Drillr — The financial MCP for AI agents

Use to discover which SEC filings exist for a ticker before searching content. For the actual content use filing_search instead. List indexed SEC filings for a given ticker with a summary header. Returns: summary (period coverage, per-type counts) + table of up to 50 filings (fiscal_year, fiscal_quarter, filing_type, filing_date, period_start, period_end). filing_types filter: omit for main reports only (US 10-K/10-Q/20-F/S-1/DEF 14A + /A amendments; JP 120/140/160; A-share annual_report / quarterly_report / q1_report; excludes ad-hoc 8-K/6-K); pass [] for all indexed types; pass explicit allowlist to override.

{
  "type": "object",
  "properties": {
    "ticker": {
      "type": "string",
      "description": "Stock ticker, e.g. NVDA, 6758.T, 00700.HK, 600519.SH"
    },
    "filing_types": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "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 — use values from the `filing_type` column of a prior unfiltered call."
    }
  },
  "required": [
    "ticker"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Search one company's SEC filings. Returns `## Facts` (exact as-reported and restated financial values) and `## Passages` (matching filing text) — both come back in one call. `ticker` is REQUIRED. When `## Facts` is empty, read `## Passages` — the figure is usually stated in the filing text. `period_start`/`period_end` match by interval overlap; `fiscal_period` sets granularity (Q1..Q4/H/9M/FY). Pass an explicit period window for the most recent figure.

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "minLength": 1,
      "description": "Natural-language financial metric query"
    },
    "ticker": {
      "type": "string",
      "minLength": 1,
      "description": "Required. Canonical or historical ticker; one only. Resolve company names with ticker_lookup first"
    },
    "period_start": {
      "type": "string",
      "description": "Calendar start date YYYY-MM-DD (calendar, not fiscal; resolve fiscal periods via financial_statements period_start/period_end)"
    },
    "period_end": {
      "$ref": "#/properties/period_start",
      "description": "Calendar end date YYYY-MM-DD (calendar, not fiscal)"
    },
    "as_of": {
      "$ref": "#/properties/period_start",
      "description": "Publication cutoff date YYYY-MM-DD. Rows with missing published_at still appear; not a strict point-in-time snapshot"
    },
    "top_k": {
      "type": "integer",
      "minimum": 1,
      "maximum": 30,
      "description": "Max results; 1-30, default 10"
    },
    "period_type": {
      "type": "string",
      "enum": [
        "instant",
        "duration"
      ],
      "description": "instant or duration"
    },
    "fiscal_period": {
      "anyOf": [
        {
          "type": "string",
          "enum": [
            "Q1",
            "Q2",
            "Q3",
            "Q4",
            "H",
            "9M",
            "FY"
          ]
        },
        {
          "type": "array",
          "items": {
            "$ref": "#/properties/fiscal_period/anyOf/0"
          },
          "minItems": 1,
          "maxItems": 7
        }
      ],
      "description": "Q1 | Q2 | Q3 | Q4 | H | 9M | FY, or a list of those"
    }
  },
  "required": [
    "query",
    "ticker"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Use for qualitative company discovery (industry, business model, supply chain, competitors, management background). For numerical screening (revenue, margins, ratios, growth rates) use run_sql on company_snapshot instead. Drillr's company knowledge graph — searchable across industry classification, product offerings, business model, segment structure, competitive landscape, supply chain, management background, and customer profile. Coverage: US, Japan, Hong Kong, China A-shares, and Korea. `market` accepts one lowercase value or a list from `us | jp | hk | cn | kr`; omit it or pass `[]` for all five. List order does not set priority. Pass a natural-language description (for example, "Hong Kong and China EV battery suppliers"). Returns a structured list of matching companies with context snippets. ONLY for finding a LIST of companies by description.

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "minLength": 1,
      "description": "Natural-language company description"
    },
    "market": {
      "anyOf": [
        {
          "type": "string",
          "enum": [
            "us",
            "jp",
            "hk",
            "cn",
            "kr"
          ]
        },
        {
          "type": "array",
          "items": {
            "$ref": "#/properties/market/anyOf/0"
          },
          "maxItems": 5
        }
      ],
      "description": "Optional market filter. Pass one lowercase value or a list from 'us' | 'jp' | 'hk' | 'cn' | 'kr'. Omit or pass [] for all five; list order does not set priority."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Resolve a company name, brand, or ticker substring to canonical ticker(s). Input: - query (required): company name, brand, or ticker substring, e.g. "Apple", "AAPL", "OpenAI" - market (optional): "us" | "jp" | "cn" — omit to search all markets Returns up to 5 matches ranked by prefix-hit first, then name length; symbols carry their market suffix.

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "minLength": 1,
      "description": "Company name or ticker substring (case-insensitive). Matches historical names + tickers too."
    },
    "market": {
      "type": "string",
      "enum": [
        "us",
        "jp",
        "hk",
        "cn",
        "kr"
      ],
      "description": "Optional market filter: 'us' | 'jp' | 'cn'. Omit to search all markets."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

PostgreSQL SELECT over financial / market / alt-data tables — returns structured rows. Hard rules (query fails otherwise): - SELECT only, no CTE (`WITH ... AS`) — use subqueries. - Period columns are TEXT, not dates — `period_end` is 'YYYY-MM'. Compare as strings (`period_end >= '2024-01'`); a `::date` cast on it fails. - Filter structured tables by ticker (`WHERE ticker IN ('AAPL','MSFT')`; screening: add `ticker NOT LIKE '%-%'` to drop preferred stock). Tables by domain (get_table_schema gives columns + coverage note): - Market: price_volume_history (OHLCV history; MUST filter ticker + time_frame), index_price, equity_extended_rt (pre/after/overnight quotes) - Fundamentals: financial_statements (GAAP income/balance/cashflow), company_snapshot (ratios, per-share, growth) - Earnings: earning_call_summary, earning_call_calendar - Analyst: analyst_ratings, analyst_ratings_consensus - Ownership: insider_and_institution_activities - 8-K events: executive_change, company_deal_events, debt_issuance, securities_offering - Executives: executive_profile, executive_compensation - Alt-data: macro / industry / trade / AI-supply-chain — call list_tables(categories=[...])

{
  "type": "object",
  "properties": {
    "sql": {
      "type": "string",
      "description": "PostgreSQL SELECT query"
    }
  },
  "required": [
    "sql"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Column definitions (name, type, description) for a data table, plus its usage note where one exists: required filters, ticker format, and market coverage.

{
  "type": "object",
  "properties": {
    "table_name": {
      "type": "string",
      "enum": [
        "financial_statements",
        "company_snapshot",
        "price_volume_history",
        "earning_call_summary",
        "insider_and_institution_activities",
        "index_price",
        "equity_extended_rt",
        "analyst_ratings",
        "analyst_ratings_consensus",
        "earning_call_calendar",
        "executive_profile",
        "executive_compensation",
        "executive_change",
        "company_deal_events",
        "debt_issuance",
        "securities_offering",
        "eia_generators",
        "eia_retail_prices",
        "eia_regional_hourly",
        "epochai_data_centers",
        "epochai_data_center_timelines",
        "epochai_gpu_clusters",
        "epochai_dc_cooling_towers",
        "epochai_dc_chillers",
        "epochai_chip_types",
        "epochai_ml_hardware",
        "epochai_chip_organizations",
        "epochai_chip_sales_cumulative",
        "epochai_chip_sales_by_chip",
        "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"
      ]
    }
  },
  "required": [
    "table_name"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

List alternative-data tables under the given categories. Returns each table's name, one-line purpose, and column names (call get_table_schema if you need column types/comments). Batch up to 5 categories in one call; omit categories, or pass ["all"], to get the category index instead. Use this BEFORE run_sql when you want to explore alt-data — run_sql alone won't tell you which tables exist. Available categories: - Energy & Power — US power plants, electricity prices, regional hourly generation/demand - Data Centers — facilities, GPU clusters, cooling - Semiconductors — AI chip specs, sales, ownership, foundry revenue, customs trade - Compute Pricing — GPU rental, cloud VM spot/on-demand, instance specs - Model Development — model specs, benchmarks, AI companies, AI polling, LLM arena - Inference Economics — LLM API pricing across providers - Macro & Trade — UN Comtrade, US Census trade flows, FRED macro series - Prediction Markets — Polymarket and Kalshi events, markets, trades, daily aggregates - Critical Minerals — USGS mineral deposits, country supply, critical materials

{
  "type": "object",
  "properties": {
    "categories": {
      "type": "array",
      "items": {
        "type": "string",
        "enum": [
          "Energy & Power",
          "Data Centers",
          "Semiconductors",
          "Compute Pricing",
          "Model Development",
          "Inference Economics",
          "Macro & Trade",
          "Prediction Markets",
          "Critical Minerals",
          "all"
        ]
      },
      "maxItems": 5,
      "description": "Altdata category names (see tool description for the list). Omit, or pass \"all\", for the category index."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Use for any news, event, development, or statement question about a company, theme, or the market. The `ticker` filter takes exchange-suffixed symbols. Returns Markdown: a `## Stories` numbered list (each storyline once), then flat `## Events` and `## Claims` tables (claims = attributed statements: analyst actions, corporate guidance, central-bank remarks). The Events `story` column refers back to the Stories number. `sources` counts corroborating reports; `first_reported`/`last_reported` give the reporting span. Lowest-ranked stories are dropped to fit length; the meta line flags how many were omitted. At least one of query/theme/ticker/since/until is required. Per-parameter detail is on the input schema — search_type=claims needs query/ticker/a time window, not theme.

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Semantic query (English). One of query/theme/ticker/since/until required."
    },
    "theme": {
      "type": "string",
      "description": "Theme word, resolved to the nearest canonical theme. Not valid with search_type=claims."
    },
    "ticker": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "array",
          "items": {
            "type": "string"
          }
        }
      ],
      "description": "Exact ticker symbol(s) — a single symbol, an array, or a comma-separated string; multiple tickers are an OR/overlap filter. Exchange-suffixed (AAPL, 7203.T, 600519.SH). Company names/brands are NOT resolved here."
    },
    "since": {
      "type": "string",
      "description": "ISO8601; filter time_event >= since."
    },
    "until": {
      "type": "string",
      "description": "ISO8601; filter time_event < until."
    },
    "search_type": {
      "type": "string",
      "enum": [
        "all",
        "events",
        "claims"
      ],
      "description": "all (default) | events | claims (opinions/statements only)."
    },
    "order_by": {
      "type": "string",
      "enum": [
        "relevance",
        "event_time",
        "create_time"
      ],
      "description": "Result ordering. relevance (default) | event_time (newest event time first) | create_time (most recently ingested first)."
    },
    "top_k": {
      "type": "integer",
      "minimum": 1,
      "maximum": 50,
      "description": "Story count. Default 10, max 50."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Search industry inflections identified through structured research of earnings calls held by US-listed companies, including the change mechanism, impact scope, market attention and affected companies. All filters are optional and combine with AND. With no filters, returns the newest first page. Results are ordered by `quarter` descending. If nothing matches, returns the text `No relevant industry inflections found.` Returns JSON as `{ "data": [...] }`. Every result contains `quarter`, `name` (English title), `regime_type` (change mechanism), `impact_scope`, `impact_degree` (`limited` | `significant` | `structural`), `attention_verdict` (market-absorption judgment), `change_summary`, `first_seen` (`YYYY-MM-DD`), and `source_tickers` (companies whose calls are primary evidence). When `impact_companies` is true, `company_impacts` contains items with `ticker`, `relation`, `direction`, `magnitude`, `impact_stage`, `evidence_status`, `affected_business`, and `impact`.

{
  "type": "object",
  "properties": {
    "ticker": {
      "anyOf": [
        {
          "type": "string",
          "maxLength": 16,
          "pattern": "^[A-Z0-9]+(?:[.-][A-Z0-9]+)*$"
        },
        {
          "type": "array",
          "items": {
            "$ref": "#/properties/ticker/anyOf/0"
          },
          "minItems": 1,
          "maxItems": 10
        }
      ],
      "description": "Optional company filter, up to 10 US ticker symbols. Returns themes where any supplied ticker is a source company or an affected company. Use symbols such as AAPL, not company names."
    },
    "keyword": {
      "type": "string",
      "minLength": 1,
      "maxLength": 200,
      "description": "Optional case-insensitive text contained in the theme name or research summary, up to 200 characters."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10,
      "description": "Results per page. Default 10, max 10."
    },
    "page": {
      "type": "integer",
      "minimum": 1,
      "description": "One-based page number. Default 1."
    },
    "impact_companies": {
      "type": "boolean",
      "default": false,
      "description": "Include the per-company company_impacts list. Default false."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Search concrete enterprise AI applications disclosed in US company earnings calls. Filter by ticker, partially match a company name, search for an application or workflow by name, or use since in YYYY-MM-DD format to include only observations updated on or after that date. Returns a data array ordered by update_date descending. Each result contains ticker, company_name, application_name, first_report_date, update_date, summary (an AI application summary), evolution_summary, business_position, deployment_stage, deployment_scope, value_type, metrics (application-related metrics), and evidence (supporting management quotes, with speaker and section when available). Use this tool to identify where and how a company applies AI, assess deployment maturity, scope, and disclosed value, and inspect the supporting evidence. Use no filters to browse the most recently updated observations. No matches return an empty data array.

{
  "type": "object",
  "properties": {
    "ticker": {
      "anyOf": [
        {
          "type": "string",
          "maxLength": 16,
          "pattern": "^[A-Z0-9]+(?:[.-][A-Z0-9]+)*$"
        },
        {
          "type": "array",
          "items": {
            "$ref": "#/properties/ticker/anyOf/0"
          },
          "minItems": 1,
          "maxItems": 10
        }
      ],
      "description": "Optional US ticker filter, up to 10 symbols. Accepts one symbol or a list. Company names are not resolved."
    },
    "company_name": {
      "type": "string",
      "maxLength": 200,
      "description": "Case-insensitive partial company-name match. Empty means no filter."
    },
    "application_name": {
      "type": "string",
      "maxLength": 200,
      "description": "Case-insensitive partial application-name match. Empty means no filter."
    },
    "since": {
      "type": "string",
      "description": "Only return observations with update_date on or after this date. Use YYYY-MM-DD."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 10,
      "description": "Results per page. Default 10, max 10."
    },
    "page": {
      "type": "integer",
      "minimum": 1,
      "description": "One-based page number. Default 1."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}