Drillr — The financial MCP for AI agents MCP server

• ai_adoptionFree

  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.

• company_searchFree

  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.

• filing_listFree

  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.

• filing_searchFree

  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.

• get_table_schemaFree

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

• industry_inflectionsFree

  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`.

• list_tablesFree

  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

• news_searchFree

  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.

• run_sqlwrite actionFree

  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=[...])

• ticker_lookupFree

  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.

• –Code scanremote-only server, no package to scann/a
• Live reliabilityremote reachable in 441ms20/20
• Tool poisoning10 tool descriptions checked15/15
• Auth qualityopen endpoint exposes 1 write-action tools with no auth3/15
• Maintenancelast push 6 days ago15/15
• Maintainer identitynamespace and repository owner differ; website matches verified namespace6/10

Drillr — The financial MCP for AI agents: common questions

Is Drillr — The financial MCP for AI agents MCP server safe?

Mostly: it is graded B (79/100). Read the Drillr — The financial MCP for AI agents safety report

How do I install Drillr — The financial MCP for AI agents?

It runs remotely at gateway.drillr.ai. Add it to Claude Code, Claude Desktop or Cursor with the snippets above, or call it through the mcp.market gateway without installing anything.

Does Drillr — The financial MCP for AI agents need an API key?

Not as far as the registry entry and our scan can tell: no credentials are declared or required.

Is Drillr — The financial MCP for AI agents maintained?

The last commit was 8 days ago (2026-09-13). The latest release is v2.3.0.

Is Drillr — The financial MCP for AI agents up?

100% of our last 7 checks got an answer. We check remote servers about four times a day.