transition_radar, and site_selection_canvas that break the pattern, but overall the conventions are consistent enough for an agent to infer meaning.

Tool Count2/5

With 82 tools, this server is extremely heavy compared to typical MCP servers (3-15 tools). While the domain is broad, many tools serve narrow sub-purposes and could be consolidated (e.g., multiple site-scoring variants, multiple grid telemetry endpoints). The count overwhelms an agent's ability to choose efficiently and feels like over-fragmentation rather than necessary granularity.

Completeness4/5

The tool surface covers the full lifecycle of data-center siting intelligence: site analysis, grid, fiber, water, climate, tax, permitting, deals, news, saved-site management, and meta-planning. Minor gaps exist (e.g., no delete or update operations for saved sites), but the core workflows are well-supported and the descriptions are comprehensive.

Resources