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azmartone67

DC Hub — Data Center & Energy Intelligence

Nearby Infrastructure

get_infrastructure
Read-onlyIdempotent

Find nearby energy infrastructure (substations, transmission lines, gas pipelines, power plants) within a radius of any location, with distances and capacities.

Instructions

Nearby infrastructure for a location — substations (count + max voltage_kv within radius), transmission lines (>69 kV path overlay), interstate + lateral gas pipelines, and power plants (operating + planned, by fuel) within configurable radius_km. Returns distance + capacity for each, joined to HIFLD/EIA. Try: get_infrastructure lat=33.45 lon=-112.07 radius_km=25. Returns raw nearby assets; do NOT use for a single scored site-suitability verdict (use analyze_site).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoCenter latitude in decimal degrees (-90 to 90, required), e.g. 33.45
lngNoAlias for lon — either name works
lonNoCenter longitude in decimal degrees (-180 to 180, required), e.g. -112.07
layerNoOptional single asset layer to return, e.g. substations, transmission, pipelines, power_plants
limitNoMax results to return (1-500; default varies by tool)
latitudeNoAlias for lat — either name works
longitudeNoAlias for lon — either name works
radius_kmNoSearch radius in kilometers around the point, e.g. 25
min_voltage_kvNoOnly include transmission/substations at or above this voltage in kV, e.g. 69

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 the tool read-only, idempotent, and non-destructive. The description adds substantial behavioral context: it returns counts, voltage levels, distance and capacity, joins to HIFLD/EIA, and applies a 69 kV threshold. This goes well beyond the annotation safety profile, providing transparent expectations for output content and data sources.

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 compact yet information-dense: it opens with a clear purpose, lists asset types, states the return format and data sources, includes a usable example, and closes with an explicit exclusion. Every sentence contributes value with no redundancy or filler.

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 an output schema exists, the description does not need to enumerate return fields. It covers purpose, usage, data sources, example, and exclusion, which fully contextualizes the tool. The inclusion of the 'do NOT use' caveat with the alternative tool makes it contextually complete for an agent deciding between tools.

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 meaning by explaining that radius_km is configurable, that voltage thresholds (>69 kV) relate to transmission lines, and by giving an example use of lat/lon/radius_km. It does not detail every parameter but complements the detailed schema descriptions with contextual semantics.

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 opens with 'Nearby infrastructure for a location' and enumerates precise asset categories (substations, transmission lines, gas pipelines, power plants), distinguishing it from a scored suitability tool. It also provides a concrete example invocation, making the tool's function unambiguous and clearly differentiated from siblings like 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?

The description explicitly states when to use the tool (for raw nearby assets) and when not to use it ('do NOT use for a single scored site-suitability verdict'), directly naming the alternative analyze_site. It also includes a worked example with lat/lon/radius, giving clear practical 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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