DataPorium MCP Server

Investor presentations (slide decks with links) or press releases of a company.

Logo image URL of a company.

Company profile and fundamentals: sector, industry, description, address, employees, market cap, valuation ratios, dividends, analyst targets and more (also used by the stock screener).

Healthcare market data: drug/brand revenue by quarter (brand_revenue, filter symbol), company revenue by healthcare industry, market size by country/region, company market share, industry market size and therapy-area market size. industry values: Pharmaceutical, Medical Device, Diagnostic Life Science, Consumer Healthcare, Health Insurance, Healthcare, Distributor, Veterinary, and others. Drug pipeline (official US government data, updated daily): "drug_approvals": US drug approvals, newest first: original approvals of new drugs (NDA), biologics (BLA) and generics (ANDA), tentative approvals, and new uses (efficacy supplements); novel_drug = first approval of a new active ingredient. Filter by symbol (the listed company that holds the drug today), query (drug or company name), date_from/date_to; filters e.g. {"novel_only": true, "application_type": "NDA,BLA", "approval_kind": "original"}. Also covers vaccines, blood products, allergenics and cell and gene therapies (filters {"center": "CBER", "product_category": "gene therapy"}). Set jurisdiction to another country code (EU, GB, JP, CN, CA, CH, FR, ES, ...) for that country's drug approvals, which have the fields the country publishes (some dates are a year only; see date_precision); more filters: generic, company, year_from, year_to, type, review, kind, form. "drug_approval_summary": counts by group_by (year, month, application_type, category, approval_kind or symbol). "drug_approval_companies": listed companies with their approval and novel drug counts (sort="novel_drugs"). "clinical_trials": registered clinical studies worldwide; filter by symbol (listed lead sponsor), query (words in title, conditions and drugs, e.g. "obesity semaglutide"), phase (early1, 1, 1/2, 2, 2/3, 3, 4, na), status (active, recruiting, completed, stopped, ...), date_from/date_to (start date); filters e.g. {"condition": "Alzheimer", "sponsor": "Mayo Clinic", "country": "Germany", "has_results": true}. "clinical_trial_summary": counts by group_by (phase, status, start_year, study_type, sponsor, symbol, condition or country) with active, recruiting, completed, stopped and phase 3 counts. "clinical_trial": the full record of one study by trial_id (e.g. "NCT04184622"): design, dates, enrollment, eligibility criteria, primary and secondary outcomes and every site. Drug side-effect reports (official public data, summed per active ingredient by year received; added 10/09/2026): "drug_events" with query = a drug (generic or brand name, e.g. "apixaban" or "Eliquis", or an application number): totals, reports by year (all, serious, deaths, hospitalizations; the current year is partial), the 25 reactions named most often over all years and in the last 5 years, patient sex and age groups, and the linked Medicare drug pages. With group_by "reactions" and query: the drug's full reaction list (filters {"period": "recent"}). Without query: drugs ranked by reports (symbol = one listed company's drugs; filters {"event_type": "death", "year": 2024, "q": "statin"}). filters {"reaction": "nausea"}: the drugs with the most reports of that reaction. A report does not prove that the drug caused the effect, and counts depend on how widely a drug is used: they are not risk rates. Say so whenever you show these numbers. A drug not read yet answers "Data is being prepared". Regulatory pathways (how medicines and devices get approved; reviewed content, each with last_verified and a confidence level; not legal or regulatory advice): "regulatory_markets": the covered markets (codes like US, EU, GB, JP, CN, DE, CA, CH), segments and the shared pathway types. "regulatory_pathways": pathway cards; filter jurisdiction (e.g. "US" or "DE"; EU members include the EU-wide routes) and segment (drug, biologic, biosimilar, generic, otc, vaccine, atmp, combination, device, ivd, samd); filters e.g. {"pathway_type": "conditional", "device_class": "II", "expedited_only": true}. "regulatory_pathway": one pathway by pathway_id (from regulatory_pathways): steps with official day targets and clock stops, requirements, time targets, fees, reliance on other regulators, device classes, approvals per year; where published also scenarios (paths through decision points), the document checklist per step and the after-approval duties (lifecycle). "regulatory_compare": jurisdiction = 2 to 8 codes separated by commas (e.g. "US,EU,JP"), optional segment; routes lined up by pathway type with target days and fees. "regulatory_approvals": approvals from official regulator data across markets (filter jurisdiction, segment, symbol, query, date_from/date_to; filters e.g. {"pathway_type": "biosimilar", "new_active_only": true, "expedited": "conditional"}). "regulatory_approval_stats": counts with group_by (year, jurisdiction, segment, pathway_type, product_class, expedited, therapeutic_area; two with a comma, e.g. "year,pathway_type"). "regulatory_review_times": actual review times per year, only where the official data has both dates. "regulatory_first_approvals": first approval date of a substance in each market (query = substance name). "regulatory_companies": listed companies ranked by approvals (jurisdiction or ALL). "regulatory_fees" and "regulatory_changes": official fees and rule changes or pending reforms. Testing and technical requirements (reviewed, with last_verified and confidence; standards named by code only): "regulatory_technical": the testing each market asks for, by topic (biocompatibility, bench_performance, electrical_safety, emc, software, cybersecurity, usability, sterilization, packaging_shelf_life, risk_management, clinical_investigation, performance_ivd; for medicines stability, analytical_validation, impurities, specifications, bioequivalence, bridging, lot_release, gmp): what must be done, test endpoints, standards, local_test (a test in a local laboratory or sample testing), whether foreign reports are accepted, what the protocol and the report must contain, and the comparison with the US, the EU and other reference markets. Filter jurisdiction (EU members include EU-wide rules), segment, pathway_id (the rows shown on that pathway page), query; filters e.g. {"topic": "biocompatibility", "device_class": "II", "local_test": "required", "standard": "ISO 10993-1", "scope_tag": "implant"}. "regulatory_technical_compare": jurisdiction (one or more codes) against reference markets with difference_type (same, stricter, lighter, extra_local_test, different_edition, not_required, accepts_reference_report, different_approach) and a plain explanation; group_by "topic" or "summary"; filters {"reference": "US,EU", "topic": "...", "difference_type": "extra_local_test"}. "regulatory_standards": standards (ISO, IEC, ICH, national) and which markets recognize, harmonize or adopt them with the edition accepted and the national code; filter jurisdiction, query; filters {"topic": "...", "family": "device", "standard": "ISO 14971"}. The drug pipeline and regulatory answers have website_link: share it with the user.

The full profile of ONE healthcare entity, with every mapped link (all by keys, each with match_method and confidence). Give exactly one of: npi: a provider (10 digits): registry record with all specialties and the practice address, Medicare enrollment (specialty, medical school, graduation year, telehealth), group practices with their health system, and hospital affiliations; for an organization NPI the hospital (CCN) or group practice it belongs to. ccn: a hospital (6 characters, e.g. "240010"): profile, beds, star rating, organization NPIs, health system and listed owner, yearly financials (net patient revenue, operating and net income and margins, discharges, occupancy, uncompensated care) for every loaded fiscal year, quality measures with national medians, affiliated clinicians. system: a health system ID or slug (e.g. "hca-healthcare"; find it with search_healthcare_providers kind="health_systems"): member hospitals, financial roll-up by year, linked group practices and the 2023 profile. group_pac_id: a Medicare group practice (10 digits): organization NPIs and its clinicians. Medicare parts (newest year in detail plus totals per year): for an npi "procedures" (top codes, payments), "lab_tests", "prescriptions" (top drugs, cost, opioid claims) and "payments" (money from drug and device companies: by year, company, nature and product); for a ccn "procedures" (stays per DRG and outpatient services per APC with the national average payment) and "payments" (teaching hospitals); for a system "procedures" and "payments". Labs and devices: "labs" (certified labs linked to the NPI, to the hospital, or the system's hospital labs) and for a ccn "device_procedures" (device-heavy stays by DRG and outpatient services by APC, per year). include: optional list of parts, e.g. ["financials", "quality"] for a hospital or ["groups", "hospitals"] for a provider, ["prescriptions"] for what a doctor prescribes (default: all parts). The answer has website_link: share it with the user.

US healthcare providers, hospitals and health systems from official US government data, linked by keys (NPI for providers, CCN for hospitals, PAC ID for group practices, a system ID for health systems). kind "hospitals" (default): every active Medicare hospital with type, ownership, beds, star rating (1 to 5), health system, listed owner (ticker) and the latest yearly financials; filter by q (name or city), state, city, zip, hospital_type (e.g. "Short-term acute care", "Critical access", "Psychiatric", "Children's"), ownership, system (ID or slug), ticker (e.g. HCA, THC, CYH, UHS); sort e.g. "net_patient_revenue" (default), "beds", "star_rating", "net_income", "operating_margin_pct". kind "health_systems": systems with hospitals, beds, clinicians, revenue roll-ups and listed owner; q, state, ticker. kind "providers": every provider with an NPI (doctors, nurse practitioners, clinics, pharmacies and more): q (name or NPI), specialty (e.g. "cardiology"), entity_type, state, city, zip, medicare=true for Medicare clinicians. kind "groups": Medicare group practices with their organization NPI and linked health system. kind "hospital_financials": yearly cost report financials per hospital (fiscal_year, state, system, ticker). kind "hospital_quality": quality measures (measure e.g. "mortality_heart_attack", "readmission_heart_failure", "would_recommend", "infection_c_diff", "ed_time_minutes") with national medians; best first by default. kind "provider_counts" / "hospital_counts": totals grouped by group_by (providers: state, specialty, medicare_specialty, entity_type; hospitals: state, hospital_type, ownership, star_rating, system, teaching). kind "specialties": specialty names and codes with provider counts. Medicare use and money (newest data year by default; year= for another): kind "procedures": Medicare services per provider and procedure code: give npi (a doctor's codes) or code (e.g. "99213", "G0439"; the providers billing it, with state). kind "procedure_codes": codes ranked by Medicare payments nationally or in a state (q = code or category words). kind "code_profile": one code (code=) by year and state. kind "procedure_providers": providers ranked by Medicare payments (state, specialty = provider type). kind "lab_tests" / "lab_test_providers": lab test codes with the lab fee schedule rate / labs ranked. kind "hospital_procedures": hospital stays per DRG (inpatient) and outpatient services per APC per hospital (ccn, code, state, system, ticker; filters {"setting": "outpatient"}); "hospital_procedure_codes": DRG/APC national totals. kind "prescriptions": Medicare drug claims and cost per prescriber and drug (npi or drug, e.g. "eliquis"). kind "drugs": drugs ranked by cost nationally or in a state (q, ticker). kind "drug_profile": one drug (drug=): totals by year and state, spending by manufacturer, top prescribers, Medicaid use, listed company. kind "prescribers": prescribers ranked. kind "drug_spending": spending by drug, manufacturer or listed company (group_by drug|manufacturer|company|year; ticker). kind "medicaid_drugs": Medicaid prescriptions and amount per drug product in a state (default national) and year (filters {"quarter": 1}). kind "payments": company payments to doctors and teaching hospitals (npi, ccn, ticker or filters {"payer": id}); "payment_companies": paying companies ranked (q, ticker); "payment_company": one company (q = name or ID); "payment_recipients": doctors and hospitals ranked by payments received. kind "company_summary": a listed company's (ticker=) Medicare drug spending and payments to doctors, its certified labs and its device approvals, recalls and adverse events. Diagnostics and labs: kind "labs": every certified US lab (about 300,000, all payers) with lab type, segment (independent_reference, pol = physician office, hospital, other), certificate, accreditations, yearly test volume, listed company (ticker, e.g. DGX, LH) and NPI/CCN links; q (name or lab number), state, city, zip, ticker, filters {"segment": "pol", "certificate_type": "accreditation", "accreditation": "CAP", "lab_type": "Pharmacy"}. kind "lab_counts": lab counts and test volumes by group_by segment, lab_type, certificate_type, state, volume_band, accreditation, company, segment_state, year or segment_year (trend). kind "lab_companies": lab companies with labs, states, volumes and Medicare lab payments. kind "lab_profile": one lab (q = 10-character lab number). kind "lab_test_segments": Medicare lab tests by group_by segment (independent_lab, pathology_practice, physician_office, hospital_setting, other), category (chemistry, hematology, immunology, microbiology, molecular_infectious, molecular_genetic, pla, pathology, toxicology, ...), year, place, segment_year, category_year, segment_category or code (with fee schedule rate and private insurer median). kind "lab_private_prices": private insurer lab prices (2016 reporting) next to the Medicare rate (code, q). Medical devices: kind "device_procedures": Medicare procedures that use devices by group_by category (e.g. joint, spine, cardiac_rhythm, structural_heart, coronary, neurostim, ophthalmic, imaging), subcategory, code, place (office, asc, hospital_outpatient, hospital_inpatient, ...), provider, year, category_year or category_place (code, year, filters {"category": "joint", "place": "asc"}). "How many knee replacements": kind "device_procedures", group_by "code", q "27447". kind "device_procedure_states": the same by state (code or filters {"category": ...}). kind "device_hospital_procedures": device-heavy hospital stays (DRG) and outpatient services (APC) by group_by category, code, state, hospital or year (ccn, state, system, ticker, filters {"setting": "inpatient"}). kind "device_procedures_icd": CALIFORNIA ONLY, all payers: inpatient procedure counts by ICD-10-PCS code with official descriptions, category and device category (group_by code, category, device_category, year; q = words or code prefix); its answer carries a required source line in "attribution": always show that line with these numbers. kind "medical_equipment": Medicare equipment and supplies (glucose monitors, sleep apnea machines, wheelchairs, braces) by group_by code, category, state or year; kind "equipment_referrers": doctors ordering the most. kind "devices": device product codes with class and counts. kind "device_approvals": 510(k) clearances, De Novo grants and PMA approvals (ticker, q, year, filters {"pathway": "pma"}; group_by year, pathway, company, year_pathway). kind "device_recalls": recalls (ticker, filters {"recall_class": "I"}; group_by year, class, root_cause, company). kind "device_adverse_events": adverse event reports by group_by year, product_code or company (ticker, filters {"event_type": "death"}). kind "device_companies": listed device makers with approval, recall and event totals; "device_company": one maker (ticker=, e.g. MDT, ABT, BSX). For one provider, hospital, system or group in full, use get_healthcare_provider. Extra filters: list_filters. The answer has website_link: share it so the user can see the data on dataporium.tech.

Full details of ONE US area in one call: the housing market, property tax, crime, demographics, climate and environmental risk, air quality and the neighborhood profile (schools, amenities, walkability, noise). Name the area with zip, or city + state, or county + state, or metro (e.g. 'Los Angeles, CA'), or just state; a state can be 'CA' or 'California'. Pick the parts with include: market (home value and rent levels, 1-year changes, forecast, homes for sale, and for metro areas also inventory, days to pending, price cuts and affordability), market_history (the long monthly time series of values and rents: only added when you ask for it, newest 24 months by default, use months or date_from/date_to for other periods), tax, crime, demographics, environmental_hazards, climate_risk, air_quality, neighborhood_profile, elections (how the area voted in the latest presidential elections, Senate and governor races, partisan lean and turnout: "how does X vote?"), jobs (unemployment rate by month and average weekly pay: "what is the unemployment rate in X?"), permits (new homes approved by building permits: "how many new homes are being built in X?"), migration (people moving in and out, net, top origins and destinations: "are people moving to X?") and gas_prices (weekly pump prices of the closest area the survey covers, plus the daily local price of the county, metro area or state under daily_local) and industries (businesses, employees and payroll by industry for the newest year: the top sectors and industry groups, "what are the main industries in X?"; a ZIP code gives businesses by sector) and official_rents (the official yearly rents for studio to 4-bedroom homes, with the 2-bedroom rent over the years: "what is the official rent for a 2-bedroom in X?"; a metro ZIP code has its own, a city uses its county, a state gets the median of its counties; this is not the market rent of 'market': show the two separately, never mix them) and flood_insurance (insured flood losses: claims, payouts, policies and premium by year, "how many flood claims were there in X?"; a ZIP code has its own, a city uses its county) and disasters (declared disasters by county or state: the latest ones, counts by incident type and year, "how many hurricanes hit X?"; a ZIP code or city uses its county) and mortgages (home loan applications, loans made, approval and denial rates, median loan amount and income, denial reasons, the trend by year and the top lenders: "how many mortgages are denied in X?", "who lends the most in X?"; a ZIP code or city uses its county) and ev_charging (open public EV charging stations and ports, Level 2 and DC fast, the main networks, stations opened per year and the state's EV registrations: "how many EV chargers are near X?"; a ZIP code also gets the stations within 5 miles and its county) and colleges (the area's colleges: how many, students, public and private, median tuition, net price, completion, debt and earnings 10 years after entry, the largest and the best paid: "what colleges are in X?"; a ZIP code or city also gets its county) and nearby_facilities (facilities with workplace safety or environmental compliance records or greenhouse gas reports: how many, the ones with the largest penalties in the last 5 years and the largest emitters; a ZIP code also the nearest within 3 miles: "are there polluting plants near X?"). Jobs, permits and migration of a ZIP or city use its county (jobs use the city itself when it has 25,000+ people). Without include you get market, tax, crime, demographics, climate_risk, neighborhood_profile and jobs as a compact summary. A part with no data for that area says available false with the reason (crime and demographics for a ZIP use the ZIP's city). When you present crime data, quote the source_note that comes with it: it says whose figures these are (the place's own police department, a county sheriff, or a neighboring city when the place has none) and, for a ZIP code, that the figures are for the city. The answer has website_link to see the homes of the area on the map at dataporium.tech: share it. To compare or rank many areas use search_housing_areas; to list the homes of an area use search_properties.

Find and rank many areas at once (states, counties, metros, cities or ZIP codes) from a ready-made scorecard of market, price, rent, crime, income and affordability numbers. START HERE for broad real-estate questions such as where to buy, rent out or live. One level per call, ranked on home value and its 1-year change, rent and rent yield, price forecast, market speed (metros), homes for sale, crime, population and income, unemployment, air quality, flood and earthquake risk, weather and property tax, and the local economy: local_unemployment_rate_pct (newest month), avg_weekly_wage, new_homes_permitted_12m and net_migration_per_1000 (people moving in minus out per 1,000 residents; county level for ZIPs and cities). filters: {"field": [min, max]}, e.g. {"gross_rent_yield_pct": [7, null], "population": [50000, null], "crime_rate_per_1000": [null, 30]}. states: ["TX", "FL"]. flood_risk: ["Low"]. Call describe_screen_fields("areas") first to learn the fields, units and typical ranges. For the full detail of ONE area use get_housing_area_insights; to list the homes of an area you picked use search_properties. The crime rate of a place is that of the police agency that serves it, which can be a neighboring city or the county sheriff: get_housing_area_insights names the agency in source_note.

Today's biggest stock gainers, losers or most active stocks.

Latest market-wide news by topic (general, stock, forex, crypto).

Find M&A deals across the market (default: the 12 months up to the newest deal). stage e.g. ["Completed"] or ["Announced/Pending"]; sectors and countries are partial matches (e.g. target_sector ["Health Care"], target_country ["United States"]); values in USD millions. Compact rows (date, buyer, target, tickers, value, stage, sectors, countries); use get_ma_transactions for full details of one deal. Private fundraising (official public data, 2009 on, updated daily): private (unregistered) offerings of US and foreign companies and funds, one notice per filing, each offering shown once at its newest filing by default. view "private_fundraising": rounds newest first (sort_by "value" = largest amount sold): issuer, industry group, state or country, offering amount, amount sold and remaining (US dollars, as stated), investors, minimum investment, first sale date, exemptions (506b, 506c, 504, 3c1, 3c7, ...), fund or operating company, ticker if listed. query = part of the issuer name or its company id; date_from/date_to filter the filing date (YYYY-MM-DD, YYYY-MM, YYYYQn or YYYY); filters e.g. {"industry": "Biotechnology", "state": "CA", "kind": "operating", "min_sold": 10000000, "exemption": "506c", "fund_type": "venture_capital_fund", "revenue_range": "1m_to_5m", "ticker": "NVDA", "listed": true, "filings": "all", "sort": "investors"}; list_filters('screen_ma_deals') shows all. view "private_fundraising_company": one issuer (company = slug or company id from the rows) with its offerings and every filing over time. view "private_fundraising_totals": totals by quarter (default; by), year, month, industry, category, sector, state, country, fund_type, exemption, revenue_range, entity_type or none: new offerings, issuers, funds vs operating companies, amount sold, offering amount and medians; filters as above plus {"basis": "latest"|"all"}. About half of all filings are pooled funds and a few large funds dominate the sums: use {"kind": "operating"} for companies only. These are notices of offerings, not verified results.

START HERE for broad stock questions (no specific company named). Ranks ~45,000 companies in one call. filters: {"field": [min, max]} using fields from describe_screen_fields("stocks"), e.g. {"market_cap": [2e9, null], "forward_pe": [5, 15], "revenue_growth_pct": [10, null], "debt_to_equity": [null, 1]}. Percent fields are in percent (10 = 10%). sector/industry/country/exchange: lists, partial match (e.g. sector ["Technology"], country ["United States"]). sort_by any field, e.g. "fcf_yield_pct". Returns at most 50 rows plus total_matches; use offset for the next page.

Recent news articles about one company or crypto coin (full text, sentiment, tags). ticker e.g. AAPL or BTC-USD.

Price history (open, high, low, close, volume) plus dividends of a stock, ETF or index. interval '1d' = daily bars (full history available), '5m' = 5-minute bars of about the last month. points = how many of the newest bars to return (default 250, about one trading year of daily bars; up to about 500 fit in one answer). ETFs and indexes work like stocks, e.g. SPY, VOO, QQQ, DIA, IWM, VTI, bond funds TLT, IEF, SHY, AGG, BND, HYG, LQD, sector funds XLK to XLRE, GLD, IAU, SLV; indexes start with a caret: ^GSPC (S&P 500; ^SPX also works), ^DJI, ^IXIC, ^NDX, ^RUT, ^VIX, ^TNX. search_symbols finds the ticker of a fund or index by name.

US airline on-time performance and passenger traffic by month (official public data): on-time %, delays 15+ minutes, cancellations, diversions, average arrival delay and delay causes (since 2010, scheduled domestic flights of the large US airlines and their regional partners, counted at the departure airport), and passengers, seats, departures and load factor per airline, airport and non-stop route (since 2015, US domestic and international, US and foreign airlines). Monthly data, about 2 to 3 months behind. Listed companies by ticker (DAL, AAL, UAL, LUV, ALK, JBLU, ALGT, ULCC, SKYW, SNCY, CPA, VLRS ...). view "airlines" (default): airlines ranked for a period (period = latest, ttm, 2025 or 2025-07) by sort = on_time_pct (default), cancelled_pct, delayed_pct, avg_arrival_delay_min, flights, passengers, load_factor_pct or passengers_change_pct; ticker = a listed company's airlines (e.g. DAL: Delta and Endeavor). view "airline": one airline (carrier code, e.g. DL, AA, UA, WN, B6, 9E) with its newest month, last 12 months, busiest airports and routes. view "series": monthly numbers of carrier (comma-separated codes), ticker, or all airlines; by "carrier" splits them; date_from / date_to (YYYY-MM). view "airports": airports ranked for a period by passengers (default), flights, on_time_pct or cancelled_pct; filters {"state": "TX"} or {"country": "US"}. view "airport": one airport (airport = 3-letter code, e.g. ATL, JFK, LAX) with its monthly series, carriers and top routes. view "routes": non-stop routes ranked by passengers for a period; origin, dest or airport (either end), carrier, ticker; by "carrier" for one row per airline and route; filters {"series": true} with one origin and dest for a route's monthly series, {"international": true}. view "travelers": passengers on flights within, to and from the US by month (domestic and international, change from a year earlier). Daily traveler counts are not available. on_time_pct counts cancelled and diverted flights as not on time. A passenger on a connecting trip is counted on each flight.

US banks and credit unions (official public data): every institution with its quarterly assets, deposits, loans, equity, profit, return on assets and equity and capital ratio (banks since 1984, credit unions since 2002), branches with deposits (banks, June 30 each year since 1994), and bank failures since 1934. Money in US dollars, ratios in percent. Ids: "b" + number for banks (b628), "cu" + number for credit unions (cu5536). view "search" (default): find institutions by query (name words, e.g. "navy federal"), ticker of the listed holding company (e.g. "JPM": its banks), kind, state, city or zip, with the newest quarter's numbers; sort total_assets (default), total_deposits, net_income_ytd, roa or name; filters e.g. {"status": "all"}. view "bank": one institution (bank = id or page name) with its newest numbers, the change from a year before, its branch count and deposits, and a failure if any. view "financials": its quarters over time (filters e.g. {"period_from": "2019-12-31"}). view "branches": its branches (year, state). view "screener": rank institutions on the newest quarter (or period, e.g. "2026-06-30"); sort any metric (e.g. roa, capital_ratio, total_deposits, assets_change_1y_pct); filters with _min/_max, e.g. {"total_assets_min": 1e10, "roa_min": 1.2, "listed": true}. view "totals": all institutions together by quarter (filters {"period_from": ...}) or {"by": "state"}. view "area_branches": branches in a state, county (with state), city or zip, ranked by deposits. view "area_deposits": deposit market share by institution in an area (or by state for the whole US). view "failures": failures with size, estimated loss and acquirer, with totals (year, e.g. 2023 for "how many banks failed in 2023", state, query). view "failure_years": failures per year. Consumer complaints (counts only, since December 2011; the newest month is partial; complaints are not verified): view "complaint_companies": companies named in complaints (query = name words, ticker, or bank = a bank id from view search), with complaints in total and in the 12 newest complete months. view "complaint_company": one company (query = its company_id or page name from complaint_companies) with its monthly series, how it answered (relief_pct, timely_pct) and its top product groups, issues and states (filters e.g. {"date_from": "2025-01"}). view "complaints": complaints by month for everything or for a ticker, bank or state (filters e.g. {"by": "year", "product_group": "mortgages", "company": "8"}). view "complaint_breakdown": ranked split for the 12 newest complete months (filters {"by": "company"} (default), "product_group", "product", "issue", "sub_issue" or "state"; plus e.g. {"product_group": "credit_cards", "sort": "relief_pct", "min_complaints": 500}). Mortgage lending (home loan applications per lender each year since 2018; counts only): view "mortgage_lenders": lenders ranked by applications in a year (default newest), nationwide or in a state or county (with state), with loans made, approval and denial rates, amounts, average rate and share of the area; find them by query (name words), ticker (e.g. RKT, UWMC, WFC) or bank (a bank id from view search); filters e.g. {"purpose": "refinance", "loan_type": "fha", "year": 2024, "sort": "denial_rate", "min_applications": 1000}. view "mortgage_lender": one lender (query = its lender_id, page name or LEI from mortgage_lenders), or every lender of a ticker or bank added up: totals by year, the split by purpose, loan type and occupancy, top states and counties. For an area's mortgage market use get_housing_area_insights with include=["mortgages"].

Revenue by business segment, product and geography, from company filings. period="annual" (default): yearly breakdown. period="quarter": revenue per fiscal quarter (needs symbol) by perspective: segment, product, region, country, end_market, channel, customer or subsegment (comma-separated; default all), or perspective="views" to list which views the company has and how many quarters each covers. Quarter rows: fiscal_period (e.g. 2025Q4), period_end, period_weeks (12 to 16), revenue, total_revenue, share_of_total_revenue, member_role (operating, corporate, elimination), is_leaf (never add a parent row to its children), is_derived (Q4 calculated as full year minus Q1 to Q3) and quality_flags. Filter with fiscal_year and fiscal_yearqtr (e.g. 2025Q2), or filters such as from_date, to_date, fiscal_quarter, leaf_only, include_derived, latest_only, limit. Without a year the newest 12 quarters are returned. Drill-down (detail=True, needs symbol): every breakdown list of ONE period down to 4 levels, e.g. segment > area > product (drug), or region > country, or product > region. Annual (fiscal_year, default the latest) or period="quarter" (fiscal_yearqtr, default the latest quarter). The answer lists every breakdown list (lists: breakdown_id, level, parents) and the rows picked by level (1 to 4; default 1), parent (the name of the item to open, e.g. "Biopharma", then "Primary Care") and breakdown (a breakdown_id from lists, or segment, subsegment, product or region). At most 100 rows per answer; the rest is counted in not_shown.

Market calendars: 'earnings_splits' (earnings dates and stock splits, filter by symbol), 'ipo' (upcoming and recent IPOs), 'economic' (economic events like CPI or Fed decisions, filter by event_name/country).

Commodity futures prices, e.g. GC=F gold, SI=F silver, CL=F crude oil, NG=F natural gas, ZC=F corn. interval '1d' or '5m'; points = how many of the newest bars to return (default 250). US agriculture (official statistics, added 10/09/2026), with agriculture instead of interval: "commodities": corn, soybeans, wheat, cotton, rice, sorghum, barley, oats, hay, peanuts, sugar_beets, potatoes, cattle and hogs with the newest US production, value, yield, acreage, price received and inventory (change from a year earlier), page name and related futures symbols; symbol (e.g. ZC=F, ZW=F, LE=F) finds the crops of a futures market. "stats": one commodity's series since 2000 for the US (default), a state (state) or a county (filters {"county_fips": "19153"}); statistic production, production_value, yield, area_planted, area_harvested, price_received or inventory; filters e.g. {"item": "winter", "frequency": "annual", "from": "2015"}. "ranking": states ranked, or the counties of a state (state), on statistic (default production; inventory for livestock) for the newest period, with change from a year earlier and share of the US; filters e.g. {"period": "2024", "sort": "change_1y_pct"}. "prices": daily and weekly cash prices (grain bids in IA, IL, KS, MO, MN, MT, NE, TX, IN by delivery point; 5-area slaughter steers and heifers; national barrows and gilts; boxed beef and pork cutout); filters {"latest": true} for the newest price of each market with its change, or {"market": "iowa", "item": "corn", "from": "2026-01-01"}. "received": monthly prices received by farmers (US or state). The current crop year is a forecast (forecast = true) until the final estimate.

Fields you can filter and rank on in screen_stocks (dataset "stocks") and search_housing_areas (dataset "areas"), with unit, description and typical range (10th, 50th, 90th percentile). Call this before screening to choose realistic thresholds. For areas pass the level you will search (ranges differ between states, cities and ZIPs).

Earnings call transcripts. Filter by fiscal_year and fiscal_qtr (e.g. Q3) to get one call; transcripts are long.

US economic data. economic_metrics is either a time series name: GDP, real_GDP, real_GDP_Per_Capita, nominal_Potential_GDP, CPI, inflation, inflation_Rate, federal_Funds, unemployment_Rate, total_Non_farm_Payroll, initial_Claims, retail_Sales, consumer_Sentiment, durable_Goods, industrial_Production_Total_Index, new_Privately_Owned_HousingUnits_Started_Total_Units, total_Vehicle_Sales, retail_Money_Funds, smoothed_US_Recession_Probabilities, 30Year_Fixed_Rate_Mortgage_Average, 15Year_Fixed_Rate_Mortgage_Average, commercial_Bank_Interest_Rate_On_Credit_Card_Plans__All_Accounts, 3Month_Or_90Day_Rates_And_Yields_Certificates_Of_Deposit; or one of these data sets (put their options in filters): - "yield_curve": the US Treasury yield curve (1 month to 30 years, percent) on the newest day, compared with 1 month and 1 year earlier, with the 10y-2y and 10y-3m spreads. date = another day; filters {"compare": "1w,1m,1y"}. - "treasury_yields": daily Treasury yields from 1990. filters {"maturity": "2y,10y", "date_from": "2024-01-01", "date_to": ..., "frequency": "daily|weekly|monthly", "limit": 100}. - "fed_decisions": Federal Reserve (FOMC) rate decisions since 1994 with the target range, the change in basis points, the current target range and the NEXT MEETING date. filters {"decision": "hike|cut|hold", "year": 2024, "status": "upcoming"}. - "inflation_by_category": consumer prices for 25 categories (core, food, food at home, energy, gasoline, electricity, shelter, rent, new and used vehicles, car insurance, airline fares, apparel, medical care, services ...): yoy_pct (12-month change) and mom_pct (1-month change, seasonally adjusted). filters {"latest": true, "sort": "yoy_pct"} ranks the categories rising fastest; {"category": "gasoline,shelter", "month_from": "2024-01"} gives history. - "corporate_bond_yields": yields of high-grade (AAA to A) corporate bonds for 2, 5, 10 and 30 years, monthly from 1984, with the spread over Treasury yields. - "us_trade": US goods exports, imports and trade balance by month from 2015 (US dollars, not seasonally adjusted). filters {"dimension": "total"} or {"dimension": "country", "code": "5700"} (China; 1220 Canada, 2010 Mexico) or {"dimension": "product", "q": "vehicles"} (2-digit HS chapters). - "us_trade_top": top trading partners or product groups over a period. filters {"dimension": "country|product", "period": "last_12_months|ytd|year|month", "year": 2025, "sort": "total_trade|exports|imports|balance", "order": "asc"} (balance asc = largest deficits). - "port_volumes": monthly container volumes of the largest US seaports (Los Angeles since 1995, Long Beach since 1987) in TEUs: loaded and empty, imports and exports, change from a year earlier, 12-month totals; prelim = the newest (first) count. Without a port: the ports ranked for date (YYYY-MM, YYYY; default each port's newest month) or filters {"period": "ttm"}, with the ports combined. filters {"port": "USLAX"} (or "USLGB", "all") gives one port's history, newest first: {"frequency": "year"}, {"date_from": "2019-01"}, {"limit": 60}. filters {"view": "ports"} lists the ports. - By industry, newest period ranked (change from a year earlier, change_1y_pct): "retail_by_store_type" (monthly sales), "wholesale_by_industry", "manufacturing_by_industry" (shipments; filters {"metric": "new_orders"}), "industrial_production_by_industry", "capacity_utilization_by_industry", "producer_prices_by_industry", "gdp_by_industry" (value added; filters {"metric": "share_of_gdp", "frequency": "quarterly"}). More filters: period, parent (e.g. "00" for the sectors), level, sort (change_1y_pct, value), order, limit. - Layoffs (one event per layoff, every source attached: state WARN notices, SEC 8-K Item 2.05 filings, news reports; status confirmed = an official notice or filing exists, reported = news only; workers only as stated): "layoffs": events, newest first. filters {"ticker": "INTC"}, {"company": "intel"}, {"state": "CA"}, {"county": "Los Angeles", "state": "CA"}, {"naics": "31-33"}, {"country": "US"}, {"source_type": "warn|sec|news"}, {"status": "confirmed|reported"}, {"min_workers": 100}, {"sort": "workers"}, {"limit": 50}; date = first announced date. filters {"event_id": 123} gives one event with all its sources (links to the notices, filings and articles); filters {"view": "company", "ticker": "INTC"}, {"view": "state", "state": "CA"} or {"view": "industry", "naics": "5112"} give one company, state or industry with totals, by month or year, top counties, industries, companies and the official numbers. "layoffs_summary": events and workers by filters {"by": "state|county|industry|country|month|year|source_type| status"} plus the same filters. "layoffs_companies": companies ranked by events and workers, filters {"days": 30|90|365|0, "state": ..., "naics": ..., "listed": true, "sort": "workers"}. "layoffs_jolts": official monthly layoffs and discharges (JOLTS, thousands and rate) for the US, states ({"area": "CA"}) and US industries ({"industry": "300000"}). "layoffs_claims": weekly unemployment insurance initial and continued claims by state ({"state": "CA"}; US = sum of the states, with the seasonally adjusted US number). Data sets answer with website_link: share it.

US election results with margins, swing, turnout and partisan lean. Coverage: president and US Senate by state for every election 1976 to 2024, US House by district 1976 to 2024 and county results of the presidential race 2000 to 2024; county, city or town, congressional district and precinct results, governors, other statewide offices, state legislature seats and statewide ballot measures for 2016, 2018, 2020, 2022 and 2024. Results are certified; election-night rows (status live) are unofficial counts and a race is only called when called_at is set. party_margin/rd_margin_pct = Republican share minus Democratic share in points (positive = Republican lead, R+3.2 means Republicans led by 3.2 points); swing_pct is the change of that margin against the previous comparable race in the same area (positive = toward Republicans); flipped = the winning party changed. view "races" (default): races with winner or leader, runner-up, votes and shares, margin, swing, flip; filter by year, office (president, us_senate, us_house, governor, lt_governor, attorney_general, secretary_of_state, treasurer, other_statewide, state_senate, state_house, ballot_measure, judicial; several with commas), state (two-letter code), name (race or candidate text) and filters such as {"margin_max": 5} (close races), {"flipped": true}, {"winner_party": "R"}, {"status": "live"}. Each row has race_id: use it as `race` in view "results". view "results": one race (race = race_id such as "2024-president", "2024-us-senate-tx", "2022-governor-az") with every candidate (votes and share) and the result by area: level state, county, cousub (city or town), cd (district) or precinct (precinct needs county = 5-digit code); state limits to a state; name searches area names; sort total_votes, rd_margin, swing, dem_pct, rep_pct, turnout or name. view "candidates": people who ran: name, state, office, year, filters {"party": "D", "won": true, "in_congress": true}; with slug (from a candidate row) one candidate with every race. view "geography": one area with its races, partisan lean, bellwether record, split tickets, turnout and demographics: level state, county, cousub, cd or national and area_code (state: 2-digit code such as 48; county: 5-digit code such as 48453), or for a county give state and county name instead of area_code. view "area": the latest elections for a state, county (name) and/or city in one call (state, county, city). view "summary": analysis = trend (national presidential vote by year), seats (seats won by party and change), legislatures (control of state legislature chambers), flips and swing (areas that changed party or moved most; year, office, level state or county), bellwethers, split_tickets, lean (partisan lean index), demographics (county results next to income, education, age and race) or coverage (what is loaded). view "turnout": turnout by state or county and year (share of citizen voting-age adults, registered voters where reported); level state or county, state, name, filters {"min_turnout": 60}. On election night the answer also has election_night_note ("Election night: unofficial, still counting, last update HH:MM UTC"): share it; live rows are only ever "Leading" (leader_label), never a winner. The answer has website_link: share it so the user can see the pages on dataporium.tech.

US energy data from official US government sources. category e.g. petroleum, natural-gas, electricity, coal, co2-emissions, total-energy; location e.g. a state code (TX); period e.g. 2024; period_type annual/monthly. category "gas_prices" gives weekly retail pump prices (US dollars per gallon, taxes included) for the US, its fuel regions, 9 states and 10 cities: dataset = product (regular, midgrade, premium, all_grades, diesel; default all), location = US, a state code (CA, CO, FL, MA, MN, NY, OH, TX, WA), a region code (R1 East Coast, R1A New England, R1B Central Atlantic, R1C Lower Atlantic, R2 Midwest, R3 Gulf Coast, R4 Rocky Mountain, R5 West Coast, R5X West Coast except California) or a city (Boston, Chicago, Cleveland, Denver, Houston, Los Angeles, Miami, New York City, San Francisco, Seattle); period = a year (2024) or month (2024-06) for history; without period you get the newest week with the change from 1, 4 and 52 weeks earlier. Website: https://dataporium.tech/energy?tab=gas-prices. category "local_gas_prices" gives daily local pump prices (US dollars per gallon) for the US, every state and DC, about 470 metro areas and about 3,100 counties (a separate daily series, never combined with the weekly one): location = a state code (AZ) for that state's own price, a 5-digit county FIPS (04013), a metro code (a CBSA code such as 38060, or a code from an earlier answer) or US; filters {"area_type": "county", "state": "AZ"} lists all counties of a state (area_type: state, metro or county); dataset = product (regular, midgrade, premium, diesel; counties have regular); period = a year or month for daily history; without period you get the newest day with the change from 1 day, 1 week, 1 month and 1 year earlier. For the most local price of a place (county, then its metro area, then its state) use filters {"nearest": true, "county": "Orange", "state": "CA"}. category "electricity_rates" gives yearly electricity prices per utility (from 2024 on): customers, sales (MWh), revenue and the average price in cents per kWh (revenue / sales over the year, not a tariff); dataset = sector (residential, commercial, industrial, transportation, all; default residential), period = year (default newest). location = a state code: that state's utilities ranked by price; location = a 5-digit county FIPS (48453): the utilities serving that county with the typical home price there and the state average; no location or "US": every state ranked with the US average and the average monthly home bill; filters {"view": "states"} with a state code: that state's history by year; filters {"utility_id": 5701}: one utility over the years with the counties it serves. More filters: sort (avg_price_cents, customers, sales_mwh, revenue_usd, change_pct), order, q (utility name), ownership, service_type, min_customers, limit. Website: https://dataporium.tech/energy?tab=electricity-rates. category "grid_prices" gives wholesale electricity prices (US dollars per MWh; divide by 10 for cents per kWh) of the Texas grid (since 2020), the California grid (about 3 years back) and the 11 zones of the New York grid (since 2020): location = texas, california or new_york (without it: the list of grids, the states they serve and their hubs and load zones); dataset = hub or load zone code(s) (e.g. HB_NORTH, LZ_HOUSTON, SP15, PGAE, NYC, LONGIL); without period: each location's newest hour and day with 7- and 30-day averages; period = a day (2025-08-20, hourly), a month (2025-08, daily) or a year (2025, monthly), or period_type / filters {"frequency": "hourly|daily|monthly", "date_from": ..., "date_to": ...}. Each hour has the real-time average, lowest and highest price and the day-ahead price. Wholesale, not what homes pay. Website: https://dataporium.tech/energy?tab=grid-prices. category "ev_charging" gives EV charging stations (updated daily) and EV registrations by state: dataset = summary (default: stations and Level 1, Level 2 and DC fast ports counted by state; with a state code as location by county; filters {"group_by": "network|connector|county|zip|city|year_opened|month_opened|status|access|none"}), stations (the stations themselves, paged: filters {"zip": "78701"}, {"lat": 30.27, "lon": -97.74, "radius_miles": 5}, {"city": "Austin", "state": "TX"}, network, connector (j1772, ccs, chademo, nacs), dc_fast, access (public default), status (open default), q) or registrations (light-duty vehicles by state and year: all-electric, plug-in hybrid, hybrid and the rest, the EV share, the 1-year change and EVs per open public charging port; location = a state code or US, period = a year or "all"). location = a state code or a 5-digit county FIPS; period = a year or month (stations opened then). Website: https://dataporium.tech/energy?tab=ev-charging.

Stock exchange trading hours or holidays. exchange e.g. NYSE, NASDAQ, LSE.

List every filter (API parameter) a tool accepts, with descriptions, for use in its `filters` argument. tool is a tool name such as 'search_properties' or 'get_financial_statements'.

Financial statements (income statement, balance sheet, cash flow). summary=True returns the compact summary version with the key lines. Filter by fiscal_year (e.g. 2025), fiscal_qtr (e.g. Q2) or fiscal_year_qtr (e.g. 2025Q2); period_type e.g. annual or quarterly. Each period has data_status: "final", or "temporary" for a quarter the company has just reported that is shown early with only the lines already confirmed; the final numbers replace it later.

Weekly futures positions by type of trader in US futures markets (official public data, futures only, as of each Tuesday, published the Friday after; since 1986): long, short and net (long - short) contracts per trader category, as a share of open interest, and the change from the week before. Reports: legacy (every market: commercial = hedgers, noncommercial = large speculators, nonreportable = small traders), disaggregated (commodities, since 2006: producer_merchant, swap_dealer, managed_money = funds, other_reportable, nonreportable) and financial (currencies, rates, equity indexes, crypto, since 2006: dealer, asset_manager, leveraged_funds = hedge funds, other_reportable, nonreportable). view "latest" (default): one week across many markets for one category, ranked by net position as a percent of open interest (order desc = most net long first, asc = most net short first); group = energy, metals, grains, softs, livestock, currencies, rates, equity_indexes, crypto, other or commodities; the default category is the large speculators (managed_money for commodity groups, leveraged_funds for financial groups, else noncommercial); net_index_3y = where the net position stands in its 3-year range (0 = most short, 100 = most long); date = an older week. view "markets": find markets (query = name words, e.g. "gold" or "yen"; group). view "market": one market (market = site symbol such as GC=F, CL=F, NG=F, ZC=F, EUR-USD, JPY-USD, BTC-USD, a page name such as gold, or a market code) with the newest week of every report, all categories. view "history": one market over time, one row per week with <category>_net, _net_change and _net_pct_of_oi (filters {"view": "full"} adds long, short, spreading and trader counts); from_date / to_date (YYYY-MM-DD, YYYY-MM or YYYY); without dates the newest weeks come first (limit weeks). Currency futures are priced in US dollars per unit of the other currency: net long euro = betting on EUR-USD rising. Positions are not a forecast; say the report date.

US federal government business of listed companies: federal contracts they win and the lobbying they pay for (official US government data). Contract totals include the units a company owns today, for all years (also years before it owned them); group_by "unit" with a symbol lists those units and their share. view "contract_totals" (default): contract obligations in US dollars (contracts of every size), group_by fiscal_year (default with a symbol), quarter (fiscal quarters), agency, unit or symbol (companies ranked, default without a symbol); filters e.g. {"fiscal_year_from": 2020, "fiscal_year_to": 2025}. US fiscal years run October to September. Groups have complete=false while late defense contracts can still come in (90 days). view "contracts": contract awards of $1 million or more, newest first: award_id, agency, sub_agency, amount (total obligated so far), award_date, start_date, end_date, description, product_category, industry, place of performance. Filter by symbol, q (words, e.g. "F-35"), agency (e.g. "Department of Defense"), fiscal_year, date_from/date_to; sort award_date or amount; filters e.g. {"min_amount": 100000000, "state": "TX", "active_only": true}. view "lobbying_totals": lobbying spending, group_by quarter, year (default with a symbol), symbol (companies ranked, default without a symbol), issue (general issue codes such as TAX, DEF, HCR, TRD with plain names), entity (government entities contacted) or registrant (outside lobbying firms). A quarter's total is the company's in-house expenses when it filed its own report (these include outside firms), else the outside firms' income. view "lobbying": the quarterly lobbying reports, newest first: lobbying firm or in-house, amount, issues with their descriptions, government entities contacted. Filter by symbol, q (words in the issues, e.g. "tariffs"), issue (codes, e.g. "TAX,TRD"), year; filters e.g. {"in_house": false, "registrant": "Akin", "quarter": 2}. Lobbying covers 2015 to today (reports are due 20 days after each quarter); contracts cover fiscal year 2015 to today. The answer has website_link: share it with the user.

Industry and sector classification of a company (revenue-based sector/industry, NAICS sector, SIC category). Without a company it gives the official US industry code list (NAICS, 2 to 6 digits) with the parent/child tree: industry_code for one code (e.g. '522110', '52' or '31-33') with its parent, path and children; industry_query to search titles (e.g. 'banking'). Code list filters: version (2022 default, or 2017), code (prefix), level (2 sector to 6 most detailed), parent, sector, limit, offset; e.g. filters={"level": 2} lists the 20 sectors. With industry_code and area_stats it gives that industry's businesses, employees and payroll: us_by_year (US totals for every year), or state, metro, county or zip (the areas ranked by employees for the newest year; zip by businesses). Filters then: year (e.g. 2023 or all), state (e.g. CA), sort, limit, offset. With metrics it gives numbers by industry (official US statistics): dataset revenue (market size: revenue and firms for every code, US and states, 2017 and 2022; quarterly revenue of service industries), gdp (value added, share of GDP, real value added, gross output), ppi (producer price index), output (industrial production index), capacity (capacity utilization), retail_sales (monthly sales by store type), wholesale_sales, shipments (monthly manufacturers' shipments, new orders, inventories). metrics "values" + industry_code: every series of that industry over time (filters: metric, frequency, state, from, to, seasonally_adjusted, limit); "series": which series exist (industry_code, industry_query or filters parent, level); "ranking" + dataset: one period across industries, e.g. the fastest rising producer prices or the largest industries by revenue (filters: metric, parent such as '00' for the sectors, level, period, sort change_1y_pct|value, order; with industry_code and {"area_type": "state"} it ranks the states).

Insider trading: recent 'transactions', insiders' 'holdings_summary', or full 'transaction_history'.

Option chain (calls and puts with strikes, expirations, prices, volume, open interest) for a stock.

US patents granted to listed companies since 2015 (official US government data), listed under the company that owned the patent when it was granted; and US trademark (brand) filings per company, year and class since 2000. view "totals" (default): patent counts, group_by year (grant year, default with a symbol), section (technology section A to H or Y, plus design patents), cpc_class (technology class such as G06F computing or H01M batteries) or symbol (companies ranked by patents, default without a symbol). Each group: patents, claims, cited_by (citations from later US patents) and share. view "patents": the patents, newest first: patent_number, title, grant_date, filing_date, patent_type, cpc_main (main technology class) and section_name, claims, inventors, citations_made and cited_by. Filter by symbol, q (words in the title, e.g. "battery"), section, cpc_class, year_from/year_to; sort grant_date or cited_by (the most cited patents); filters e.g. {"patent_type": "utility", "min_cited_by": 10}. view "trademarks": with a symbol, the company's trademark applications filed and registrations issued (all its trademark owners together), group_by year (default), class or year_class; trademark_class = one of the 45 international classes (9 electronics and software, 25 clothing, 35 business and retail services, 42 technology services, ...). Without a symbol, the top filers of a period (default: the last complete year), group_by owner (default) or ticker (listed companies), by applications or registrations (filters {"by": "registrations"}), with share of all filers; filters e.g. {"listed_only": true, "country": "JP"}. Owners are companies (individuals are never named). view "trademark_owners": find trademark owners by name (q, e.g. "nike") or symbol, with their totals since 2000; one owner's filings: view "trademarks" with filters {"owner": "<slug>"} and no symbol. New patents are granted every Tuesday; trademarks are updated weekly. The answer has website_link: share it with the user.

Stock trades reported by members of the US Congress in their official financial disclosure filings: who traded, party, chamber, trade and filing dates, buy or sell, amount range. Data is delayed by law (filed up to 45 days after the trade). view "trades" (default): the trades, newest first; filter by symbol (e.g. NVDA, or several: "AAPL,MSFT"), member_name (part of a name, e.g. "Pelosi"), member_id, party (D, R or I), chamber, trade_type (buy or sell), date_from/date_to (trade dates, YYYY-MM-DD) or days (last N days). Each trade has the member, party on the trade date, state, ticker and company, asset_name and asset_class, buy or sell, the amount range as filed, held_by (Self, Spouse, Joint or Child), trade_date, filed_date, filing delay and filing_url (the official filing). view "summary": totals grouped by group_by (party, member, symbol, month, year, chamber, trade_type, asset_class, held_by, state, amount_bucket or delay_bucket): trades, buys, sells, estimated buy and sell value, net, members, symbols, average filing delay and late filings. view "members": members with trade counts, buys, sells, last trade, their most traded symbols and the family split (self_trades, spouse_trades, joint_trades, child_trades, family_trades, family_value_estimate, family_share_pct); sort="family_trades", "spouse_trades", "family_value" or "family_share" ranks by it. One member's history: member_id with symbol (e.g. member_id="P000197", symbol="NVDA") returns all of that member's trades in that one symbol, including trades by the spouse, joint and children (field held_by). With view="summary" and group_by="held_by" the same filters give the family split: Self, Spouse, Joint and Child. view "symbols": tickers with trade counts, buys, sells, members and the split by party (use days=90 and sort="buys" for the most bought stocks). view "net_worth": net worth from the yearly financial disclosure reports, as reported (assets and liabilities are filed as ranges, so net_worth_min, net_worth_max and the middle net_worth_mid; the personal home and some accounts are left out). Without member_id: each member's latest year ranked by net worth (the richest members); filter by party, chamber, year, filters {"state": "CA", "in_office": true}; sort net_worth, assets, liabilities or name; group_by party, chamber, state or year gives the median, average and total net worth per group (party comparison). With member_id or member_name: that member's history, one row per report year. Rows have the number of assets, the share held by the member, spouse, jointly and by children, and the five largest assets. view "assets": what a member owns: every asset of one report (member_id or member_name, year = report year, default the latest) with asset class, ticker, held_by, value range and income, plus the report's net worth, its liabilities and the years available; filters e.g. {"asset_class": "Stock", "held_by": "Spouse"}. With symbol and no member: the members whose latest report lists that stock. branch: "congress" (default), "executive" (senior executive branch officials such as the President, Vice President and cabinet secretaries: their trades, net worth and assets) or "all". Amounts are ranges (for example $15,001 - $50,000); value fields are estimates from the middle of each range, never exact. Filings can come up to 45 days after the trade, or later when filed late (late_filing). sort: for trades trade_date, filed_date, amount_mid, member_name or symbol; for the other views trades, buys, sells, buy_value, sell_value, net_value or last_trade_date. Extra filters (list_filters shows all), e.g. {"late_filing": true, "held_by": "Spouse", "asset_class": "Stock", "amount_min": 100000, "state": "TX"}. country: "US" (default) or another country's parliament: "DE" (German Bundestag, members from 2013) or "NL" (Dutch House of Representatives). These countries publish no stock trades, so with DE or NL the views are: "members" (members ranked by outside income; sort income, side_jobs, interests or name; year limits the income to one calendar year), "interests" (side jobs with role and organization, outside income with exact amounts or ranges, company stakes, donations and benefits (DE), gifts and paid travel (NL); filters e.g. {"interest_type": "stake"}, {"interest_type": "income"}, {"org_type": "Private company"}, {"q": "Aufsichtsrat"}, {"has_amount": true}; sort year, yearly_value or amount), "summary" (group_by party, member, year, interest_type, category, org_type, organization or ticker: record counts and outside income in euros; e.g. top earners: group_by="member", sort="income") and "holdings" (company stakes, Germany; {"listed_only": true} for stakes in listed companies with our ticker). member_id looks like DE-78869 or NL-1057; member_name finds members by name. Amounts are in euros as reported (Germany: exact since 2021, ranges before; business owners report gross receipts). Dutch answers built on the official register of side activities carry source_credit ("Source: Tweede Kamer"): show it with that data. The answer has website_link: share it so the user can see the trades on dataporium.tech.

Find US homes in a place: give zips, or city (+ state), or county (+ state), or a state. With no location it searches the whole US using a fixed sample, and the counts are approximate (marked approximate in the answer). status: for_sale (default), pending, rent, sold (last 12 months) or off_market. Returns total_matches, a summary of ALL matches (count, median price, median price per sqft, median true days on market, share with a price cut, share relisted), the market numbers of the area (inventory, median prices, days to pending, sales, sale vs list price, months of supply, home value index and forecast), and up to 50 homes per call with short facts: price, price per sqft, beds, baths, sqft, real listed date and days on market (not the date that resets on every relist), true days on market for the whole selling effort, relisted or not, last and total price cut, sold price vs the last list price, and price per sqft vs the ZIP median. Use offset for the next page. Each home has a property_id for get_property, and the answer has website_link: share it so the user sees these homes on the map. Duplicate homes are already removed. For the full details of the area (market history, property tax, crime, demographics, climate, air quality, neighborhood profile) call get_housing_area_insights; to compare or rank areas call search_housing_areas.

Everything about one US home, by property_id (from search_properties) or by address in any spelling (e.g. "110 Coastal Garden, Irvine CA" or "125 Shell"; ZIP, city and state are optional). Returns the address and location, status, price, estimate, HOA, beds, baths, sqft, lot, year built, description, photos, the listing facts (real listed date, days on market, relisted and when, the whole selling effort with first price and days off the market, every price cut), the clean timeline (listed, price changed, pending, removed, relisted, sold, listed for rent), last sale, tax history, schools, agent and office contacts, comparisons with nearby homes (2 miles) and with the ZIP and city, the area market numbers and an estimated monthly payment. To recalculate, call get_property again with new payment inputs (down payment, interest rate, term, tax rate, insurance, HOA, PMI, maintenance) or a new radius_miles and nearby filters. If several homes match an address, the answer lists them with a match score: ask the user which one, then call again with its property_id. The answer has website_link: share it so the user can open this home on the map at dataporium.tech. For the full details of the area (market history, property tax, crime, demographics, climate, air quality, neighborhood profile) call get_housing_area_insights (the answer has more_area_data with the exact call); to compare or rank areas call search_housing_areas.

Currency exchange rates. currency_conversion like 'USD-EUR'. interval picks the bar size (5m and 1h cover recent days, 1d/1w/1mo cover years).

Recalls of vehicles and consumer products, vehicle crash test ratings, vehicle owner complaints, trucking carrier safety, and workplace safety, environmental compliance and greenhouse gas emissions of facilities and listed companies (official public data). Vehicle recalls since 1966 (cars, trucks, motorcycles, RVs, and also tires, child car seats and vehicle equipment), consumer product recalls since the 1970s, owner complaints since 1995. view "recalls" (default): recalls, newest first: date, title, manufacturer and its ticker, units affected, hazard group, makes and model years or product type. Filter by ticker (e.g. F, GM, TSLA, TGT, MAT), kind (vehicle or consumer), make, model and model_year, query (words, e.g. "airbag inflator" or "crib"), date_from/date_to; filters e.g. {"hazard": "fire", "product_type": "bicycles", "do_not_drive": true, "category": "tire"}; sort date, units or title. view "recall": one recall (recall = recall_id or slug from view recalls) with what is wrong, what can happen, the remedy, incidents reported and the makes, models and model years it covers. view "totals": recalls counted by year (default), month, kind, category, make, model, model_year, manufacturer, ticker, hazard, product_type or component (by), with the same filters, e.g. a company's recalls per year (ticker, by "year") or the most recalled models of a make (make, by "model"). view "ratings": crash test star ratings (overall, frontal, side, rollover, 1 to 5 stars) and safety features by make, model and model year; filters e.g. {"min_overall": 5, "year_from": 2022, "vehicle_class": "suv"}. view "complaints": owner complaints about vehicles by month (default, newest first), year, make, model, model_year or component (by), with crashes, fires, injuries and deaths reported; filters e.g. {"component": "AIR BAGS"}. Complaints are the owner's account and not verified; popular models get more. Makes and models are written as published, e.g. FORD and F-150 (any case). view "carriers": active US trucking carriers with 10+ power units, largest fleet first: power units, drivers, crashes over 24 months (fatal, injury, tow-away), crash rate per 100 power units, roadside inspections and driver and vehicle out-of-service rates, safety rating, ticker. Find by query (name or carrier number), ticker (e.g. JBHT, KNX, WERN, ODFL, SAIA), filters e.g. {"state": "TX", "kind": "passenger", "operation": "interstate", "safety_rating": "conditional", "min_power_units": 100}; sort power_units, drivers, crashes, fatal_crashes, crash_rate, vehicle_oos_rate, driver_oos_rate (carriers with 20+ inspections) or inspections. view "carrier": one carrier in full (carrier = carrier number or slug from view carriers) with its fleet rank and the averages of all trucking carriers, of its state and of all roadside inspections. view "trucking_stats": all carriers (also small ones) by state (default, last 12 months), month or year (by): active carriers, power units, drivers and fleet sizes (monthly snapshot), new carriers, crashes and deaths (where they happened), inspections and out-of-service rates; date_from/date_to as YYYY-MM, filters e.g. {"state": "TX"}; sort (by state) e.g. crashes or vehicle_oos_rate. Crashes do not say who caused them. Workplace safety, environmental compliance and emissions (2010 on; counts and money, no personal data): view "facilities": facilities with their totals for the last 5 years (date_from/date_to as years): workplace safety inspections, violations (serious = serious, willful or repeat) and penalties; environmental (air, water, waste) inspections, violations, formal enforcement actions, penalties and quarters in noncompliance; newest yearly greenhouse gas emissions. Find by query (part of a name), ticker (e.g. XOM, DUK, TSN, WMT, AMZN), filters e.g. {"state": "TX", "county": "Harris", "zip": "77506", "lat": 29.7, "lon": -95.0, "radius_miles": 5, "kind": "environmental", "program": "air", "naics": "324"}; sort penalties (default), violations, serious_violations, inspections, enforcement_actions, emissions or distance. view "facility": one facility in full (facility = facility_id from view facilities): every year of both kinds and of emissions, owners and listed company. view "compliance_companies": with ticker, a listed company's facilities by year and kind (inspections, violations, penalties, enforcement actions), its emissions by year, largest cases and emitters; without ticker, listed companies ranked (sort penalties, violations, serious_violations, emissions, facilities). view "emissions": greenhouse gas emissions in metric tons CO2e of large facilities, newest year by default (filters {"year": 2022}): by (group_by) facility (default), company, state, sector or year; ticker, filters e.g. {"state": "TX", "sector": "Power Plants"}. Penalties are as recorded; violations are not all equally serious; larger companies have more facilities: compare with care.

Yearly pay that US employers offered in certified work-visa filings (official public data, summarized): H-1B (kind h1b, default), E-3 (e3) and PERM green card filings (perm), by fiscal year (October to September) since 2020, per employer, job group (official occupation code, e.g. 15-1252 Software Developers) and worksite city, metro area or state: filings, workers, median, 25th and 75th percentile and mean pay, the median prevailing wage and the most common job title. Groups with fewer than 3 filings show counts only. year defaults to the newest complete fiscal year. view "employers" (default): employers ranked by filings (or sort "median") in the US: find one by query (name words, e.g. "google") or ticker (e.g. AMZN, GOOGL, INFY: every employer name linked to it); job ranks employers for one job group (e.g. "the highest paying employers of software developers": job "15-1252", sort "median", filters {"min_filings": 50}). view "employer": one employer (employer = employer_id or slug from view employers, or ticker: its largest employer name): pay by year, top job groups, top cities and the top job x city groups. view "jobs": job groups with US pay (query = title words, e.g. "data scientist"; area for one place). view "job": one job group (job = code, e.g. "15-1252"): pay by year, top employers, metro areas, cities, states. view "areas": places ranked by filings or pay (by = state, metro (default) or city; state narrows; job for one job group, e.g. software developer pay by metro area). view "area": one place (area = US, a state code such as CA, a metro area code such as 41940, or a slug from view areas, e.g. "seattle-wa"): pay by year, top job groups and top employers. view "totals": all employers by year (by "year", default; with job and/or area), or ranked by state, metro, city or job for one year. A filing is an offer for a position, not a hire. Say the fiscal year and that it is offered pay. College costs and graduate pay (official public data, US colleges operating now; added 10/09/2026): view "colleges": colleges ranked or searched: query (name words) or state; filters e.g. {"ownership": "public", "level": "four_year", "max_net_price": 20000, "max_admission_rate": 0.3, "size": "large", "type": "hbcu", "city": "Austin", "county": "Travis"}; sort enrollment (default), net_price, tuition_in_state, admission_rate, earnings_10yr, median_debt, debt_to_earnings, completion_rate or repayment_rate (rankings leave out colleges with fewer than 500 undergraduates unless filters {"min_enrollment": 0}). Net price = yearly cost after grants; earnings = median of former students 10 years after they started. view "college": one college (college = college_id or slug from view colleges, or query: the best name match): every measure, its biggest and best paid programs, tuition and net price by year, its earnings rank, medians to compare. view "college_programs": programs (field of study x credential) with graduates, median debt and median earnings 1 and 4 years after completion: one college's programs (college), or a field across colleges ranked by earnings (field = 4-digit code such as 1107 computer science or 5138 nursing, or a 2-digit family such as 11; or query = words of the field title); filters e.g. {"credential": "bachelor", "ownership": "public"}; sort earnings_1yr, earnings_4yr, median_debt, debt_to_earnings or completions. view "college_fields": fields of study (majors) ranked by the median earnings of their programs 1 year after completion ("which majors pay the most?"): filters e.g. {"credential": "bachelor"}; field or query narrow it. Small programs' earnings and debt are withheld for privacy (null). Say these are medians of graduates who work.

SEC filings of a company (10-K, 10-Q, 8-K, ...) with links. form_type filters by form.

Daily performance or P/E history of market sectors or industries (e.g. sector 'Technology', exchange 'NASDAQ').

Institutional ownership: 'major' holders, 'quarterly' 13F holdings (filters year, qtr, period), 'quarterly_summary', ownership 'by_type', share 'class', and holders by country 'geo'.

Short interest of US stocks and ETFs: the shares sold short and not yet bought back, reported twice a month (settlement dates around the 15th and the month end, published about a week later), history from 2017. view "history" (default): one or more symbols (e.g. "GME" or "AAPL,TSLA"), newest first: short_interest, previous_short_interest, change_shares, change_pct, avg_daily_volume, days_to_cover; for one symbol also `latest` with shares_outstanding and short_pct_shares_outstanding. date_from/date_to limit the dates. Without a symbol, settlement_date ("latest" or a date) lists every symbol on that date (sort short_interest, change_pct or days_to_cover). view "most_shorted": stocks ranked on the newest (or a given) settlement date by sort: short_pct (percent of shares outstanding, default), days_to_cover, short_interest, short_value, change_pct or change_shares; market nyse, nasdaq, nyse_american, otc or listed (all exchanges, default); filters e.g. {"min_market_cap": 300000000, "min_avg_volume": 100000, "sector": "Healthcare", "min_price": 5}. view "dates": the settlement dates with data. view "fails_to_deliver" (added 10/09/2026): shares not delivered to the buyer by the settlement date, per settlement day since 2017, published twice a month (1st to 14th, 15th to month end). With one symbol: daily rows (quantity = open balance of shares not delivered that day, price of the day before, value = quantity x price, newest first; date_from/date_to), totals per half-month (days with fails, average, largest day, longest run), the largest day and the newest half-month. Without a symbol: the largest fails of the newest half-month (settlement_date = a date inside another half-month); sort max_value (default), avg_value, total_value, max_quantity, avg_quantity, days_with_fails or longest_run; filters e.g. {"listed_only": true, "flagged_only": true, "min_value": 1000000}. flagged = at least 10,000 shares on 5 or more settlement days in a row (a simple screen, not an official list). Daily numbers are open balances, not new fails; a fail can have ordinary causes and does not by itself show improper short selling: say so when you use it. The percent of shares outstanding uses today's share count (a float figure is not available). The answer has website_link: share it with the user.

A company's social media and employer data: X (Twitter) profile/posts/follower history, LinkedIn profile/posts/followers and employees, Glassdoor profile and ratings.

Find a company's tickers across exchanges (main ticker, all listed symbols, CIK). Use this first when you only know a company name. Give symbol OR company_name (exact name, e.g. 'Apple Inc.').

Mergers, acquisitions and investments. 'deals' = full deal records for a company (symbol/company_name), 'summary' = shorter records, 'flat' = one row per deal with filters like ticker_buyer, ticker_target, txn_stage, date_announced, 'reports' = aggregated counts by level/level_values, 'filters' = searchable deal list.