Drillr — The financial MCP for AI agents

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              {
                "description": "Use to discover which SEC filings exist for a ticker before searching content.\nFor the actual content use sec_report_search instead.\n\nList indexed SEC filings for a given ticker with a summary header.\n\nReturns: summary (period coverage, per-type counts) + table of up to 50 filings\n(fiscal_year, fiscal_quarter, filing_type, filing_date, period_start, period_end).\n\nfiling_types filter: omit for main reports only (10-K, 10-Q, 20-F, S-1, DEF 14A\nand /A amendments; excludes 8-K/6-K); pass [] for all indexed types; pass explicit\nallowlist to override.",
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                      "description": "Filter by filing type. Omit for default (periodic reports + IPO/shelf registrations + amendments; excludes ad-hoc disclosures). Pass [] for all indexed types. Pass an explicit allowlist to override \u2014 use values from the `filing_type` column of a prior unfiltered call.",
                      "items": {
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                "description": "Use when you need narrative content (risk factors, MD&A, guidance language).\nFor consolidated financial numbers use run_sql on financial_statements instead.\n\nSEC report semantic search. Search paragraph content in 10-K/10-Q/20-F/6-K filings.\n\nSearch Strategy:\n- (1) Time-specific queries (\"recent\", \"last year\", \"Q4 2025\", etc.): Apply a time range\n  filter upfront to narrow results to the relevant period.\n- (2) Broad queries with no time requirement: Broad first, narrow later. First 1-2 searches:\n  use MINIMAL filters. Do NOT set filing_types or period unless explicitly needed. This\n  discovers what information exists in what filings. Later searches: add period/type filters\n  to narrow down.\n\nFiling types: 10-K (US annual), 10-Q (US quarterly), 8-K (US current/material events),\n20-F (foreign annual), 6-K (foreign current), S-1 (US IPO registration), F-1 (foreign IPO registration).\n\nSCOPE:\n- (1) Indexes ONLY company-filed SEC reports. Does NOT index institutional filings (13F-HR, 13D, 13G). For institutional holdings, use insider_and_institution_activities with source='institution'.\n- (2) For standardized consolidated financial numbers (revenue, margins, EPS, etc.), use financial_statements first \u2014 SEC filings contain narrative context, not structured data.\n\nHigh-Value Search Scenarios (use proactively when relevant):\n- Share dilution / SBC / buyback \u2192 search \"Shareholders' Equity\" or \"Capital Stock\" for share-level (not just dollar-level) breakdowns\n- Facilities / properties \u2192 search \"Properties\" for owned vs leased status, lease terms, land use rights\n- Interest rate sensitivity \u2192 search \"Market Risk\" or \"Interest Rate Risk\" for floating-rate debt exposure, swap arrangements\n- Segment revenue breakdown \u2192 search \"Segment Information\" or \"Revenue Disaggregation\"\n- Risk factors \u2192 search \"Risk Factors\" for specific risks\n- Management guidance \u2192 search \"Outlook\" or \"Guidance\" in MD&A\n- Accounting policies \u2192 search \"Critical Accounting Policies\"\n- Stock split history \u2192 search \"Stock Split\" for split ratios and dates\n\nUse for: segment revenue, risk factors, management discussion, guidance, accounting policies, operational metrics, related party transactions, property details, share structure.",
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                      "type": "array"
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                    "period_end": {
                      "description": "End period YYYY-MM",
                      "type": "string"
                    },
                    "period_start": {
                      "description": "Start period YYYY-MM",
                      "type": "string"
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                    "query": {
                      "description": "Search query",
                      "type": "string"
                    },
                    "ticker": {
                      "description": "Stock ticker, e.g. NVDA, AAPL",
                      "type": "string"
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                      "default": 10,
                      "maximum": 30,
                      "minimum": 1,
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                    "query"
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                "description": "Use for qualitative company discovery (industry, business model, supply chain,\ncompetitors, management background). For numerical screening (revenue, margins,\nratios, growth rates) use run_sql on company_snapshot instead.\n\nDrillr's company knowledge base \u2014 searchable across industry classification,\nproduct offerings, business model, segment structure, competitive landscape,\nsupply chain, management background, and customer profile.\n\nPass a natural language description (e.g. \"EV battery suppliers to Tesla\",\n\"Japanese semiconductor equipment makers\", \"AI inference chip startups\"). Returns\na structured list of matching companies with context snippets.\n\nONLY for finding a LIST of companies by description.",
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                  "properties": {
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                      "type": "string"
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                  },
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                "name": "company_search"
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              {
                "description": "Use when you need structured rows from financial/market/altdata tables.\nFor SEC filing narrative use sec_report_search instead. For company qualitative\ndiscovery use company_search instead. For recent news + market events use\nsignal_list instead.\n\nExecute a read-only PostgreSQL SELECT query on financial and alternative-data tables.\nCall `get_table_schema` first to look up columns for a specific table. For alternative-data categories, call `list_tables(categories=[...])` to see tables + columns.\n\nSQL constraints:\n- No CTE (`WITH ... AS`) \u2014 use subqueries instead.\n- Date columns are TEXT \u2014 use plain string comparison (`period_end >= '2024-01'`). Never use `::date` cast or `INTERVAL` arithmetic.\n- No `ROUND(float8, int)` \u2014 use `CAST(value AS DECIMAL(10,2))` if rounding is needed.\n- Structured-data queries: always filter by ticker (`WHERE ticker IN ('A','B','C')`; screening: `ticker NOT LIKE '%-%'` to exclude preferred stock). Alternative data is macro/industry/patent \u2014 no ticker filter required.\n\nStructured data (tables grouped by domain):\n\n## Market Data\n- price_volume_history: Historical OHLCV price data, ~32M rows. MUST filter by ticker AND time_frame ('daily'|'weekly'|'monthly') to avoid timeout. Columns: ticker, period_end, time_frame, open, high, low, close, adj_close, volume, turnover, vwap, change, change_percent.\n  Ticker conventions:\n    * STOCK / ETF \u2014 bare 1\u20135 letters (AAPL, MSFT, SPY, QQQ, VOO, TLT). Foreign listings use exchange suffix: 1557.T (JP), 310960.KS (KR).\n    * INDEX \u2014 leading \"^\" (^GSPC=S&P 500, ^DJI, ^IXIC=NASDAQ Composite, ^NDX=NASDAQ 100, ^RUT=Russell 2000, ^VIX). Foreign indices: ^FTSE, ^GDAXI, ^N225, 000001.SS (Shanghai Composite), FTSEMIB.MI.\n    * COMMODITY \u2014 [code]+USD or +USX (CLUSD=WTI futures, BZUSD=Brent, NGUSD=natgas, GCUSD=gold, SIUSD=silver, HGUSD=copper, ZCUSX=corn, CTUSX=cotton). Suffix USX = priced in cents.\n    * FOREX \u2014 6 letters base+quote, no separator (EURUSD, USDJPY, GBPUSD, USDCNY).\n    * CRYPTO \u2014 [token]+USD (BTCUSD, ETHUSD, SOLUSD, DOGEUSD, USDTUSD, USDCUSD).\n  Pitfalls:\n    1. Same asset, different tickers: NASDAQ 100 \u2192 index ^NDX (~26000) vs ETF QQQ (~640). Pick the one matching user intent.\n    2. WTI spot \u2260 futures. CLUSD here is NYMEX futures, NOT spot (FRED has spot via DCOILWTICO).\n    3. Tickers with \".\" or \"^\" MUST be quoted in SQL: `WHERE ticker = '^NDX'`.\n- index_price: Real-time price snapshot for 3 major US indices: S&P 500 (^GSPC), Dow Jones (^DJI), NASDAQ 100 (^NDX).\n- index_composition: Index constituent membership for S&P 500 / Dow Jones / NASDAQ 100 / Nikkei 225 (^N225) / TOPIX (^TPX). Includes current and historical members (active rows have exit_date IS NULL).\n- equity_extended_rt: Extended-hours snapshot, one row per ticker (~6.1K US equities; PK=ticker; refreshed every few minutes \u2014 see extended_updated_at). Use ONLY for pre-market / after-hours / overnight quotes. Columns: ticker, company_name, market, price_current (last regular-session close), then three parallel blocks pre_* / after_* / overnight_* (price, change_val, change_rate, high, low, volume, turnover), plus extended_updated_at. For regular-session OHLCV history use price_volume_history; for valuation / fundamentals use company_snapshot. Japan tickers are NOT in this view yet \u2014 extended-hours data is US-only.\n\n## Fundamentals\n- financial_statements: Quarterly and annual financial data covering income statement, balance sheet, and cash flow statement. Filter by ticker + fiscal_period ('FY' annual, 'Q1'..'Q4' quarterly).\n- company_snapshot: Real-time company snapshot, one row per company. Pre-computed metrics (ratios, percentages, per-share figures, growth rates) only \u2014 NOT raw statement line items. For raw statements use financial_statements; for qualitative discovery use company_search.\n\n## Earnings\n- earning_call_summary: Earnings call data with AI-structured summaries (management_highlights, guidance, risks, segment_performance, q_and_a). Filter by ticker + period_end (yyyy-mm). NOT for structured financial numbers.\n- earning_call_calendar: Earnings conference call schedule with EPS and revenue estimates vs actuals. `date` column is the scheduled earnings call datetime (UTC timestamptz) \u2014 NOT the press-release / 10-Q filing date (the filing typically lands the same day or shortly after). Use eps_actual IS NULL for upcoming calls, IS NOT NULL for reported.\n\n## Analyst Coverage\n- analyst_ratings: Individual analyst rating events \u2014 every re-rating is a separate row. ~565K rows, 519 firms. Filter by ticker; `date` is TEXT (string comparison, no ::date cast). importance >= 4 for high-impact calls.\n- analyst_ratings_consensus: Per-ticker analyst consensus rollup \u2014 one row per ticker, refreshed daily. Columns: strong_buy/buy/hold/sell/strong_sell counts, total_analysts, consensus, pt_consensus/high/low.\n\n## Ownership & Insider Activity\n- insider_and_institution_activities: Insider trades (Form 3/4/5) and institutional holding changes (13D/G/F, 13F-HR) from SEC EDGAR. Filter by ticker + source ('insider' vs 'institution'). For institutions, filer_name is the holder; shares/market_value give position size.\n\n## Corporate Events (8-K)\n- executive_change: 8-K executive change events \u2014 appointments, departures, reasons, successors, effective dates. Filter by ticker + event_type.\n- company_deal_events: Single source of truth for corporate deal activity (8-K 1.01/1.02/2.01, S-4, press releases). M&A, financing, material agreements as event-based rows (announced/signed/amended/closed/terminated/waived). Filter by ticker only \u2014 do NOT filter by deal_type. One deal may span multiple rows linked via deal_fingerprint.\n- debt_issuance: 8-K debt issuance events \u2014 principal amount, interest rate, maturity, lender, use of proceeds. Use is_current = true for latest version when amendments exist.\n- securities_offering: 8-K securities offering events \u2014 shares, price per share, total proceeds, investors, lock-up. Use is_current = true for latest version when amendments exist.\n\n## Executive Profiles & Compensation\n- executive_profile: Key executive profiles \u2014 name, title, compensation, gender, birth year. One row per executive per company.\n- executive_compensation: Annual executive compensation breakdown \u2014 salary, bonus, stock awards, option awards, total. Filter by ticker + year.\n\n## Alternative Data\nFor alternative-data tables, call list_tables(categories=[...]) first.",
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                "description": "Use BEFORE run_sql when you're unsure which columns a table has.\n\nLook up column definitions (name, type, description) for a data table.",
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                        "epochai_chip_sales_cumulative",
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                        "epochai_chip_sales_by_designer",
                        "epochai_chip_owners_cumulative_by_chip",
                        "epochai_chip_owners_cumulative_by_designer",
                        "epochai_chip_owners_quarters_by_chip",
                        "tsmc_revenue",
                        "twcustoms_trade",
                        "computeprices_gpus_dim",
                        "computeprices_gpu_offer_daily",
                        "computeprices_gpu_offers",
                        "computeprices_providers_dim",
                        "skypilot_ondemand_price",
                        "skypilot_spot_price",
                        "skypilot_instance_dim",
                        "epochai_models",
                        "epochai_benchmark_scores",
                        "epochai_benchmark_runs",
                        "epochai_ai_companies",
                        "epochai_ai_companies_revenue_reports",
                        "epochai_ai_companies_usage_reports",
                        "epochai_ai_companies_staff_reports",
                        "epochai_ai_companies_funding_rounds",
                        "epochai_ai_companies_compute_spend",
                        "epochai_polling",
                        "arena_leaderboard",
                        "litellm_price_history",
                        "litellm_models_dim",
                        "computeprices_llm_prices",
                        "computeprices_llm_price_daily",
                        "computeprices_llms_dim",
                        "comtrade_country",
                        "comtrade_hs_code",
                        "census_import_export",
                        "fred_series",
                        "fred_observations",
                        "fred_macro_daily",
                        "polymarket_events",
                        "polymarket_markets",
                        "polymarket_outcomes",
                        "polymarket_trades",
                        "polymarket_daily",
                        "polymarket_volume_daily",
                        "polymarket_holders_daily",
                        "kalshi_events",
                        "kalshi_markets",
                        "kalshi_trades",
                        "kalshi_daily",
                        "kalshi_volume_daily",
                        "mineral_deposit",
                        "mineral_deposit_commodity",
                        "mineral_deposit_operator",
                        "mineral_country_supply",
                        "mineral_critical_snapshot",
                        "fiscal_year_config"
                      ],
                      "type": "string"
                    }
                  },
                  "required": [
                    "table_name"
                  ],
                  "type": "object"
                },
                "name": "get_table_schema"
              },
              {
                "description": "Use to convert between fiscal year/quarter and calendar months for a given ticker.\nCompanies have different fiscal year starts (Apple Sep, Nvidia Jan) \u2014 call this\nbefore filtering on period_end columns.\n\nFiscal year \u2194 calendar month bidirectional conversion. Forward: ticker + fiscal_year + fiscal_quarter \u2192 period_start/period_end. Reverse: ticker + yyyy_mm \u2192 fiscal_year/fiscal_quarter.",
                "execution": {
                  "taskSupport": "forbidden"
                },
                "inputSchema": {
                  "$schema": "http://json-schema.org/draft-07/schema#",
                  "additionalProperties": false,
                  "properties": {
                    "fiscal_quarter": {
                      "description": "Quarter 1-4, or 0 for full fiscal year (required for forward conversion)",
                      "maximum": 4,
                      "minimum": 0,
                      "type": "integer"
                    },
                    "fiscal_year": {
                      "description": "Fiscal year (required for forward conversion)",
                      "type": "integer"
                    },
                    "ticker": {
                      "description": "Stock ticker (e.g. AAPL, MSFT, NVDA)",
                      "type": "string"
                    },
                    "yyyy_mm": {
                      "description": "Calendar month yyyy-mm (required for reverse conversion)",
                      "pattern": "^\\d{4}-\\d{2}$",
                      "type": "string"
                    }
                  },
                  "required": [
                    "ticker"
                  ],
                  "type": "object"
                },
                "name": "fiscal_utility"
              },
              {
                "description": "List alternative-data tables under the given categories. Returns each table's name,\none-line purpose, and column names (call get_table_schema if you need column\ntypes/comments). Batch up to 5 categories in one call.\n\nUse this BEFORE run_sql when you want to explore alt-data \u2014 run_sql alone won't\ntell you which tables exist.\n\nAvailable categories:\n- Energy & Power \u2014 US power plants, electricity prices, regional hourly generation/demand\n- Data Centers \u2014 facilities, GPU clusters, cooling\n- Semiconductors \u2014 AI chip specs, sales, ownership, foundry revenue, customs trade\n- Compute Pricing \u2014 GPU rental, cloud VM spot/on-demand, instance specs\n- Model Development \u2014 model specs, benchmarks, AI companies, AI polling, LLM arena\n- Inference Economics \u2014 LLM API pricing across providers\n- Macro & Trade \u2014 UN Comtrade, US Census trade flows, FRED macro series\n- Prediction Markets \u2014 Polymarket and Kalshi events, markets, trades, daily aggregates\n- Critical Minerals \u2014 USGS mineral deposits, country supply, critical materials",
                "execution": {
                  "taskSupport": "forbidden"
                },
                "inputSchema": {
                  "$schema": "http://json-schema.org/draft-07/schema#",
                  "additionalProperties": false,
                  "properties": {
                    "categories": {
                      "description": "Altdata category names (see tool description for the list)",
                      "items": {
                        "enum": [
                          "Energy & Power",
                          "Data Centers",
                          "Semiconductors",
                          "Compute Pricing",
                          "Model Development",
                          "Inference Economics",
                          "Macro & Trade",
                          "Prediction Markets",
                          "Critical Minerals"
                        ],
                        "type": "string"
                      },
                      "maxItems": 5,
                      "minItems": 1,
                      "type": "array"
                    }
                  },
                  "required": [
                    "categories"
                  ],
                  "type": "object"
                },
                "name": "list_tables"
              },
              {
                "description": "Use when you need recent news, events, or market-moving signals for specific\ntickers or sectors. For SEC filing narrative use sec_report_search instead.\n\nRecent news + market events filtered by ticker / sector / time range. Each row\nis one signal: id, headline, summary, suggested_tickers, sector, score,\ntrigger_sources, earliest_trigger_event_time, created_at, tags. Continuously\nupdating feed.\n\nCoverage:\n- ~6,900 tickers across US + ADRs of global companies\n- Cross-asset: equities, macro, geopolitics, commodities, crypto\n- Default sort by earliest_trigger_event_time DESC\n\nParameters:\n- tickers (optional): array of tickers \u2014 returns signals\n  whose suggested_tickers overlaps any of these\n- sector (optional): array of sector strings \u2014 returns signals whose sector\n  overlaps any of these\n- from_date (optional): ISO 8601 timestamp; filter earliest_trigger_event_time >= from_date\n- to_date (optional): ISO 8601 timestamp; filter earliest_trigger_event_time <= to_date\n- order_by (optional, default earliest_trigger_event_time): 'created_at' | 'earliest_trigger_event_time'\n- limit (optional, default 20, max 100): max results\n- offset (optional, default 0): pagination offset",
                "execution": {
                  "taskSupport": "forbidden"
                },
                "inputSchema": {
                  "$schema": "http://json-schema.org/draft-07/schema#",
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                      "description": "ISO 8601 timestamp; filter earliest_trigger_event_time >= from_date.",
                      "type": "string"
                    },
                    "limit": {
                      "description": "Default 20, max 100.",
                      "maximum": 100,
                      "minimum": 1,
                      "type": "integer"
                    },
                    "offset": {
                      "description": "Default 0.",
                      "minimum": 0,
                      "type": "integer"
                    },
                    "order_by": {
                      "description": "Sort column (always DESC). Default: earliest_trigger_event_time.",
                      "enum": [
                        "created_at",
                        "earliest_trigger_event_time"
                      ],
                      "type": "string"
                    },
                    "sector": {
                      "description": "Filter by sector (array overlap match).",
                      "items": {
                        "type": "string"
                      },
                      "type": "array"
                    },
                    "tickers": {
                      "description": "Filter by suggested_tickers (array overlap match).",
                      "items": {
                        "type": "string"
                      },
                      "type": "array"
                    },
                    "to_date": {
                      "description": "ISO 8601 timestamp; filter earliest_trigger_event_time <= to_date.",
                      "type": "string"
                    }
                  },
                  "type": "object"
                },
                "name": "signal_list"
              }
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      "latency_ms": 12.76,
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      "details": {
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  "server_card_payload": {
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