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azmartone67

DC Hub — Data Center & Energy Intelligence

Analyze Site

analyze_site
Read-onlyIdempotent

Get a full multi-factor data-center suitability score for a single site — power, gas, fiber, market, and risk — from one lat/lon call.

Instructions

Use when a user has ONE specific lat/lon (a parcel, a candidate site) and wants the full multi-factor data-center suitability read in one call. Example: "Score this Phoenix parcel for a 100MW build — power, gas, fiber, market & risk." — analyze_site lat=33.45 lon=-112.07 capacity_mw=100 state=AZ. Params: lat (-90 to 90, required unless candidate_id), lon (-180 to 180, required unless candidate_id), candidate_id (a cand_… from get_refined_queue — resolves coordinates from the frozen mint and ignores lat/lon), capacity_mw (target load in MW, e.g. 50-500), state (2-letter US, optional — improves the tax-incentive/context lookup), include_grid/include_risk/include_fiber (booleans, default true). Returns (full, paid): {overall_score (aka composite_score, 0-100 composite — for the integrity-first version that never imputes a missing factor, use get_composite_site_score), interpretation (verdict string, e.g. "Excellent site"), scores{power_infrastructure, gas_pipeline_access, fiber_connectivity, market_conditions, risk_resilience — each 0-100}, nearby{substations_50km, power_plants_80km, gas_pipelines_50km, facilities_100km, fiber_carriers_in_state, generation_capacity_mw, total_capacity_mw}, power_cost{industrial_cents_kwh, commercial_cents_kwh, period, basis}, fiber{connectivity_score, nearest_carrier_km, near_net_bucket, top_carriers[], single_carrier_risk}, location, citation}. FREE tier returns a REAL, citable HEADLINE — composite_score + verdict + the single top limiting factor (the lowest sub-score) + citation; the full per-factor breakdown, nearby infrastructure, power cost, fiber carriers, and the branded Site Analysis PDF (generate_site_analysis) are Pro. For dedicated water / disaster / climate / tax reads use get_water_risk / get_disaster_risk / get_climate_intel / get_tax_incentives. Do NOT use to compare 2+ sites (use compare_sites) or to find sites that match a target (use find_alternatives).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoSite latitude in decimal degrees (-90 to 90; required unless candidate_id given), e.g. 33.45
lngNoAlias for lon — either name works
lonNoSite longitude in decimal degrees (-180 to 180; required unless candidate_id given), e.g. -112.07
stateNoUS state abbreviation (optional) — improves the tax-incentive lookup, e.g. AZ
latitudeNoAlias for lat — either name works
longitudeNoAlias for lon — either name works
capacity_mwNoTarget power load for the build in megawatts (MW), e.g. 100 (typical 50-500)
candidate_idNoPREFERRED for queue survivors: a cand_… id from get_refined_queue — coordinates come from the FROZEN mint (lat/lon args are ignored; zero transcription drift; expired ids fail closed with candidate_expired). See dchub.cloud/docs/candidate-lifecycle
include_gridNoInclude grid-headroom / substation analysis (default true)
include_riskNoInclude water/drought/climate risk analysis (default true)
include_fiberNoInclude fiber-connectivity analysis (default true)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
fiberNoFiber read: {connectivity_score, nearest_carrier_km, near_net_bucket, top_carriers[], single_carrier_risk} (full payload)
quotaNoCaller quota state (remaining calls, tier) when available.
lockedNoWhich sections are Pro-locked on the free headline: {per_factor_breakdown, nearby_infrastructure, power_cost, fiber_carriers, site_analysis_report}
nearbyNoNearby infrastructure counts: {substations_50km, power_plants_80km, gas_pipelines_50km, facilities_100km, fiber_carriers_in_state, generation_capacity_mw, total_capacity_mw} (full payload)
scoresNoPer-factor breakdown (full/paid payload)
_entityNoPayload class discriminator (e.g. facility|market|iso_grid|queue_results|deal|report|response) — branch on this before parsing the rest.
previewNoHeadline preview line (free tier)
verdictNoVerdict string, e.g. "Excellent site" / BUILD-CAUTION-AVOID read
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.
locationNoEcho of the analyzed location (full payload)
power_costNoPower cost read: {industrial_cents_kwh, commercial_cents_kwh, period, basis} (full payload)
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.
overall_scoreNoAlias of composite_score on the full payload
site_headlineNotrue when this is the free citable HEADLINE (score + verdict + limiting factor)
interpretationNoVerdict prose on the full payload
composite_scoreNo0-100 composite site suitability score (free HEADLINE tier and full tier)
limiting_factorNoSingle top limiting factor (the lowest sub-score) — always present on the free headline
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 readOnlyHint=true and idempotentHint=true, and the description adds substantial behavioral context: the free vs. paid tier difference, candidate_id resolving coordinates from the frozen mint and ignoring lat/lon, expired ids failing closed, and the distinction from get_composite_site_score for a no-imputation variant. It also details the return payload structure and conditional behaviors.

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 well-structured: it opens with the decision context, gives an example, enumerates params, then covers return values and free/paid differences. Every sentence contributes useful detail; it earns its length for a tool with 11 parameters and a complex response shape.

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 tool's complexity, the description is exceptionally complete: it covers the full return object, free vs. paid limitations, alternative tools by endpoint, candidate lifecycle behavior, and even points to integration docs. The output schema is also noted as present, and the description complements it rather than merely repeating it.

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 description coverage is 100%, and the description adds useful semantics beyond the schema: lat/lon are 'required unless candidate_id', candidate_id 'resolves coordinates from the frozen mint and ignores lat/lon', include_* booleans 'default true', and capacity_mw has an example range '50-500'. This exceeds the baseline of 3 for full schema coverage, though not every alias (lng/latitude/longitude) is repeated in the description.

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 by stating the exact use case: 'Use when a user has ONE specific lat/lon... wants the full multi-factor data-center suitability read in one call.' It names the verb (analyze) and resource (site) and clearly distinguishes from sibling tools like compare_sites and find_alternatives through explicit 'Do NOT use' guidance.

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?

Provides explicit when-to-use guidance with a concrete example, and also tells when not to use it: 'Do NOT use to compare 2+ sites (use compare_sites) or to find sites that match a target (use find_alternatives).' It also recommends dedicated tools for water/disaster/climate/tax reads, making the decision boundary very clear.

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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