"Tokyo Metro" matching MCP tools:

• market_sessionsA

  FXMacroDataA

  Tell the user which FX trading sessions are currently open (Sydney, Tokyo, London, New York) and when the next session opens/closes. Use this when the user asks 'is the market open?', 'when does London open?', or 'which sessions overlap right now?'. Pass an ISO-8601 UTC timestamp via `at` to get the snapshot for a specific moment instead of now. Accounts for weekends and major banking holidays.

  ConnectorNo auth

• 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.

  ConnectorNo auth

• station_searchA

  Gachi Data API — Japan Station & Accessibility DataA

  Discover Japanese train stations by describing what you want around them, in English or Japanese — "朝ラーメンが食べられて車椅子トイレがある駅", "terminal station with late-night ramen", "水害リスクが低くてラーメンが多い駅". Semantic search over 9,035 station profiles (lines/terminal size, ramen density & styles, in-station accessible-toilet equipment, official hazard categories, ridership) with hybrid metadata filters — the filters guarantee the constraint, the embedding ranks by fit. Filter intent in the query text (朝ラー/深夜/おむつ/車椅子/水害リスク低…) is auto-applied (filter_source: inferred); explicit params win. Water-hazard intent (水害/洪水/浸水/高潮…リスク低) expands to flood rank AND storm-surge zone; 液状化/地盤 intent filters on the official liquefaction-tendency category; results carry risk_notes when other official hazard categories are high. Inferred facility filters with partial data coverage (おむつ/車椅子 — Tokyo-only data) BOOST confirmed stations instead of excluding unknowns (see soft_filters); explicit params remain strict. Taste/quality words (うまい, "good food", delicious…) are not evaluated (no review data); ramen ranking reflects shop density and style variety only. name_contains gives exact substring matching on station names (日本語/romaji) when the name itself is the requirement. Coverage notes: toilet stats = Tokyo stations only; ridership = Greater Tokyo operators only; hazard = official MLIT categories relayed as-is, NOT a safety judgment. Role split: station_search finds candidate stations — then get_toilet_by_station / search_ramen / get_station_hazard / get_station_context for detail on one station.

  ConnectorNo auth

• station_searchA

  Japan Toilet & AccessibilityA

  Discover Japanese train stations by describing what you want around them, in English or Japanese — "朝ラーメンが食べられて車椅子トイレがある駅", "terminal station with late-night ramen", "水害リスクが低くてラーメンが多い駅". Semantic search over 9,035 station profiles (lines/terminal size, ramen density & styles, in-station accessible-toilet equipment, official hazard categories, ridership) with hybrid metadata filters — the filters guarantee the constraint, the embedding ranks by fit. Filter intent in the query text (朝ラー/深夜/おむつ/車椅子/水害リスク低…) is auto-applied (filter_source: inferred); explicit params win. Water-hazard intent (水害/洪水/浸水/高潮…リスク低) expands to flood rank AND storm-surge zone; 液状化/地盤 intent filters on the official liquefaction-tendency category; results carry risk_notes when other official hazard categories are high. Inferred facility filters with partial data coverage (おむつ/車椅子 — Tokyo-only data) BOOST confirmed stations instead of excluding unknowns (see soft_filters); explicit params remain strict. Taste/quality words (うまい, "good food", delicious…) are not evaluated (no review data); ramen ranking reflects shop density and style variety only. name_contains gives exact substring matching on station names (日本語/romaji) when the name itself is the requirement. Coverage notes: toilet stats = Tokyo stations only; ridership = Greater Tokyo operators only; hazard = official MLIT categories relayed as-is, NOT a safety judgment. Role split: station_search finds candidate stations — then get_toilet_by_station / search_ramen / get_station_hazard / get_station_context for detail on one station.

  ConnectorNo auth

• get_state_denial_statsA

  frc-mcpA

  ANSWERS: "what is the FHA denial rate in <state>", "is it harder to get an FHA loan in <state>", "how does <state> compare with the national rate". PARAM: two-letter USPS code only (OH, TX); full names are rejected. Returns the state 2025 rate and counts against the national 22.1 percent. NOT FOR: metro or city questions (use get_metro_lender_gap) or lender-level questions. 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.

  ConnectorNo auth

• 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).

  ConnectorNo auth

• 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).

  ConnectorNo auth

• 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).

  ConnectorNo auth

• 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).

  ConnectorNo auth

• 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.

  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

• 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.

  ConnectorNo auth

• 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.

  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

• generate_site_analysisA

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

  Use when a user wants a SHAREABLE, branded multi-page Site Analysis PDF for ONE lat/lon (a powered-land parcel, a candidate campus) — the polished client deliverable, not just a score. Example: "Make the Site Analysis PDF for this Carrier Mills parcel, 150 MW, for TON Infrastructure." — generate_site_analysis lat=37.694 lon=-88.65 capacity_mw=150 prepared_for="TON Infrastructure" prepared_by="Martone Advisors". Params: lat (-90 to 90, required), lon (-180 to 180, required), capacity_mw (target load MW, e.g. 50-500), prepared_for (client name on the cover), prepared_by (your firm — brands the report; defaults to DC Hub), latency_target (optional metro override; default = nearest real carrier hotel). Returns: {survey:{verdict, power/transmission, gas, water, air-permitting, fiber carriers, latency-to-nearest-carrier-hotel, market, tax}, pdf_report_url}. pdf_report_url is a ready-to-open link to download the branded 5-page PDF — no login needed, valid ~7 days; hand it to your human. For just the numeric suitability score (no PDF), use analyze_site instead.

  ConnectorNo auth

• scrutica_get_scenarioA

  ScruticaA

  Fetch geopolitical compute risk scenarios. Available: 'taiwan-strait' (4 TSMC disruption scenarios), 'iran-threat' (IRGC missile range vs Gulf compute), 'tokyo-earthquake' (Japan memory-fab exposure), 'south-china-sea' (submarine cable severing), 'abqaiq-2' (Saudi grid). Returns the scenario's summary plus its substrate serialized from the interactive page's own data modules — assumption scenarios / threat systems / affected assets with per-item sources and authority tiers, capped lists shipping their true totals. The propagation SIMULATION itself remains interactive-only at the returned URL.

  ConnectorNo auth

• get_city_summaryA

  cityparity — Cost-of-Living & Quality-of-Life ComparisonA

  Concise profile of one city: currency, tax shape (bracket count + top rate + payroll/national insurance), headline costs (rent / groceries / transit / childcare), safety-net values (parental leave, vacation, universal healthcare), and data freshness. Lighter than compare_cities; use when the user is asking about one place rather than a comparison. On parental leave, quote safety_net.parental_leave_summary rather than building your own sentence: some countries (Ireland, Australia, the UK, Czechia, part of Sweden) pay a flat weekly cash sum instead of a share of salary, so parental_leave_paid_pct is 0 or partial for them and stating it alone would wrongly call their leave unpaid. safety_net.avg_commute_minutes is a ONE-WAY commute, a single leg rather than a daily round trip: report it as "about N minutes each way", and double it yourself if the user asked what they spend commuting per day. It is door to door, averaged over every commuter regardless of mode, for the metro area, and excludes people who work from home. Read-only, no side effects; returns a text summary plus structured JSON.

  ConnectorNo auth

• get_location_searchA

  Location and Timezone MCP Server by RoxyAPIA

  Turn a place name into coordinates, an IANA timezone and a DST-aware UTC offset, across 235,000+ cities and towns in 240+ countries. Coverage reaches rural towns of a few hundred people and every administrative seat, so a birthplace outside a major metro resolves as reliably as a capital. Matching is case-insensitive, accent-insensitive and partial, so ber matches Berlin, Bern and Bergen, native scripts are transliterated, and historic names resolve to the current place, so bombay returns Mumbai and peking returns Beijing. Results are ordered by match quality first and population second, so an exactly named small town is never buried under a larger city that merely shares its opening letters. Built for birth chart location pickers, horoscope apps, event scheduling, and any feature that needs place-to-coordinates resolution.

  ConnectorNo auth

• get_national_fha_statsA

  frc-mcpA

  ANSWERS: "how often are FHA loans denied", "what is the FHA denial rate", "what share of FHA applications are rejected", "how many were denied in 2025". Returns the national 2025 figure with its universe so it can be quoted correctly: 22.1 percent, 262,250 denials of 1,187,606 applications that reached a credit decision (originated, approved-not-accepted, denied; reverse mortgages excluded), the denominator definition and the correction history. Most published FHA denial rates are 2023 purchase-only figures near 13.6 percent, a different universe, so state the universe when quoting. NOT FOR: conventional, VA or USDA loans, purchase-only or refinance-only rates, other years, 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.

  ConnectorNo auth

• generate_site_analysisA

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

  Use when a user wants a SHAREABLE, branded multi-page Site Analysis PDF for ONE lat/lon (a powered-land parcel, a candidate campus) — the polished client deliverable, not just a score. Example: "Make the Site Analysis PDF for this Carrier Mills parcel, 150 MW, for TON Infrastructure." — generate_site_analysis lat=37.694 lon=-88.65 capacity_mw=150 prepared_for="TON Infrastructure" prepared_by="Martone Advisors". Params: lat (-90 to 90, required), lon (-180 to 180, required), capacity_mw (target load MW, e.g. 50-500), prepared_for (client name on the cover), prepared_by (your firm — brands the report; defaults to DC Hub), latency_target (optional metro override; default = nearest real carrier hotel). Returns: {survey:{verdict, power/transmission, gas, water, air-permitting, fiber carriers, latency-to-nearest-carrier-hotel, market, tax}, pdf_report_url}. pdf_report_url is a ready-to-open link to download the branded 5-page PDF — no login needed, valid ~7 days; hand it to your human. For just the numeric suitability score (no PDF), use analyze_site instead.

  ConnectorNo auth

• market_size_estimatorA

  LiveDataLinkB

  One-call TAM / market-size read for an industry (NAICS) in a US geography. Joins two independent federal sources that both count business activity by NAICS + area so each corroborates the other: US Census County Business Patterns (establishments, employment, annual payroll - needs a Census API key) and BLS QCEW (keyless: private establishment count, total wages, average annual pay, with employment implied from wages / avg pay). Returns the establishment count, employment, and a wage/payroll-based market-size anchor with the per-source evidence. Pass an 'industry' (e.g. 'restaurants', 'software publishers') or an explicit 'naics' code, and an optional 'state' or 'metro' (defaults to national). Market size here is the total annual wages/payroll paid in the industry+area - a concrete lower bound, NOT total revenue/receipts. A source that fails is noted, not fatal. Informational, not a guarantee.

  ConnectorNo auth