tech.dataporium/mcp

Tools

• describe_screen_fields

  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).

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• get_business_segments

  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.

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• get_calendar

  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).

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• get_commodity_prices

  Commodity futures prices, e.g. GC=F gold, SI=F silver, CL=F crude oil, NG=F natural gas. interval '1d' or '5m'; points = how many of the newest bars to return (default 250).

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• get_company_documents

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

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• get_company_logo

  Logo image URL of a company.

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• get_company_profile

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

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• get_earnings_transcripts

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

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• get_economic_metrics

  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). Data sets answer with website_link: share it.

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• get_election_results

  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.

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• get_energy_data

  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.

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• get_exchange_schedule

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

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• 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.

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• get_fx_rates

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

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• get_government_activity

  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.

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• get_healthcare_market

  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. 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.

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• get_healthcare_provider

  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.

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• get_housing_area_insights

  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). 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.

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• get_industry_classification

  Industry and sector classification of a company (revenue-based sector/industry, NAICS sector, SIC category).

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• get_insider_activity

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

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• get_ma_transactions

  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.

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• get_market_movers

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

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• get_market_news

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

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• get_options_chain

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

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• get_patents

  US patents granted to listed companies since 2015 (official US government data), listed under the company that owned the patent when it was granted. 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}. New patents are granted every Tuesday. The answer has website_link: share it with the user.

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• get_politician_trades

  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.

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• get_property

  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.

  shallow
• get_sec_filings

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

  shallow
• get_sector_industry_performance

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

  shallow
• get_shareholders

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

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• get_short_interest

  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. 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.

  shallow
• get_social_media

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

  shallow
• get_stock_news

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

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• get_stock_prices

  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).

  shallow
• list_filters

  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'.

  shallow
• screen_ma_deals

  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.

  shallow
• screen_stocks

  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.

  shallow
• search_healthcare_providers

  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.

  shallow
• search_housing_areas

  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.

  shallow
• search_properties

  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.

  shallow
• search_symbols

  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.').

  shallow

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