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azmartone67

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by azmartone67
OverviewSchemaRelated ServersScoreDiscussions(1)
JavaScript
Remote

get_grid_scoreboard

Read-onlyIdempotent

Rank global power grids side by side by renewable share, gas share, fuel mix, and demand to identify the greenest or most gas-reliant grid for data center siting.

Instructions

Live GLOBAL grid scoreboard — 7 US grid operators (PJM, ERCOT, CAISO, MISO, SPP, NYISO, ISO-NE) + Great Britain (NESO) + ~24 European bidding zones (Germany, France, Netherlands, Italy/Milan, Spain, Poland, Switzerland, Portugal, the Nordics + Central/Eastern Europe — via ENTSO-E) + Taiwan (Taipower) + Japan (OCCTO areas) + South Korea (KPX) + Brazil SIN (ONS), ranked side-by-side RIGHT NOW: renewable share %, gas share %, full fuel mix (gas/nuclear/coal/wind/solar/hydro MW), and demand. 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 as wind+solar+hydro share (apples-to-apples); 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

Tool Definition Quality

A4.8/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive. The description adds valuable context: live data, ranking logic, data sources, and specific reporting limitations (Brazil gas share, partial grids).

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?

Description is dense but well-structured, front-loading purpose and then covering coverage, limitations, sources. Slightly lengthy but each sentence adds value.

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?

Despite no output schema, the description fully explains what the tool returns (rankings, metrics, grid list) and gives context on data sources and attribution. Complete for the tool's purpose.

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?

No parameters; baseline 4 applies. Description does not need to add parameter meaning.

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 clearly states it provides a live global grid scoreboard with specific metrics and distinguishes from siblings compare_isos and get_grid_data. It uses precise verbs 'get' and 'live GLOBAL grid scoreboard'.

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?

Explicitly states when to use: to answer 'which grid worldwide is greenest... for siting a data center' and contrasts with alternatives. Also notes limitations for Brazil and partial grids.

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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