osint

Query Power Demand (national / region rollup)

query_power_demand_rollup_v1

Read-onlyIdempotent

Query verified U.S. hourly electricity demand (MW) as EIA's own published national and regional totals from the EIA Grid Monitor (region-data).

Use this for "how much load for the whole country, or a region" questions. Filter by respondent (US48 = the Lower-48 national total, or one of the 13 EIA regions — CAL, CAR, CENT, FLA, MIDA, MIDW, NE, NW, NY, SE, SW, TEN, TEX), data_date (one day) or the data_date_from/data_date_to range, and hour_number. To pin one specific UTC hour, combine data_date + hour_number. Group by any of respondent, respondent_level (national vs region), data_date, hour_number, or datetime_utc. datetime_utc and respondent_level are grouping/output axes only — not filters. Pass each parameter as a top-level key of params (flat — not nested under a filter, filters, or where key). Example: {"respondent": "US48", "data_date": "2026-06-10", "hour_number": 14} for the US48 total at one hour; add "group_by": ["datetime_utc"] over a data_date_from/data_date_to range for a series. Returns JSON aggregates with citations and optional row-level records when include_records is true.

demand_mw is EIA's OWN published demand total, served verbatim — the Adjusted series (the same canonical definition as power.demand's demand_mw), NOT a sum exascale computed. This closes power.demand's refusal of national/region totals (BA demand is non-additive across balancing authorities). demand_forecast_mw is the same respondent-hour's day-ahead forecast, so forecast-vs-actual misses need no second query.

History runs hourly from 2019-01-01 onward — this published series begins about 3.5 years later than power.demand's balancing-authority history — and is served by default; the response as_of is the knowledge cut. A query with NO calendar window and no calendar-axis group_by defaults to the latest day with reported demand and says so in a default_latest_day note — group by data_date or datetime_utc, or pass a date range, for a series over time. Pin as_of to an earlier vintage to reproduce what was served then.

INVERTED additivity: demand_mw is ALREADY a published total, so it is NOT additive across respondents — US48 already equals the sum of the 13 regions. A result spanning more than one respondent without grouping by it carries a respondent_aggregation scope note and ranking remainders omit the demand metrics: filter respondent=US48 for the national total, or group by respondent for the per-respondent series. Occasional source-quality anomalies (an hour EIA did not publish; a rare impossible value EIA published) are served verbatim and cited, never altered.

Does not determine balancing-authority-level demand (use power.demand for the BA series), demand before 2019-01-01, the raw un-Adjusted series (this route publishes the Adjusted series only), plant, generator, county, or state attribution, installed capacity (use power.capacity), monthly plant generation (use power.generation), retail sales, revenue, or customers (use power.retail_sales), wholesale prices, or long-horizon forecasts (the forecast is day-ahead only).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A5/5.0

Behavior5/5

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

Beyond annotations (readOnly, idempotent, not destructive), the description adds critical behavioral traits: demand_mw is served verbatim, not a computed sum; inverted additivity with a respondent_aggregation scope note; source anomalies served verbatim and cited; default_latest_day behavior; and as_of vintage pinning. 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.

Conciseness5/5

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

Though lengthy, the description is appropriately sized for the tool's complexity. It front-loads the core purpose in the first sentence and organizes information into clear thematic sections (scope, parameters, data provenance, additivity, exclusions). Every sentence delivers operational value without fluff.

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 data source, history range, parameter semantics, defaults, additivity caveats, data quality handling, and explicit exclusions with alternatives. An output schema exists, so return-level details are not needed here; the description is complete for selecting and invoking the tool correctly.

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?

The input schema is nearly empty (only a generic params object, 0% coverage), so the description fully carries the load. It enumerates valid respondent values, data_date/hour_number combination, range parameters, group_by axes, flat params structure, and provides a concrete JSON example. It also explains which params are filters vs output axes.

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 starts with a specific verb and resource: 'Query verified U.S. hourly electricity demand (MW) as EIA's own published national and regional totals from the EIA Grid Monitor (region-data).' It clearly distinguishes from siblings by stating this closes power.demand's refusal of national/region totals and that BA demand is non-additive.

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 explicitly states when to use: 'Use this for "how much load for the whole country, or a region" questions.' It also provides exclusions with alternatives, e.g., 'Does not determine balancing-authority-level demand (use power.demand for the BA series)' and lists other tools for capacity, generation, retail sales, etc.

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

A4/5.0

Disambiguation4/5

Each capability has a clearly named describe/query pair, and domains like power, AI infrastructure, robotics, and space are distinct. The generic describe_capability_v1/query_capability_v1 could be confused with the named variants, and the many ISO-specific interconnection queues share similar names, but descriptions explicitly disambiguate them.

Naming Consistency5/5

All tools follow a consistent lowercase snake_case verb_noun pattern: describe_<capability>_v1 and query_<capability>_v1, with a few utility tools like list_capabilities_v1 and get_source_evidence_v1. There is no mixing of conventions.

Tool Count2/5

At 62 tools, the set is far too large for typical server scope. The describe/query pairs inflate the count even though each is justified, and the generic capabilities plus per-ISO variants make it feel heavy and hard to navigate.

Completeness4/5

The server covers each domain thoroughly with describe, query, and evidence verification, plus generic fallbacks for capabilities not yet in the client's tool list. Minor gaps exist (e.g., no county/state attribution for some data, no load types in ISO queues) but they are explicitly documented and workable.

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