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DC Hub — Data Center & Power Intelligence

Grid Scoreboard

get_grid_scoreboard
Read-onlyIdempotent

GLOBAL grid scoreboard — 9 US grid operators (PJM, ERCOT, CAISO, MISO, SPP, NYISO, ISO-NE, BPA, TVA) + Great Britain (NESO) + the European bidding zones (Germany, France, Netherlands, Italy/Milan, Spain, Poland, Switzerland, Portugal, the Nordics + Central/Eastern Europe — via ENTSO-E; the exact live-vs-configured count is in counts_basis.eu_zones_live / eu_zones_configured, measured per call rather than asserted here) + Taiwan (Taipower) + Japan (OCCTO areas) + South Korea (KPX) + Brazil SIN (ONS), ranked side-by-side on each feed's LATEST PUBLISHED reading: renewable share %, gas share %, full fuel mix (gas/nuclear/coal/wind/solar/hydro MW), and demand. ★FRESHNESS IS NOT UNIFORM and every row says so: each carries mix_period, mix_age_hours and freshness_basis. The US rows come from EIA hourly RTO, which publishes the FUEL-TYPE BREAKDOWN several hours behind aggregate demand — an overnight mix reading is routinely 18-24h old (it will show near-zero solar) while demand on the same row is ~1-2h old. Read mix_age_hours before narrating any row as current, and NEVER describe a row as the mix "right now" unless its mix_age_hours is small; demand_period and mix_period are separate clocks and the row reports both plus demand_vs_mix_lag_hours. One call answers "which grid worldwide is greenest, or most gas-reliant, for siting a data center?" — vs compare_isos (pairwise) or get_grid_data (single ISO). Every ranked grid scores renewable_share_pct as wind+solar+hydro (apples-to-apples across all feeds; geothermal is reported separately and, where it exists, also as renewable_share_incl_geothermal_pct — note get_grid_intelligence uses that geothermal-inclusive figure for US ISOs); Brazil ranks by renewable share but reports NO gas share (ONS bundles gas/coal/oil/biomass into one thermal figure — never presented as gas); Australia NEM (AEMO) + Singapore (EMA) are listed unranked in partial_grids (no full fuel split — kept honest). Source: US = EIA hourly RTO; GB = Elexon Insights; EU = ENTSO-E Transparency; TW = Taipower; JP = TSO eria_jukyu CSVs; KR = KPX real-time; BR = ONS Balanço de Energia; AU = AEMO NEM; SG = EMA NEMS — all live via DC Hub, greenest-first. Quote with attribution to DC Hub (CC-BY-4.0). Answers "which grid is cleanest right now", "how is ERCOT doing at this moment". Try: get_grid_scoreboard.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNotrue when the scoreboard build succeeded
countNoRanked grids — the long-standing alias of zones_ranked (NOT grids.length, which also carries the unrankable rows)
gridsNoFully-ranked grids, greenest (highest renewable share) first — US ISOs + GB + EU zones + TW + JP + KR + BR
quotaNoCaller quota state (remaining calls, tier) when available.
sourceNoUpstream feeds behind the rows in THIS response, generated (EIA hourly RTO, Elexon, ENTSO-E, Taipower, OCCTO, KPX, ONS, AEMO, EMA)
_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.
coverageNoCoverage line GENERATED from the rows that actually ranked — a feed that returned nothing is absent from it
freshnessNoRows are each feed's LATEST PUBLISHED reading, NOT a synchronized snapshot: {basis, us_mix_source, stale_mix_threshold_hours, stale_mix_rows[], how_to_read}. Read this before narrating any row as current
ranked_byNoRanking criterion (renewable share = wind+solar+hydro, greenest first) plus the full definition — identical on every feed, geothermal and biomass excluded from the numerator
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.
counts_basisNoWhat each count actually counts, plus per_source rows, eu_zones_live vs eu_zones_configured, and the unranked/unavailable tallies
zones_rankedNoGrid rows carrying a live renewable_share_pct, i.e. the ranked set
partial_gridsNoGrids listed UNRANKED because the feed has no full fuel split (Australia NEM, Singapore EMA)
eu_gas_contextNoEU gas-flow context: {active_countries, total_throughput_gwh_per_day, unit, source, note}
deep_intelligenceNoPointers to the deeper per-ISO / per-site tools to call next
independent_sourcesNoDistinct upstream feeds behind those rows — far below zones_ranked because every EU bidding zone comes from ONE feed (ENTSO-E). null when the per-source tally failed
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.
us_interconnection_queue_gwNoTotal queued generation across the 7 US ISO interconnection queues, GW

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses important behavioral traits: freshness is not uniform, rows carry mix_age_hours/freshness_basis, and the US EIA data has a known lag. It explicitly instructs the agent to read mix_age_hours before narrating any row as current, and explains that some grids are listed unranked in partial_grids. No contradictions 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph that packs in a wealth of information, but it is overlong and would benefit from structured bullets or sections. The repeated 'answers' phrases and trailing 'Try: get_grid_scoreboard' add redundancy, though the opening efficiently states the tool's global scope.

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?

The description covers freshness caveats, partial coverage, regional anomalies, data sources, attribution, and clear use cases. Combined with the presence of an output schema, an agent has everything needed to call and correctly interpret the tool without additional lookups.

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?

The input schema has zero parameters, so no parameter-specific documentation is needed; the schema coverage is 100% by definition. The description instead adds value by explaining output field semantics (mix_age_hours, demand_vs_mix_lag_hours, renewable_share_pct), which is above the baseline for a no-parameter tool.

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 immediately identifies the tool as a global grid scoreboard, enumerates all covered regions, and specifies the ranked metrics (renewable share %, gas share %, fuel mix, demand). It also differentiates itself from siblings by explicitly contrasting with compare_isos (pairwise) and get_grid_data (single ISO), leaving no ambiguity about what the tool returns.

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 states concrete use cases: answering 'which grid worldwide is greenest, or most gas-reliant, for siting a data center?' and 'which grid is cleanest right now'. It also gives exclusion criteria by naming alternatives (compare_isos for pairwise, get_grid_data for single ISO) and warns about edge cases like Brazil having no gas share and AU/SG being unranked.

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.