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DC Hub — Data Center & Energy Intelligence

Grid Scoreboard

get_grid_scoreboard
Read-onlyIdempotent

Compare live renewable and gas shares across 20+ grid operators to find the greenest or most gas-reliant power for data center siting.

Instructions

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). 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
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is clear. The description adds vital behavioral context beyond that: freshness is non-uniform across grids, demand and mix timestamps differ, Brazil's gas share is not available, and Australia/Singapore are listed unranked. These are important caveats that prevent misuse and are not conveyed by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

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

The description is a very long, dense wall of text. While it packs in crucial detail, it is not concise or well-structured; it could be broken into sections or moved to the output schema. It violates the 'every sentence earns its place' ideal by over-explaining details that might belong elsewhere, making it cumbersome to parse quickly.

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?

For a tool with no parameters, an output schema (not shown), and simple annotations, the description is extremely complete. It explains what the tool returns (renewable share %, gas share %, fuel mix, demand), the meaning of freshness fields, data sources per region, and how to interpret special cases (Brazil, Australia/Singapore). It gives context that cannot be derived from the schema or annotations, making it fully self-sufficient.

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?

This tool has zero parameters, and the schema has no properties. Per the rubric baseline of 4 for 0-param tools, no description is needed for parameter semantics. The description doesn't need to add meaning beyond schema since there is nothing to explain, so a solid 4 is appropriate.

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 'GLOBAL grid scoreboard' and specifies a clear verb+resource: it ranks grids worldwide on latest published readings. It explicitly distinguishes itself from siblings by mentioning 'vs compare_isos (pairwise) or get_grid_data (single ISO)', so the purpose is unambiguous and well-differentiated.

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 when to use this tool: 'One call answers "which grid worldwide is greenest, or most gas-reliant, for siting a data center?"' and contrasts it with alternatives (compare_isos pairwise, get_grid_data single ISO). It also warns about freshness caveats, telling users to read mix_age_hours before narrating, and explains when NOT to treat data as current. This is explicit when-and-when-not 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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