build.exascale/osint

Describe valid filters, groupings, metrics, detail fields, and citation fields for ai_infrastructure.construction.

Describe valid filters, groupings, metrics, detail fields, and citation fields for ai_infrastructure.employment.

Describe valid filters, groupings, metrics, detail fields, and citation fields for ai_infrastructure.equipment_trade.

Describe valid filters, groupings, metrics, detail fields, and citation fields for ai_infrastructure.production.

Describe valid filters, groupings, metrics, detail fields, and citation fields for ai_infrastructure.trade.

Describe any served capability by name — the generic twin of the named describe tools. Pass `capability` as either a capability id from list_capabilities_v1 (e.g. "power.capacity") or a query primitive name (e.g. "query_power_capacity_v1"). Returns the same schema payload as the named describe tool: valid filters, groupings, metrics, detail fields, and citation fields. Use the generic pair (this + query_capability_v1) when list_capabilities_v1 names a capability that has no named tool in your client's tool list — clients cache tool lists, and capabilities shipped after that cache are still fully reachable here.

Describe capacity factor: net generation / (operating nameplate × hours), joined across EIA-860M and EIA-923.

Describe the EIA Henry Hub and state electric-power gas-price atoms, units, aggregate grain, missingness, vintages, and citations.

Describe annual EIA-860 ownership filters, convention, metrics, and exact-cell citations.

Describe ERCOT planning capacity-contribution classes, seasons, peak definitions, methodologies, vintages, and exact-cell citations.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.capacity.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.demand_rollup.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.demand.

Describe EIA-923 receipt/cost filters, raw units, missing semantics, and exact-cell citations.

Describe valid filters, groupings, atoms, metrics, detail fields, and citation fields for power.generation.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.interconnection_queue_caiso.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.interconnection_queue_ercot.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.interconnection_queue_isone.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.interconnection_queue_nyiso.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.interconnection_queue_pjm_cycle.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.interconnection_queue_pjm.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.interconnection_queue_spp.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.interconnection_queue.

Describe native-XBRL Form 1 plant-cost facts, units, coverage, and exact-fact citations.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.price_ercot.

Describe ERCOT hourly actual wind/solar output, published regions, coverage, DST identity, and exact-cell citations.

Describe valid filters, groupings, metrics, detail fields, and citation fields for power.retail_sales.

Describe valid filters, groupings, metrics, detail fields, and citation fields for robotics.adoption.

Describe valid filters, groupings, metrics, detail fields, and citation fields for robotics.trade.

Describe valid filters, groupings, metrics, detail fields, and citation fields for space.satellite_filings.

Fetch and hash-verify the raw source row behind a returned citation. Pass a citation object inside `params`. Two ready-to-pass shapes come straight from the query tools: each aggregate row's `citations[ref].verify` object, or a detail record's `citation` (from include_records). Either proves the number with no re-query. The tool verifies the raw workbook SHA-256 before returning source-header row values. Use this when an agent must prove an answer from the underlying source row.

List available exascale.build data capabilities for agent discovery before querying. Also call this BEFORE stating that a capability is not available — client tool lists are cached and this surface grows; anything listed here is reachable via query_capability_v1 even if your tool list predates it.

Query verified U.S. private construction spending ($ millions) for data centers and semiconductor/computer-electronics manufacturing plants, from the U.S. Census Bureau's Value of Construction Put in Place (C30). Use this for "how much is being spent BUILDING data centers (or chip fabs) in the US" questions — the construction buildout in dollars, not capacity or investment. Filter by `category` ("data_center" — Census's named subcategory under Office; or "computer_electronic_electrical" — the semiconductor/computer-electronics manufacturing line under Manufacturing), `basis` ("seasonally_adjusted" = a seasonally-adjusted ANNUAL RATE, or "not_seasonally_adjusted" = the NOT-adjusted MONTHLY LEVEL), `data_month` (one month, ISO first-of-month e.g. "2026-04-01") or the `data_month_from`/`data_month_to` range, `year`, and `revision_status` ("preliminary", "revised", or "final"). Group by any of `category`, `basis`, `data_month`, `year`, or `revision_status`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"category": "data_center", "basis": "seasonally_adjusted", "data_month": "2026-04-01"}` for one month; add `"group_by": ["data_month"]` over a `data_month_from`/`data_month_to` range for a series. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Census workbook, sheet, row, and column. The two categories are DISTINCT series and are never conflated: `data_center` is data-center buildings; `computer_electronic_electrical` is the chip/electronics-manufacturing (fab) line — the CHIPS-Act build-out. `basis` is the other fork: the seasonally-adjusted series is an ANNUAL RATE (what the current monthly pace annualizes to), while the not-seasonally-adjusted series is the actual MONTHLY LEVEL. `revision_status` carries Census's own preliminary/revised/final marking verbatim. Data is monthly; the data-center series beg…

Query verified U.S. employment, establishments, and wages — total and by industry (data centers, semiconductors, construction, retail, accommodation, food service) — for any county, state, or the nation, from the U.S. Bureau of Labor Statistics' Quarterly Census of Employment and Wages (QCEW). Use this for two families of questions: (1) "how many people work in / how many establishments / what wages in data centers or chip fabs" — INDUSTRY employment, not an "AI jobs" count; and (2) the place-based question — "what happened to a county's employment, wages, construction, or local economy (e.g. during and after a data-center / fab buildout)": total covered employment plus the buildout-phase and induced-sector series for every US county, quarterly since 2014. Filter by `industry_code` — each code lives at ONE aggregation depth, shown here with its agglvl codes (national/state/county): "10" Total, all industries — every covered job (agglvl 10/50/70 = all ownerships combined; 11/51/71 = split by ownership) "23" Construction (sector; 14/54/74) "44-45" Retail trade (sector; 14/54/74) "721" Accommodation (3-digit; 15/55/75) "722" Food services & drinking places (3-digit; 15/55/75) "236220" Commercial & institutional building construction (6-digit; 18/58/78) "518210" Computing infrastructure / data processing / web hosting — the data-center industry (6-digit; 18/58/78) "334413" Semiconductor & related device manufacturing (6-digit; 18/58/78) `agglvl`'s first digit is geography (1 national / 5 state / 7 county); pick ONE industry_code and the matching agglvl for its depth to get a clean additive scope. Also filter by `own_code` ("5" = Private — the usual one; "1"/"2"/"3" = federal/state/local government; "0" = Total Covered, only on industry "10"), geography (`state` USPS e.g. "VA", `county_fips` 5-digit e.g. "51107" Loudoun County, or `area_fips`), and time (`year`, `qtr` "1"-"4", the `quarter` ISO first-of-quarter e.g.…

Query verified U.S. monthly IMPORTS of semiconductor-manufacturing EQUIPMENT (HS-8486) — customs value (USD) by country of origin — from the U.S. Census Bureau's International Trade data. Use this for "is the fab buildout actually tooling up, and who supplies the machines" questions — the equipment leg of the fab lifecycle: construction spending (ai_infrastructure.construction) measures the shell, this measures the tools flowing in, and chip imports (ai_infrastructure.trade) measure the output side. HS-8486 covers machines and apparatus used solely or principally to MANUFACTURE semiconductor boules/wafers, devices, and integrated circuits — AND flat-panel displays (Census does not split them at this level); it is NOT the chips themselves (those are HS-8542). Filter by `country` (the verbatim Census name, e.g. "JAPAN", "NETHERLANDS", "KOREA, SOUTH"), `cty_code` (the Census country code), `country_level` ("total" = the all-countries TOTAL, "country" = an individual country, "grouping" = a Census bloc/continent like ASIA / APEC / EU), `year`, `data_month` (one month, ISO first-of-month e.g. "2026-04-01") or the `data_month_from`/`data_month_to` range. Group by any of `country`, `cty_code`, `country_level`, `data_month`, or `year`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"country_level": "country", "group_by": ["country"], "order_by": "general_value_usd", "top_n": 5}` for the top tool-supplying countries; `{"country_level": "total", "group_by": ["data_month"]}` for the national trend. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Census response row, re-verifiable via get_source_evidence_v1. Measures: `general_value_usd` (general imports value) and `consumption_value_usd` (imports for consumption) — value only; no tool counts, and no tool-type or vendor breakdown (one HS4 heading: no lithography-vs…

Query verified U.S. semiconductor & electronic-component PRODUCTION and CAPACITY UTILIZATION — the Federal Reserve's monthly G.17 industrial-production index (2017=100) and capacity-utilization rate (percent) for NAICS 3344 — from the Board's own release, history to 1972. Use this for "are the domestic fabs actually producing / how hot are they running" questions — the OUTPUT leg of the fab lifecycle: construction spending (ai_infrastructure.construction) measures the shell, equipment imports (ai_infrastructure.equipment_trade) the tools flowing in, chip imports (ai_infrastructure.trade) what crosses the border; this measures domestic production and how much of the installed capacity is in use. NAICS 3344 is "semiconductor and OTHER electronic component" manufacturing — the finest split the Fed publishes here (broader than semiconductors alone, and NOT the same slice as QCEW's 334413). Filter by `series_kind` ("ip" = the production index, on both bases; "capacity_utilization" = percent of capacity in use, seasonally adjusted only; "capacity" = the capacity index behind the rate), `series_name` (the verbatim Fed series, e.g. "IP.G3344.S", "CAPUTL.G3344.S"), `basis` ("seasonally_adjusted" / "not_seasonally_adjusted" — IP only), `year`, `data_month` (ISO first-of-month, e.g. "2026-05-01") or the `data_month_from`/`data_month_to` range. Group by any of `series_name`, `series_kind`, `basis`, `data_month`, or `year`. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"series_kind": "capacity_utilization", "group_by": ["data_month"], "data_month_from": "2024-01-01"}` for the utilization trend; `{"series_kind": "ip", "basis": "seasonally_adjusted", "group_by": ["year"]}` for the production index by year (an average per year). Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Fed SDMX observation, re-verifiable via get_source_evidence_v1. Measures are avg/min/m…

Query verified U.S. monthly IMPORTS of integrated circuits (HS-8542) — customs value (USD) by country of origin — from the U.S. Census Bureau's International Trade data. Use this for "how much $ of chips did the US import (from Taiwan / South Korea / in total) and how is it trending" questions. HS-8542 is ALL integrated circuits (processors, memory, amplifiers, parts) — NOT AI-accelerator / GPU-specific. Filter by `country` (the verbatim Census name, e.g. "TAIWAN", "KOREA, SOUTH"), `cty_code` (the Census country code, e.g. "5830"), `country_level` ("total" = the all-countries TOTAL, "country" = an individual country, "grouping" = a Census bloc/continent like ASIA / APEC / EU), `year`, `data_month` (one month, ISO first-of-month e.g. "2026-04-01") or the `data_month_from`/`data_month_to` range. Group by any of `country`, `cty_code`, `country_level`, `data_month`, or `year`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"country_level": "country", "group_by": ["country"], "order_by": "general_value_usd", "top_n": 5}` for the top source countries; `{"country_level": "total", "group_by": ["data_month"]}` for the national trend. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Census response row, re-verifiable via get_source_evidence_v1. Measures: `general_value_usd` (general imports value) and `consumption_value_usd` (imports for consumption) — value only; HS-8542 reports no meaningful quantity at this level, so there is no chip count. NEVER SUM across country rows: Census's groupings (ASIA, APEC, EU, OECD, ASEAN, the continents) OVERLAP each other and the individual countries, and the all-countries TOTAL contains everything — so adding rows double-counts. Filter `country_level=total` for the U.S. national figure, `country_level=country` for individual countries, or group_by country for the per-country ser…

Query any served capability by name — the generic twin of the named query tools, reaching every capability including ones newer than your client's cached tool list. Pass `capability` as either a capability id from list_capabilities_v1 (e.g. "power.price_ercot") or a query primitive name (e.g. "query_power_price_ercot_v1"), and `params` as the same flat JSON object the named query tool accepts — call describe_capability_v1 first for valid filters, e.g. {"capability": "power.capacity", "params": {"state": "TX", "group_by": ["energy_source_code"]}}. Returns the identical cited envelope as the named tool: same rows, same citations, same as_of.

Query verified U.S. capacity factor — how hard a fleet actually runs — by joining EIA-860M capacity and EIA-923 generation. Requires `data_month`: one ISO month start, e.g. "2026-01-01". If the user names no month, ask which one (or state the month you chose); if a month is not covered, the error lists the months that are — do not retry blindly. capacity_factor = net generation (MWh) / (operating nameplate capacity (MW) × hours in the month), computed over plant×fuel present in BOTH sources, so scope is auto-aligned. Optional `group_by` of `state` and/or `fuel_group`, and `state`/`fuel_group` filters. Returns the capacity factor per group with its generation and capacity, a `coverage` declaration (what share of in-scope capacity/generation matched), and a citation to BOTH the capacity and the generation source row. Basis is nameplate; storage is excluded; the capacity snapshot is matched to the month. Does not determine per-generator capacity factor, a net-summer/winter basis, or months absent from either source.

Query verified EIA natural-gas price records from two public series. `atom="henry_hub_daily_spot"` returns one trading-day Henry Hub spot price in EIA's published $/MMBtu unit (series RNGWHHD). `atom="state_electric_power_monthly"` returns one state-month average price paid by electric-power consumers in EIA's published $/Mcf unit; `{"atom":"state_electric_power_monthly","state":"TX"}` is the Texas power-sector proxy slice in one call. Filter either atom by `date`, `date_from`/`date_to`, series, state, or price status. The state series is an aggregate of what the state's power sector paid that month, not a plant-level fact. EIA null values remain explicit `price=null` records with `price_status="source_missing"`—never zero or imputed. The two source units are never converted or blended; mixed-unit avg/min/max are null. This tool does not assign proxies to plants or derive heat rates, $/MWh, spark spreads, or any ratio. API revisions are preserved as immutable capture vintages selected by `as_of`. Every price cites its exact archived response record, series id, period, value cell, and SHA-256 for source-evidence verification.

Query verified annual EIA-860 generator ownership. Returns the Owner schedule's raw owner names and ownership shares for each `eia_plant_id` + `generator_id`. `percent_owned` is the workbook's raw fraction of one (0.6 = 60%), not a whole-number percent. Filter by plant, generator, exact raw owner name, state, owner state, annual vintage, or source-reported balancing authority code; `{"state":"TX","balancing_authority_code":"ERCO"}` returns an ERCOT slice in one call. Owner-name matching is exact and intentionally performs no normalization or entity resolution. Critical EIA convention: the Owner schedule contains only jointly owned generators and generators wholly owned by an entity other than the operator. A generator absent from it is wholly owned by the operator in that same annual EIA-860 Generator schedule. An exact plant+generator query exposes this as `ownership_resolution`; it does not fabricate an Owner row or a 1.0 source share. Annual vintages remain independently queryable. Every returned share cites its exact ZIP member, sheet, row, and `Percent Owned` cell for SHA-256 verification.

Query ERCOT's as-published planning capacity-contribution assumptions. These are planning assumptions, not realized performance or plant firm MW; classes, seasons, peak definitions, methodologies, and vintages are never blended.

Query verified U.S. generator-level operating, planned, retired, or canceled power capacity from EIA-860M. Use this for capacity questions by state/jurisdiction, county FIPS, source-reported balancing authority code, fuel, prime mover, technology, lifecycle, or year. Pass filters inside the `params` object. The operating/planned/retired/canceled selector is `lifecycle` (e.g. `lifecycle: "operating"`, the default) — there is no `status` or `status_group` parameter. Returns JSON aggregates with citations and optional generator-level records when `include_records` is true. Does not determine electricity supplied, generation MWh, real-time dispatch, capacity factor, battery storage throughput/duration, demand/load, prices, data-center load, or transmission deliverability. For capacity REQUESTED in an ISO interconnection queue (projects pending interconnection, not yet built), use the relevant ISO's queue tool: query_power_interconnection_queue_v1 (MISO), query_power_interconnection_queue_pjm_v1 (PJM — or query_power_interconnection_queue_pjm_cycle_v1 for PJM's cluster/cycle grid), or query_power_interconnection_queue_caiso_v1 (CAISO).

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 d…

Query verified U.S. hourly electricity demand (MW) by balancing authority from EIA-930. Use this for "how much load" questions at the hourly balancing-authority grain: filter or group by `balancing_authority_code`, `region`, `data_date` (or the `data_date_from`/`data_date_to` range), `hour_number`, `datetime_utc`, or `is_imputed`. Pass filters inside the `params` object. Returns JSON aggregates with citations and optional row-level records when `include_records` is true. `demand_mw` is EIA's own cleaned (Adjusted) series, with receipts: the as-reported `demand_mw_raw` and the `is_imputed` flag ride every detail record. `demand_forecast_mw` is the same row's day-ahead forecast, so forecast-vs-actual misses need no second query. History runs hourly from 2015-07-01 onward and is served by default: a bare `data_date` anywhere in that window answers from the newest promoted vintage covering it, and the response `as_of` is that knowledge cut. A query with NO calendar window (no `data_date`, `data_date_from`, or `data_date_to`) and no calendar-axis `group_by` defaults to the latest day that has reported demand — not the full history — and says so in a `default_latest_day` note; group by `data_date` or `datetime_utc`, or pass a date range, to read a series over time. Pin `as_of` to an earlier vintage to reproduce exactly what was served then; one response may cite several source files, and every citation carries its own file and vintage. An empty result names the served coverage window in an `empty_scope` note. Demand is NOT additive across balancing authorities: a result summing more than one BA carries a `ba_aggregation` scope note and ranking remainders omit the demand metrics — group by `balancing_authority_code` for the source-grain series. Does not determine plant, generator, county, or state attribution (EIA-930 carries no such IDs, and BA footprints do not follow state lines), US48 or regional totals (computed rollups are refused; EIA's own published series is the…

Query verified raw EIA-923 fuel receipts and delivered fuel costs. Returns one Page 5 Fuel Receipts and Costs row per published receipt: plant/month, fuel, supplier, purchase type, source physical quantity, and delivered cost in EIA's stated cents/MMBtu. Filter by plant, month/range, exact source strings, state, fuel, cost status, or source-reported balancing authority code; `{"state":"TX","balancing_authority_code":"ERCO"}` returns an ERCOT slice in one call. Quantity units remain fuel-specific (short tons, barrels, or Mcf). EIA withholds costs for some plants. The raw `.` marker is preserved in `fuel_cost_raw`, the numeric cost is null, and `fuel_cost_status` explicitly reports `withheld` for unregulated receipts. Missing is never zero or imputed. This tool does not derive heat rates, efficiency, marginal cost, generation cost, or $/MWh; combine the cited raw atoms outside exascale.build if analysis requires those judgments. Every quantity or cost can be verified against its exact workbook cell.

Query verified U.S. monthly net electricity generation (MWh) from EIA-923. Use this for "how much was generated" questions by state, source-reported balancing authority code, fuel, prime mover, sector, plant, or generator, for a given month. For a fuel total or a fuel mix (e.g. "coal generation", "top fuels"), filter or group by `fuel_group` — it sums the several energy_source_code values a fuel spans (coal alone is 6 codes), so a total is correct-by-construction; use the raw `energy_source_code` only when you want one exact as-reported code, since it splits coal/biomass across sub-codes. Select one `atom`: `by_fuel` (default — the complete plant total) or `by_generator` (generator-level, joinable to EIA-860M); never sum across atoms. History runs monthly from 2014-01 onward and is served by default: a bare `data_month` anywhere in that window answers from the newest promoted vintage covering it, and the response `as_of` is that knowledge cut (pin `as_of` to any date to reproduce what was served then — it resolves to the newest vintage at or before it; an empty result names the served window in an `empty_scope` note). `balancing_authority_code` is reported by EIA only from 2018 onward — a BA-filtered query cannot see earlier months. Pass filters inside the `params` object. Returns JSON aggregates with citations down to the exact source month-cell. Does not determine installed capacity (MW — use power.capacity), demand/load, wholesale prices, fuel cost, heat rate, capacity factor, or real-time/hourly dispatch.

Query the CAISO generator interconnection queue — California ISO's public Public Queue Report, the waiting line of projects that have REQUESTED to connect to the CAISO grid (California, plus the out-of-state edges it studies: NV, AZ). Returns cited, project-level records with CAISO's full published structure: the net megawatts to grid (`net_mw_to_grid`, CAISO's own headline figure) and the per-component Type/Fuel/MW triplets for hybrids (`type_1..3`, `fuel_1..3`, `mw_1..3`, plus an `is_hybrid` flag), the as-reported `application_status`, the cluster `study_process` (C01..C14, plus serial/legacy tracks), the three-valued deliverability status (`deliverability_status` = Full Capacity / Partial Capacity / Energy Only) with `tpd_allocation_percentage` / `tpd_allocation_group` / `offpeak_deliverability`, location (`state`, `county`, derived `county_fips`, `utility`, `pto_study_region`), the per-phase study statuses, and lifecycle dates (`ir_receive_date`, `queue_date`, `proposed_online_date`, `current_online_date`, and — for completed projects — `actual_online_date`). Group or filter by `application_status`, `state`, `county_fips`, `deliverability_status`, `study_process`, `fuel_1`, `type_1`, `utility`, `pto_study_region`, `offpeak_deliverability`, `tpd_allocation_group`, `suspension_status`, `ia_status`, or (group only) `is_hybrid`; filter `queue_date` by the `queue_date_from` / `queue_date_to` range. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"application_status": "ACTIVE", "fuel_1": "Battery", "state": "CA"}` for active battery requests in California; `{"group_by": ["application_status"]}` for net MW and project counts by status; `{"application_status": "ACTIVE", "group_by": ["deliverability_status"]}` for the active pipeline split by deliverability. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get…

Query the ERCOT generator interconnection queue — ERCOT's public GIS Report (EMIL PG7-200-ER), the waiting line of generation projects that have REQUESTED to connect to the ERCOT (Texas) grid. Returns cited, project-level records with ERCOT's full published structure across four lifecycle sheets (Large Gen + Small Gen = active; Inactive Projects; Cancellation Update): the requested `capacity_mw` (ERCOT publishes ONE capacity figure — no summer/winter split), the ERCOT `fuel` and `technology` codes (e.g. SOL/PV solar, OTH/BA battery, GAS/CC combined-cycle, WIN/WT wind — HYD is HYDROGEN, hydro is WAT), the `cdr_reporting_zone` (NORTH/SOUTH/WEST/COASTAL/HOUSTON/PANHANDLE), the `interconnecting_entity`, the `poi_location`, the composite `gim_study_phase` token string, and the milestone dates (`screening_study_started`, `fis_approved`, `ia_signed`, `construction_start`/`construction_end`, `approved_for_energization`/`approved_for_synchronization`, `projected_cod`). Group or filter by `application_status`, `size_category`, `fuel`, `technology`, `cdr_reporting_zone`, `county_fips`, `state`, `gim_study_phase`, or `interconnecting_entity`; filter `projected_cod` by the `projected_cod_from` / `projected_cod_to` range. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"application_status": "ACTIVE", "fuel": "SOL"}` for active solar requests; `{"application_status": "ACTIVE", "group_by": ["fuel"], "order_by": "capacity_mw", "top_n": 5}` for the active pipeline's biggest fuels by requested MW. The GIS Report is published MONTHLY and its full history is queryable — this is NOT a single point-in-time snapshot. Omit `as_of` for the latest month, or pass `as_of` (a date) to get the queue as it stood at a past month: `as_of` resolves to the newest monthly snapshot at or before it, with vintages back to 2018-12 (the floor; an earlier `as_of` is refused, naming the floor). Example: `{"application_status": "ACTIVE", "as_of": "2019-06-30"}` returns the…

Query the ISO-NE generator interconnection queue — ISO New England's public IRTT "public queue" report, the waiting line of projects that have REQUESTED to connect to the New England grid (CT, MA, ME, NH, RI, VT). Returns cited, project-level records with ISO-NE's full published structure (all 31 columns of the rendered report): the three megawatt readings kept SEPARATE (`net_mw`, `summer_mw` = max summer output, `winter_mw` = max winter output — `net_mw` is 0 for the many Capacity-Network-Resource-only requests, a real value, not missing), the request `request_type` (G = Generation / ETU = Elective Transmission Upgrade / TS = Transmission Service), the space-delimited multi-value `fuel_type` (EIA energy-source codes, e.g. "SUN BAT"), the `unit_type` and service code `serv` (CNR = Capacity Network Resource / NR = Network Resource), the `jurisdiction` (F = FERC / N = Non-FERC), the ISO-NE load `zone`, the per-stage study statuses (`fs_status` … `ia_status`) with their study-document links, location (`state`, `county`, derived `county_fips`, `poi`), and lifecycle dates. Group or filter by `application_status`, `project_status`, `request_type`, `unit_type`, `fuel_type`, `serv`, `jurisdiction`, `zone`, `state`, `county_fips`, or `cluster`; filter `requested_date` by the `requested_date_from` / `requested_date_to` range. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"application_status": "A", "request_type": "G", "state": "MA"}` for active generation requests in Massachusetts; `{"group_by": ["application_status"]}` for requested MW and project counts by status. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. `net_mw` / `summer_mw` / `winter_mw` are REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built, so the queue is withd…