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Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.

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.

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). Core equity coverage: US, Japan, Hong Kong, China A-shares, and Korea. Tickers are US bare (AAPL), Japan `.T` (6758.T), Hong Kong `.HK` (00700.HK), A-shares `.SH`/`.SZ` (600519.SH), and Korea `.KS`/`.KQ` (005930.KS). financial_statements, company_snapshot, and price_volume_history span all five. Specialized tables may be narrower — call get_table_schema before treating an empty result as a finding. Tables by domain (call get_table_schema for detail): - 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=[...])

Use BEFORE run_sql when you're unsure which columns a table has. Look up column definitions (name, type, description) for a data table.

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

Search this playbook's complete tool catalog by keyword: the built-in playbook tools (memory, skills, canvas, workflow runs, secrets) and every connected server's federated tools (names like supabase__execute_sql or cloudflare__search). Matches against tool names and descriptions; a name match ranks above a description match. Returns up to `limit` (default 10, max 25) entries with name, description, and full input schema. Every returned tool can be called directly by name even when it is absent from tools/list — the advertised list is a view, not a boundary, unless this connection was pinned with ?toolset=. Read-only and free of side effects. Use this when the tool you need is not in your current list, before concluding a capability is missing. Pass playbook_id as the UUID or GUID of the playbook this call should target.

FINAL-GATE Output Sanitizer ($0.02). Post-processing checkpoint that strips secrets/credentials, PII, crypto material (seed phrases, private keys, wallet addresses), internal network details (private IPs, MAC addresses, .internal/.local/.corp hostnames), and unsafe shell/SQL/path-traversal fragments from a raw model response — plus a narrow, high-precision safety net for direct self-harm-instruction-seeking and targeted-harassment phrasing (not a general toxicity classifier). Returns a single `sanitized_output` with every match replaced by `[REDACTED]`; use that instead of the original whenever verdict is NO_COMMIT. Run this as the LAST gate before a response reaches its destination — after `dcl_evaluate_jailbreak_crypto`/other input-side checks have already run, and immediately before `dcl_commit` seals the final decision. Internally re-uses the same detection tables as `dcl_evaluate_secrets`/`dcl_evaluate_pii` for the secrets/PII categories, so results stay consistent with those tools.

Search the user's memories. Default mode is 'hybrid': blends semantic (cosine over Vectorize) and keyword (BM25 over SQLite FTS5) hits via Reciprocal Rank Fusion (k=60). Semantic catches paraphrases; keyword catches exact-string hits (IDs, dates, code snippets). Pass mode:'semantic' or mode:'keyword' to run just one. Content defaults to the LLM-optimized (compressed) form when available (raw:true to invert). Excludes forgotten + superseded. Optional filters narrow after retrieval: tags (AND), written_by, session_id, and/or since/until (ms epoch).

List the user's most recent memories in reverse chronological order. Use for browsing or catching up on what the user's other model sessions have written recently. Same content/content_raw shape as memory_search. Optional filters (tags, written_by, session_id, since, until) narrow the listing at the SQL level.

Inspect the schema of the local finbridge database (SQLite with ingested KR/US company fundamentals, filings, and daily prices): tables, views, columns, per-table row counts (counted in the background and refreshed every 30 minutes; null with rows_note "counting…" right after a server start), and ready-to-run example queries for query_db. Read this before writing a query_db statement. It returns no company data itself — get_db_schema describes the tables, query_db runs the SELECT. Args: (none) Returns: {tables: [{name, columns: [{name, type}], rows}], views: [{name, columns: [{name, type}]}], examples: [sql_string]} Key objects: - companies: KR companies have source='dart' + stock_code (6-digit), US companies source='edgar' + ticker - financials: one row per company x fiscal_year x quarter (quarter=0 = annual); raw unscaled KRW/USD amounts - prices_daily: daily OHLCV per company_id - views v_financials (financials joined with company name/ticker/stock_code) and v_latest_annual (latest annual row per company) — prefer these in query_db Examples: - Call before writing SQL for query_db, to learn table/column names. - Check row counts to see how much data the nightly ingest has loaded. Use when: preparing a query_db, or checking ingest coverage. Don't use for market data itself (get_stock_prices / get_valuation / the screeners read the same tables with the right joins already done). FinBridge has no real-time equity quote tool — equity prices here are end-of-day closes from the nightly ingest; the only live data is crypto (get_crypto_ticker) and regulator filings (get_dart_filings / get_edgar_filings). Errors: 'database has not been built yet' — the ingest pipeline has not run on the server.

Run a single read-only SELECT query against the local finbridge database (ingested KR/US fundamentals, filings, daily prices). The statement must start with SELECT or WITH; multiple statements, PRAGMA, and any write/DDL keywords (INSERT/UPDATE/DELETE/DROP/ALTER/CREATE/ATTACH/...) are rejected. The query runs in a separate read-only process with SQLite authorization, a 2-second deadline, two concurrent queries per server process, and a 1 MB result budget. Free accounts cannot query raw history or history views; the latest-annual snapshot remains available. The escape hatch for questions no dedicated tool answers — Japan, Taiwan and Europe are largely reachable only this way. Prefer screen_companies for ordinary fundamental screens (it handles per-market period and currency rules that a hand-written query will get wrong), and call get_db_schema first for the table shapes. Args: - sql: one SELECT (or WITH ... SELECT) statement. A single trailing ';' is tolerated. - limit: max rows returned, 1-500 (default 50) - response_format: 'markdown' (default, table) or 'json' (compact) Returns: {columns: [name], rows: [[cell, ...]], row_count, truncated} — truncated=true means more rows matched than 'limit'. Examples (v_financials / v_latest_annual views are the easiest entry points): - Largest companies by latest annual revenue: "SELECT name, ticker, stock_code, fiscal_year, revenue FROM v_latest_annual ORDER BY revenue DESC LIMIT 10" - Samsung Electronics annual trend: "SELECT fiscal_year, revenue, operating_income, net_income FROM v_financials WHERE stock_code = '005930' AND quarter = 0 ORDER BY fiscal_year DESC" - KR vs US company counts: "SELECT source, COUNT(*) AS n FROM companies GROUP BY source" - Recent Samsung Electronics closes: "SELECT date, close FROM prices_daily p JOIN companies c ON c.id = p.company_id WHERE c.stock_code = '005930' ORDER BY date DESC LIMIT 20" (prices_daily holds KR, US, TW; US history starts 2023-03-28) Use when: custom aggregation/joins over ingested data that screen_companies cannot express. Don't use for anything that writes — it will be rejected. FinBridge has no real-time equity quote tool — equity prices here are end-of-day closes from the nightly ingest; the only live data is crypto (get_crypto_ticker) and regulator filings (get_dart_filings / get_edgar_filings). Errors: non-SELECT input, ';' inside, or forbidden keywords -> rejected with the reason; unknown table/column -> SQL error with a hint to call get_db_schema first.

Screen KR/US companies by technical signals over the latest indicator snapshots (v_indicators / indicators_latest, refreshed nightly). Signals and the sort key are fixed whitelists mapped to SQL predicates; every threshold is bound as a parameter, so inputs are never interpolated into SQL. This is the open-ended technical screen: you pick the signals and thresholds. The four named strategies are fixed checklists instead (screen_minervini, screen_canslim, screen_kell, screen_schwartz). Not this tool for: fundamentals (screen_companies) or funds (screen_etfs). Args: - market: 'kr' (DART), 'us' (EDGAR), or 'all' (default) - signals: any of golden_cross, dead_cross, rsi_oversold (RSI<30), rsi_overbought (RSI>70), near_52w_high (within 3% of high), near_52w_low, above_sma20, volume_surge (vol_ratio>=2), macd_bullish (macd_hist>0), rs_leader (RS rating >=80 vs home market), rs_outperform (RS rating >=60). ANDed together; omit for none. - min_price: optional minimum close; min_vol_avg20: optional minimum 20-day average volume (liquidity filter) - sort_by: ret_1d|ret_5d|ret_20d|ret_60d|ret_120d|ret_250d|rsi14|vol_ratio|pct_from_52w_hi|pct_from_52w_lo|close|atr14|rs_pctile|rs_120d (default ret_20d) - order: 'asc'|'desc' (default 'desc'); limit: 1-100 (default 20); response_format: 'markdown'|'json' Relative strength (rs_pctile 1-99, rs_120d) measures each stock vs its OWN national market (KR vs the KR universe, US vs the US universe): rs_pctile is the national percentile of blended 3/6/12-month momentum (IBD-style; 99=strongest); rs_120d is 6-month excess return in pp over the national median. Returns: {count, market, signals, sort_by, order, rows:[{name, source, ticker|stock_code, as_of, close, rsi14, macd_hist, ret_5d, ret_20d, ret_60d, vol_ratio, pct_from_52w_hi, pct_from_52w_lo, golden_cross, dead_cross, above_sma20, rs_pctile, rs_120d}]}. Examples: - Oversold KR names by 20-day return: {market:'kr', signals:['rsi_oversold'], sort_by:'ret_20d', order:'asc'} - US breakouts near highs on volume: {market:'us', signals:['near_52w_high','volume_surge'], min_vol_avg20: 1000000} - Strongest KR leaders vs the KOSPI/KOSDAQ universe: {market:'kr', signals:['rs_leader'], sort_by:'rs_pctile', min_vol_avg20: 100000} Use when: ranking/filtering many companies by momentum or trend signals. Don't use for one company's detail (get_technicals) or fundamentals (screen_companies). Notes: KR/US/TW prices are adjusted for corporate actions but not dividends (indicators around dividend events may be slightly distorted); US history starts 2023-03-28 (volume from 2024-07-01) so long-window figures are shallower there. Market data, not investment advice. Errors: an empty result is not an error (count 0); 'database has not been built yet' -> ingest/indicators has not run.

SAFETY / OVERSIGHT CHECK before a dangerous or destructive action (shell command, file deletion, DB statement, network call). Call this to have VITNA check the action BEFORE you run it: it flags dangerous shell / SQL / secret-exfil / prompt-injection / suspicious-network patterns and returns { decision: allowed|blocked|flagged }. RESPONSE SHAPE DEPENDS ON THE KEY: an unclaimed trial key gets the decision label only, because the pattern detail is what makes the classifier worth stealing. A claimed key additionally gets threat_category, severity, reason (the rule names that matched, never the matched text), a threats[] array and a redacted echo of what was scanned, plus a signed audit record the user can review. VITNA evaluates and records; it does NOT enforce, so treat blocked / flagged as a stop and get human approval. This is how a user keeps you in check. Heuristic pattern match, not a sandbox: novel or obfuscated payloads can pass.

USE for person or word name text only (gematria → cosmic cards + Expression / Soul Urge / Personality). NOT for any date questions (weekday, leap year, moon, season, Rx, Cosmic card, day_ruler, Life Path) → get_date; two people → get_compatibility. RETURNS Francis Bacon cards, Latin cipher suite, periodic{} + periodic_set{}, numerology (expression/soul_urge/personality plus full-name karmic_lessons / hidden_passion / cornerstone / capstone / first_vowel / echoes and number lens polarity/planet/element/color), and per-word tarot/medicine/iching/chakra/lenormand/karma/planetary_spread. Free-text input guard (Del as a name is allowed; DEL FILE and SQL verbs are not).

Screen untrusted text for common SQL-injection signals without echoing it.