"Moscow Metro" matching MCP tools:

• analyze_siteA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Use when a user has ONE specific lat/lon (a parcel, a candidate site) and wants the full multi-factor data-center suitability read in one call. Example: "Score this Phoenix parcel for a 100MW build — power, gas, fiber, market & risk." — analyze_site lat=33.45 lon=-112.07 capacity_mw=100 state=AZ. Params: lat (-90 to 90, required unless candidate_id or location), lon (-180 to 180, required unless candidate_id or location), location (a market NAME or metro slug instead of coordinates, e.g. location="ashburn" — resolved to that market's PUBLISHED CENTROID through the DCPI market row, with a resolved_from block naming what it resolved to; a MARKET-level read, NOT the parcel you named, and a trailing state is not stripped so "Ashburn, VA" will not resolve), candidate_id (a cand_… from get_refined_queue — resolves coordinates from the frozen mint and ignores lat/lon), capacity_mw (target load in MW, e.g. 50-500 — returns a `capacity_context` block sizing that load against nearby installed generation; it deliberately does NOT move overall_score, and the block names where the load IS applied), state (2-letter US, optional — improves the tax-incentive/context lookup), include_grid/include_risk/include_fiber (booleans, default true). Returns (full, paid): {overall_score (aka composite_score, 0-100 composite — for the integrity-first version that never imputes a missing factor, use get_composite_site_score), interpretation (verdict string, e.g. "Excellent site"), scores{power_infrastructure, gas_pipeline_access, fiber_connectivity, market_conditions, risk_resilience — each 0-100}, nearby{substations_50km, power_plants_80km, gas_pipelines_50km, facilities_100km, fiber_carriers_in_state, generation_capacity_mw, total_capacity_mw}, power_cost{industrial_cents_kwh, commercial_cents_kwh, period, basis}, fiber{connectivity_score, nearest_carrier_km, near_net_bucket, top_carriers[], single_carrier_risk}, location, citation}. FREE tier returns a REAL, citable HEADLINE — composite_score + verdict + the single top limiting factor (the lowest sub-score) + citation; the full per-factor breakdown, nearby infrastructure, power cost, fiber carriers, and the branded Site Analysis PDF (generate_site_analysis) are Pro. For dedicated water / disaster / climate / tax reads use get_water_risk / get_disaster_risk / get_climate_intel / get_tax_incentives. Do NOT use to compare 2+ sites (use compare_sites) or to find sites that match a target (use find_alternatives).

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• polymarket_arbitrageA

  Open MeteoA

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• check_regionA

  commutescoutA

  Full current-conditions report for a California region. Use this for area-scale questions ("how is the Bay Area?", "what's happening in SoCal?") instead of stitching together point queries. It sweeps every source over the whole region at once: CHP incidents (severity-sorted, worst first), lane closures in place (full closures called out), chain controls, and wildfires inside the region. Regions: Bay Area, Sacramento metro, Tahoe/Sierra, Central Valley, Southern California, San Diego, Central Coast, North State. An unrecognized region name returns the list. Large regions are capped to the most severe items; the counts are always exact and the response says when a list was truncated. Freshness: CHP ~1/min fetched live, everything else 5-minute cache.

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• get_map_snapshotA

  Smarter WeatherA

  Render a weather map image for visual analysis. Simple form: pass `product` (a viz-catalog product_id like "mrms_qpe_01h_pass2_conus", "goes_truecolor_conus", "spc_day1_categorical", "hrrr_precip_hybrid_derived_conus" (future radar), "hrrr_subhourly_conus" (15-min Future Radar), "mrms_radar_nowcast_conus", "rtma_conus", "nbm_daily_temps", or "nexrad_l3:{SITE}:{PRODUCT}" for single-site radar, e.g. "nexrad_l3:TLX:N0B") plus a location and zoom (5=regional, 8=metro, 10=city). Composed form: pass `scene` -- a declarative scene document layering basemap + multiple weather products + active alerts + storm features + inline GeoJSON in one image (layers draw bottom-to-top, under basemap labels). Example scene: {"scene":"1.0","view":{"center":{"lat":43.8,"lon":-91.2},"zoom":8},"layers":[{"type":"weather","product":"goes_truecolor_conus"},{"type":"weather","product":"nexrad_l3:ARX:N0B"},{"type":"alerts","filter":{"events":["Tornado Warning"]},"onError":"skip"}]}. Alert filters (all optional, AND-combined): `ids` (specific alerts), `events`, `severities`, `minSeverity` (Extreme>Severe>Moderate>Minor>Unknown). Single-site radar keys: the address is `nexrad_l3:{SITE}:{KEY}` where KEY is `N{tilt}{measurement}` and tilt 0 is the 0.5 degree sweep -- N0B reflectivity (dBZ, where and how heavy), N0G base velocity (knots toward/away from the radar), N0S storm-relative velocity (storm motion removed, so a couplet is rotation rather than translation -- prefer it for rotation questions), N0C correlation coefficient (0-1, debris and hail), N0X differential reflectivity (dB). Legacy codes (N0V, N0R, N0Q) are accepted as aliases. Not every site produces every key; when a render reports which keys a site has, retry with one of those. Optional `time` (unix seconds): closest frame. Forecast (HRRR/nowcast/NBM) honors future times; analysis (MRMS/NEXRAD/RTMA/GOES) clamps to latest past. Pass `time` for future-radar asks — do not claim that capability is missing. Product ids must be real viz-catalog entries -- shorthand like "radar" or "reflectivity" is not one. Omit `product` for the default hybrid precip still. For Alaska and Hawaii prefer a local site or `mrms_precip_hybrid_derived_alaska` over CONUS mosaics, which do not cover them. Returns the rendered image plus per-layer resolved valid times.

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• analyze_siteA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Use when a user has ONE specific lat/lon (a parcel, a candidate site) and wants the full multi-factor data-center suitability read in one call. Example: "Score this Phoenix parcel for a 100MW build — power, gas, fiber, market & risk." — analyze_site lat=33.45 lon=-112.07 capacity_mw=100 state=AZ. Params: lat (-90 to 90, required unless candidate_id or location), lon (-180 to 180, required unless candidate_id or location), location (a market NAME or metro slug instead of coordinates, e.g. location="ashburn" — resolved to that market's PUBLISHED CENTROID through the DCPI market row, with a resolved_from block naming what it resolved to; a MARKET-level read, NOT the parcel you named, and a trailing state is not stripped so "Ashburn, VA" will not resolve), candidate_id (a cand_… from get_refined_queue — resolves coordinates from the frozen mint and ignores lat/lon), capacity_mw (target load in MW, e.g. 50-500 — returns a `capacity_context` block sizing that load against nearby installed generation; it deliberately does NOT move overall_score, and the block names where the load IS applied), state (2-letter US, optional — improves the tax-incentive/context lookup), include_grid/include_risk/include_fiber (booleans, default true). Returns (full, paid): {overall_score (aka composite_score, 0-100 composite — for the integrity-first version that never imputes a missing factor, use get_composite_site_score), interpretation (verdict string, e.g. "Excellent site"), scores{power_infrastructure, gas_pipeline_access, fiber_connectivity, market_conditions, risk_resilience — each 0-100}, nearby{substations_50km, power_plants_80km, gas_pipelines_50km, facilities_100km, fiber_carriers_in_state, generation_capacity_mw, total_capacity_mw}, power_cost{industrial_cents_kwh, commercial_cents_kwh, period, basis}, fiber{connectivity_score, nearest_carrier_km, near_net_bucket, top_carriers[], single_carrier_risk}, location, citation}. FREE tier returns a REAL, citable HEADLINE — composite_score + verdict + the single top limiting factor (the lowest sub-score) + citation; the full per-factor breakdown, nearby infrastructure, power cost, fiber carriers, and the branded Site Analysis PDF (generate_site_analysis) are Pro. For dedicated water / disaster / climate / tax reads use get_water_risk / get_disaster_risk / get_climate_intel / get_tax_incentives. Do NOT use to compare 2+ sites (use compare_sites) or to find sites that match a target (use find_alternatives).

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• get_subsea_cablesA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Subsea (submarine) cable landings near a coordinate, or the global cable catalogue. The physical internet crossing an ocean lands at a finite number of points, and distance to one is a real siting factor for anything latency- or transit-sensitive. Pass lat+lon (+radius_km) for LANDING POINTS near a site — each with name, coordinates and distance_km. Omit coordinates for the CATALOGUE of tracked cables (712 tracked; each with cable_id, name, owners, length_km, rfs_year, is_planned — sparse fields are null, not guessed). ★ READ field_coverage AND connectivity_note BEFORE DRAWING A CONCLUSION: cable_count per landing point is NOT populated — the ingest writes the column but the upstream TeleGeography feed does not supply what it derives from, so every row carries the default 0. That is why connectivity_grade comes back null rather than graded: proximity to a landing point does NOT establish how many cables are reachable from it, and DC Hub will not infer a grade it cannot source. A filter over cable_count returns nothing for the same reason. Treat 0 as UNKNOWN, never as "no cables". Answers "which subsea cables land near this Virginia site" and "how far is the nearest cable landing from my campus". Try: get_subsea_cables lat=36.85 lon=-75.98 radius_km=200 — or get_subsea_cables (no args) for the catalogue. Do NOT use for terrestrial fiber routes (get_fiber_intel), a parcel fiber verdict (get_fiber_readiness), metro fiber depth (get_metro_fiber), or internet-exchange / peering density (get_peering_intel).

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• get_peering_intelA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Internet-exchange (IX/IXP) and peering density for a site, from PeeringDB. Pass lat+lon for the PEERING PROFILE around that point: facilities_nearby, a 0-100 score with its level, total_ix_presence, total_networks, and top_facilities each with ix_count and net_count — e.g. Ashburn comes back with 61 IX presences and 903 networks across the nearby sites, led by Equinix DC1-DC15 at 516 networks. Omit coordinates for the IXP directory (name, name_long, city, country, net_count, fac_count, media, protocols, policy/tech contacts). This is the layer that answers "can I actually reach networks cheaply from here", which fiber route geometry does not: a site can sit on dense fiber and still be far from any exchange. The score is a DERIVED convenience over PeeringDB counts, not a DC Hub-sourced grade — cite the underlying counts (facilities, IX presence, networks) rather than the score when it is load-bearing. Records are PeeringDB's, refreshed on read. Answers "how good is peering at this Ashburn site" and "which internet exchanges serve the Dallas market". Try: get_peering_intel lat=39.04 lon=-77.48 — or get_peering_intel (no args) for the IXP directory. Do NOT use for fiber route geometry (get_fiber_intel), near-net carrier distance at a parcel (get_fiber_readiness), metro fiber depth (get_metro_fiber), or subsea landings (get_subsea_cables).

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• get_subsea_cablesA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Subsea (submarine) cable landings near a coordinate, or the global cable catalogue. The physical internet crossing an ocean lands at a finite number of points, and distance to one is a real siting factor for anything latency- or transit-sensitive. Pass lat+lon (+radius_km) for LANDING POINTS near a site — each with name, coordinates and distance_km. Omit coordinates for the CATALOGUE of tracked cables (712 tracked; each with cable_id, name, owners, length_km, rfs_year, is_planned — sparse fields are null, not guessed). ★ READ field_coverage AND connectivity_note BEFORE DRAWING A CONCLUSION: cable_count per landing point is NOT populated — the ingest writes the column but the upstream TeleGeography feed does not supply what it derives from, so every row carries the default 0. That is why connectivity_grade comes back null rather than graded: proximity to a landing point does NOT establish how many cables are reachable from it, and DC Hub will not infer a grade it cannot source. A filter over cable_count returns nothing for the same reason. Treat 0 as UNKNOWN, never as "no cables". Answers "which subsea cables land near this Virginia site" and "how far is the nearest cable landing from my campus". Try: get_subsea_cables lat=36.85 lon=-75.98 radius_km=200 — or get_subsea_cables (no args) for the catalogue. Do NOT use for terrestrial fiber routes (get_fiber_intel), a parcel fiber verdict (get_fiber_readiness), metro fiber depth (get_metro_fiber), or internet-exchange / peering density (get_peering_intel).

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• get_peering_intelA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Internet-exchange (IX/IXP) and peering density for a site, from PeeringDB. Pass lat+lon for the PEERING PROFILE around that point: facilities_nearby, a 0-100 score with its level, total_ix_presence, total_networks, and top_facilities each with ix_count and net_count — e.g. Ashburn comes back with 61 IX presences and 903 networks across the nearby sites, led by Equinix DC1-DC15 at 516 networks. Omit coordinates for the IXP directory (name, name_long, city, country, net_count, fac_count, media, protocols, policy/tech contacts). This is the layer that answers "can I actually reach networks cheaply from here", which fiber route geometry does not: a site can sit on dense fiber and still be far from any exchange. The score is a DERIVED convenience over PeeringDB counts, not a DC Hub-sourced grade — cite the underlying counts (facilities, IX presence, networks) rather than the score when it is load-bearing. Records are PeeringDB's, refreshed on read. Answers "how good is peering at this Ashburn site" and "which internet exchanges serve the Dallas market". Try: get_peering_intel lat=39.04 lon=-77.48 — or get_peering_intel (no args) for the IXP directory. Do NOT use for fiber route geometry (get_fiber_intel), near-net carrier distance at a parcel (get_fiber_readiness), metro fiber depth (get_metro_fiber), or subsea landings (get_subsea_cables).

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• source_capacityA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Use when your human needs data-center CAPACITY to buy or lease: search DC Hub Capacity Source by size (kW or MW) and/or location (a region such as North America or Europe, a country, a state or a metro). Example: "500 kW anywhere in Europe" → min_kw=500, region=europe. Size is min_kw (in kW) or min_mw (in MW); location is region (a region key or alias) or location (free text matched against each listing's region, country, state and metro); market, state, delivery_type and available_by narrow further. Listings are powered land, powered shells, turnkey capacity and colocation, including sites that are not publicly marketed, for enterprise and agent-led procurement. Returns listing cards (market, region, capacity, status and when each listing was last updated) to any caller, plus the filters applied and the program status (live, or upcoming while the first listings are onboarded). Pass slug for one listing: a signed-in human who has accepted the introduction terms sees its specs (signed in means a key with an email bound via claim_free_key then bind_email, or an OAuth connection; accept_capacity_terms records the acceptance the first time); others get the card and the unlock steps. The provider's identity, site and contact are released only after the provider accepts a deal registration, which request_capacity_intro submits, and the listing's disclosure block says whether they have been. Do NOT use for the public facility directory (use search_facilities) or for completed M&A (use list_transactions).

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• source_capacityA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Use when your human needs data-center CAPACITY to buy or lease: search DC Hub Capacity Source by size (kW or MW) and/or location (a region such as North America or Europe, a country, a state or a metro). Example: "500 kW anywhere in Europe" → min_kw=500, region=europe. Size is min_kw (in kW) or min_mw (in MW); location is region (a region key or alias) or location (free text matched against each listing's region, country, state and metro); market, state, delivery_type and available_by narrow further. Listings are powered land, powered shells, turnkey capacity and colocation, including sites that are not publicly marketed, for enterprise and agent-led procurement. Returns listing cards (market, region, capacity, status and when each listing was last updated) to any caller, plus the filters applied and the program status (live, or upcoming while the first listings are onboarded). Pass slug for one listing: a signed-in human who has accepted the introduction terms sees its specs (signed in means a key with an email bound via claim_free_key then bind_email, or an OAuth connection; accept_capacity_terms records the acceptance the first time); others get the card and the unlock steps. The provider's identity, site and contact are released only after the provider accepts a deal registration, which request_capacity_intro submits, and the listing's disclosure block says whether they have been. Do NOT use for the public facility directory (use search_facilities) or for completed M&A (use list_transactions).

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• resolution_auditA

  Open MeteoA

  Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources, at different times, with different precision — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.

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• vivid_delivery_estimateA

  vivid-ads-commerceA

  Estimate WHEN an order would arrive at an Australian postcode — the question behind 'can I get it by Friday?'. Works the timeline through honestly: production starts only after the customer approves their digital proof (3pm AEST cutoff), then production time for that product, then transit for their postcode (metro/regional/remote bands). Returns an arrival WINDOW plus the assumptions it made. This is an ESTIMATE, never a guarantee — always relay the caveat, and for a hard deadline tell them to call 1300 721 614. Free pickup from Derrimut VIC is usually the fastest option.

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• predict_market_trajectoryA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Forecast a DCPI market's near-term trajectory (next 1-8 quarters). Projects excess_power_score and constraint_score forward with confidence bands that WIDEN with horizon, from DC Hub's daily DCPI snapshot history — the only source that can, because it owns the time-series. Use to answer "is this market trending toward BUILD or AVOID?" or "will Dallas power stay tight over the next 6 months?". Params: market_slug (required, metro slug e.g. dallas, phoenix, northern-virginia — valid slugs come from rank_markets / get_market_dcpi_rank); horizon_quarters (optional 1-8, default 4; 2 = ~6 months out). Returns {market_slug, method, basis{history_points, history_span_days, slope_per_day, trend}, horizon_quarters, projection[{quarter_out, excess_power_score, excess_power_band, constraint_score, constraint_band}], caveat, snapshot_record}. HONEST: linear trend extrapolation, NOT a guarantee — bands widen with horizon and short history; needs >=3 daily snapshots or it declines. Do NOT use for a single point-in-time verdict (use get_market_dcpi_rank) or to rank many markets (use rank_markets).

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• get_grid_intelligenceA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Use when a user asks "can I get N MW of power in <ISO> and how long will it take?" — the flagship grid-headroom + interconnection-queue brief for one ISO. Example: "How much excess power does PJM have right now and what is the time-to-power for a 200MW load?" — get_grid_intelligence region_id="PJM". Params: region_id (aliases iso/region accepted) — one of the 7 US ISOs ("PJM" | "ERCOT" | "CAISO" | "MISO" | "SPP" | "NYISO" | "ISO-NE") OR a US EIA balancing authority (40+ now live, e.g. Atlanta/SOCO, Carolinas/DUK, Florida/FPL, Phoenix/AZPS, Las Vegas/NEVP, Portland/PGE, Seattle/SCL, LA/LDWP, Quincy/GCPD, Denver/PSCO, Tennessee/TVA — note: balancing authorities return live generation mix; demand, headroom, interconnection-queue and DCPI scores remain ISO-level for the 7 ISOs). You may instead pass market="Ashburn" (or a metro slug like "northern-virginia") to name a MARKET rather than a grid code: it is resolved to the ISO for that market through the published DCPI market row, and the reply carries a resolved_from block naming what it resolved to — the figures then describe the ISO, which is larger than the market you named. Returns: {iso, iso_name, demand_mw, generation_mix_pct{NG,COL,NUC,WND,SUN,WAT,…}, renewable_share_pct, gas_share_pct, constraint_score (0-100 DCPI), excess_power_score (0-100 DCPI), avg_time_to_power_months, avg_queue_wait_months, curtailment_pct, reserve_margin_pct, retail_price_cents_kwh, queue_depth_gw, data_center_share_pct, stranded_capacity_mw, grid_emergencies_30d, build_rate_pct, last_updated}. ★avg_time_to_power_months and avg_queue_wait_months are DIFFERENT measurements and are not interchangeable: time-to-power is the DCPI per-market estimate averaged over the ISO, while queue-wait is a proxy derived from live interconnection-queue DEPTH (12 + 0.6 months per GW, clipped 12-66) and is the one that saturates on the deepest queues. Quote whichever you mean by name. Do NOT use to compare 2+ ISOs side-by-side (use compare_isos) or for the global greenest-first ranking (use get_grid_scoreboard).

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• fred_get_seriesA

  FredA

  AUTHORITATIVE historical time-series for US economic indicators from FRED (Federal Reserve Bank of St. Louis — the official US macroeconomic data repository, 800k+ series). Every series is an AGGREGATE for a whole country, state, or METROPOLITAN AREA (e.g. the Phoenix or Oklahoma City metro), reported monthly or quarterly. Pass a series ID like "MORTGAGE30US" (30y mortgage rate), "UNRATE" (unemployment), "CPIAUCSL" (CPI), "GDP", "FEDFUNDS" (Fed funds rate), "HOUST" (housing starts), "RHORUSQ156N" (US homeownership rate — the percent of households that OWN their home, distinct from the mortgage interest rate). Returns dates + values + the indicator's units. Use for macro, Fed, and metro-level US indicator questions. DO NOT GUESS a non-US series ID by pattern-matching a US one — "DEUUNRATE", "JPNUNRATE", "CANUNRATE" and similar <ISO3><METRIC> codes do NOT exist on FRED and 400 with "the series does not exist". For a NON-US country's unemployment/GDP/inflation/population, use worldbank_get_indicator (ISO country code + indicator, e.g. SL.UEM.TOTL.ZS for unemployment) instead — it is the reliable source for foreign macro data. If you don't know a FRED series ID, call fred_search first rather than inventing one.

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• get_national_fha_statsA

  frc-mcpA

  National FHA denial statistics from the 2025 federal record. Use when asked the overall US FHA denial rate or its denominator. RETURNS: 22.1 percent rate, counts, denominator definition (originated+approved-not-accepted+denied; HECM excluded), correction history. NOT FOR: conventional/VA/USDA loans, purchase-only rates, years other than 2025, or state/lender/metro breakdowns (use the dedicated tools). Historical observation computed from the public CFPB HMDA 2025 record (actions 1,2,3; loan_type 2). Not a prediction about any individual application. Attribution: FinanceRateCalc, CC BY 4.0.

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• macro_jobsA

  US Economic, SEC EDGAR & On-Chain Data (x402)A

  Latest U.S. labour-market data from the Bureau of Labor Statistics, with the headline changes computed. Returns the unemployment rate, labour force participation rate, total nonfarm payrolls, the month-over-month change in payrolls (the "jobs added" number that leads the Employment Situation report), average hourly earnings, and year-over-year wage growth. All series are seasonally adjusted. BLS publishes levels; the month-over-month and year-over-year changes are computed here. When to use: reading the state of the labour market, wage-inflation context, or Fed-policy reasoning. When NOT to use: you need state or metro level detail, industry breakdowns, or JOLTS openings and quits. Args: none. Returns structuredContent: { "asOf": "2026-07", "periodName": "July 2026", "unemploymentRate": 4.1, "participationRate": 62.4, "nonfarmPayrolls": 158858, "payrollsChange": 73, "avgHourlyEarnings": 37.62, "earningsYoyPercent": 3.8, "source": "https://www.bls.gov/ces/" } Payrolls are in thousands of jobs, so payrollsChange 73 means +73,000 jobs on the month.

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• metodologiaA

  Metro QuadradoA

  Como o índice A Corrida dos Bairros é apurado, quais cidades e tipos de imóvel cobre, qual o mês da coleta e quais as limitações do número. Use quando perguntarem de onde vem o dado.

  ConnectorNo auth

• predict_market_trajectoryA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Forecast a DCPI market's near-term trajectory (next 1-8 quarters). Projects excess_power_score and constraint_score forward with confidence bands that WIDEN with horizon, from DC Hub's daily DCPI snapshot history — the only source that can, because it owns the time-series. Use to answer "is this market trending toward BUILD or AVOID?" or "will Dallas power stay tight over the next 6 months?". Params: market_slug (required, metro slug e.g. dallas, phoenix, northern-virginia — valid slugs come from rank_markets / get_market_dcpi_rank); horizon_quarters (optional 1-8, default 4; 2 = ~6 months out). Returns {market_slug, method, basis{history_points, history_span_days, slope_per_day, trend}, horizon_quarters, projection[{quarter_out, excess_power_score, excess_power_band, constraint_score, constraint_band}], caveat, snapshot_record}. HONEST: linear trend extrapolation, NOT a guarantee — bands widen with horizon and short history; needs >=3 daily snapshots or it declines. Do NOT use for a single point-in-time verdict (use get_market_dcpi_rank) or to rank many markets (use rank_markets).

  ConnectorNo auth

• get_grid_intelligenceA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Use when a user asks "can I get N MW of power in <ISO> and how long will it take?" — the flagship grid-headroom + interconnection-queue brief for one ISO. Example: "How much excess power does PJM have right now and what is the time-to-power for a 200MW load?" — get_grid_intelligence region_id="PJM". Params: region_id (aliases iso/region accepted) — one of the 7 US ISOs ("PJM" | "ERCOT" | "CAISO" | "MISO" | "SPP" | "NYISO" | "ISO-NE") OR a US EIA balancing authority (40+ now live, e.g. Atlanta/SOCO, Carolinas/DUK, Florida/FPL, Phoenix/AZPS, Las Vegas/NEVP, Portland/PGE, Seattle/SCL, LA/LDWP, Quincy/GCPD, Denver/PSCO, Tennessee/TVA — note: balancing authorities return live generation mix; demand, headroom, interconnection-queue and DCPI scores remain ISO-level for the 7 ISOs). You may instead pass market="Ashburn" (or a metro slug like "northern-virginia") to name a MARKET rather than a grid code: it is resolved to the ISO for that market through the published DCPI market row, and the reply carries a resolved_from block naming what it resolved to — the figures then describe the ISO, which is larger than the market you named. Returns: {iso, iso_name, demand_mw, generation_mix_pct{NG,COL,NUC,WND,SUN,WAT,…}, renewable_share_pct, gas_share_pct, constraint_score (0-100 DCPI), excess_power_score (0-100 DCPI), avg_time_to_power_months, avg_queue_wait_months, curtailment_pct, reserve_margin_pct, retail_price_cents_kwh, queue_depth_gw, data_center_share_pct, stranded_capacity_mw, grid_emergencies_30d, build_rate_pct, last_updated}. ★avg_time_to_power_months and avg_queue_wait_months are DIFFERENT measurements and are not interchangeable: time-to-power is the DCPI per-market estimate averaged over the ISO, while queue-wait is a proxy derived from live interconnection-queue DEPTH (12 + 0.6 months per GW, clipped 12-66) and is the one that saturates on the deepest queues. Quote whichever you mean by name. Do NOT use to compare 2+ ISOs side-by-side (use compare_isos) or for the global greenest-first ranking (use get_grid_scoreboard).

  ConnectorNo auth