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DC Hub — Data Center & Power Intelligence

Analyze Site

analyze_site
Read-onlyIdempotent

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 or location), lon (-180 to 180, required unless candidate_id or location), location (a market NAME or metro slug instead of coordinates, e.g. location="ashburn" — resolved to that market's PUBLISHED CENTROID through the DCPI market row, with a resolved_from block naming what it resolved to; a MARKET-level read, NOT the parcel you named, and a trailing state is not stripped so "Ashburn, VA" will not resolve), 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 — returns a capacity_context block sizing that load against nearby installed generation; it deliberately does NOT move overall_score, and the block names where the load IS applied), 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 or location given), e.g. 33.45
lngNoAlias for lon — either name works
lonNoSite longitude in decimal degrees (-180 to 180; required unless candidate_id or location given), e.g. -112.07
stateNoUS state abbreviation (optional) — improves the tax-incentive lookup, e.g. AZ
mpp_payNoAutonomous payment (Stripe MPP), step 1: set true to receive a signed $0.50 payment challenge for this call instead of the free preview. No money moves — a challenge is a price quote. Humans never set this, so it does not affect the normal free/trial funnel.
latitudeNoAlias for lat — either name works
locationNoMarket NAME or metro slug instead of coordinates, e.g. "ashburn", "northern-virginia", "dallas". Resolved to that market's PUBLISHED CENTROID through the DCPI market row, and the answer carries a resolved_from block saying so. This is a MARKET-level read, not the parcel you named — pass lat/lon for a specific site. Not an alias for lat/lon: a place name is not a coordinate.
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)
mpp_credentialNoAutonomous payment (Stripe MPP), step 2: the Shared Payment Token you minted for challenges[0]. Set it here to pay $0.50 for this single call and receive the full result — no API key, no subscription, no human. One payment covers one call.

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.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description goes far beyond this by revealing important behaviors: location resolves to a published centroid, not the parcel; candidate_id ignores lat/lon and fails closed when expired; capacity_mw deliberately does not move overall_score; and the free/paid tier split is disclosed. No contradiction exists.

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 the length is justified by the tool's complexity: 14 parameters, multiple input modes, tiered returns, and sibling differentiation. It is front-loaded with the core use case and example, and every sentence adds necessary operational detail. Slightly over-long for a quick scan, but appropriately structured.

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 complex input modes, output shape, free versus paid behavior, and many sibling tools, the description covers everything an agent needs: it explains what each parameter does, what the return contains in both tiers, what to use instead, and what caveats apply. Nothing essential is missing.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds real semantics beyond the schema: the mutual exclusivity of lat/lon vs candidate_id vs location, the resolution quirk that 'Ashburn, VA' won't resolve, the meaning of capacity_context, and the default behavior of include_* booleans. This is high-value parameter explanation, not repetition.

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 a precise use case — one specific lat/lon for a full multi-factor data-center suitability read — and then distinguishes from siblings like compare_sites and find_alternatives. It names the resource, the action (analyze site), and the scenario clearly.

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 states when to use the tool versus alternatives: use it for ONE site with a full suitability read; do NOT use it to compare 2+ sites (use compare_sites) or to find matching sites (use find_alternatives). It also routes to dedicated tools for water/disaster/climate/tax reads. This is exemplary 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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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.