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DC Hub — Data Center & Energy Intelligence

Get Climate Intel

get_climate_intel
Read-onlyIdempotent

Obtain USGS seismic hazard and NOAA climate normals for a location to inform data-center structural bracing and cooling design, with data availability declared.

Instructions

Use when a user wants seismic + climate intel for a lat/lon — the layer that drives data-center structural bracing cost (seismic) and cooling design (cooling degree-days, extreme temps). Grounded STRICTLY in USGS ASCE 7 (seismic) + NOAA climate normals via ACIS; every value traces to a federal source and missing data is declared unavailable, never estimated. Example: get_climate_intel lat=33.45 lon=-112.07. Returns {seismic_hazard_usgs:{status, peak_ground_acceleration_g, ss, s1, seismic_design_category, hazard_class}, climate_normals_noaa:{status, reference_station:{id,name,distance_km}, cooling_design_metrics:{cooling_degree_days_annual, extreme_max_dry_bulb_f, extreme_max_wet_bulb_f (null if source lacks it), data_vintage}}, overall_climate_summary, data_availability, sources}. radius_km (optional, default 25) snaps to the nearest NOAA station; beyond it climate returns unavailable_exceeds_radius. Seismic is US (ASCE 7); non-US → seismic unavailable. For natural-hazard ratings use get_disaster_risk; for one blended 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
radius_kmNoMax distance (km) to snap to the nearest NOAA station (optional, default 25)

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

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses data provenance (USGS ASCE 7, NOAA via ACIS), a strict no-estimation policy, explicit unavailable states for missing data, and radius-dependent failure behavior. This gives the agent a strong mental model of side effects and edge cases.

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 long but information-dense and front-loaded with purpose and scope. Every sentence earns its place, especially the grounding, example call, return shape, and exclusions. It could be slightly more scannable with structure, but it is appropriately sized 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?

Given the output schema exists, the description still covers the main return sections, failure modes, data sources, geographic limitations, and alternatives. It is complete enough for an agent to select and invoke the tool correctly, including knowing what happens when data is missing or the site is outside the US.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful usage context beyond the schema: the concrete example invocation, the purpose of radius_km as a station-snapping distance, and the consequence of exceeding it. This lifts it above baseline but doesn't add deep format details for every 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 states a specific purpose: providing seismic and climate intel for a lat/lon, tied to structural bracing and cooling design. It clearly names the resource and distinguishes itself from related tools like get_disaster_risk and get_composite_site_score.

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 says 'Use when' with a concrete use case, and gives alternatives: 'For natural-hazard ratings use get_disaster_risk; for one blended verdict use get_composite_site_score.' It also explains scope limitations for non-US locations and radius behavior, so an agent knows when this tool is or isn't appropriate.

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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