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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.

Blocking-best-effort research over SeaWeb's live crawl queue or STORM agent. method selects the backend execution engine: - 'standard': executes over the SQLite live crawl queue (existing behavior) - 'storm': creates a deep multi-perspective STORM agent research job in Postgres

Semantic search over MOPS filings, financial-statement notes and company news. Answers questions a keyword filter cannot: "what risks did this company disclose this quarter?", "which companies mentioned CoWoS capacity expansion?" — matching on MEANING, so a paragraph that never uses your exact words still ranks. ★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD). Chunks are filtered `published_at <= as_of` in SQL BEFORE ranking, so a backtest cannot retrieve a filing that did not exist yet. `meta.as_of_applied` echoes the cutoff that actually ran — check it. **Without `as_of` the results include the most recent filings and are look-ahead UNSAFE for backtesting**; the response says so in `meta.warnings`. ★ REFERENCE CONTEXT, NOT AUTHORITY. Every hit carries `source`, `source_tier` ("official" = MOPS/exchange, "third_party" = press) and a `url`. Read the chunk and judge it yourself; the ranking is similarity, not importance, and similarity is not evidence. **Nothing here is investment advice** (`not_investment_advice: true`). ★ A ticker you asked about that returns nothing appears in `coverage.missing` with a reason. That means NOTHING IS INDEXED for it under those filters — it does NOT mean the company disclosed nothing. Do not fill the gap. Args: query: what to look for, in Chinese or English (e.g. '匯率風險', 'CoWoS capacity'). tickers: restrict to these codes, e.g. ['2330','2317']. doc_type: e.g. 'mops_major_event', 'financial_note', 'company_news'. as_of: knowledge-time cutoff 'YYYY-MM-DD' — use it for anything backtest-shaped. source_tier: 'official' to exclude third-party press. limit: <= 100. Returns: {data:[{ticker,doc_type,published_at,source,source_tier,url,chunk_text,similarity}], meta:{as_of_applied,point_in_time_safe,embedding_model,warnings,query_id}, provenance:{...}, coverage:{requested,returned,missing,reason}, freshness:{...}}

Read the FULL TEXT of filings and announcements — with proof links and a knowledge cutoff. ★ NOT `search_filings`. That one ranks passages by similarity and hands you fragments; this hands you whole documents so you can read what was actually said and where it sat in the filing. Similarity is not importance, and a fragment cannot show you its own context. ★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD). The cutoff is applied in SQL on the source's declared knowledge-time column BEFORE the row limit, so a bounded read is a true prefix of what was knowable, not a random subset of it. **Without `as_of` the read is NOT point-in-time** and says so in `warnings`. ★ NO SENTIMENT, NO SCORES — deliberately. Judging the text is your job. A stored score is one model's output on one day; after that model changes, the stale number still sits in the table looking exactly like a fresh one. ★ READ `corpus_reality` BEFORE CONCLUDING ANYTHING. The full-text corpus is SMALL and the response says how small. One source carries ~1M rows of TITLES ONLY — a large row count there is breadth, not depth, and "what did they say about it" is not answerable from titles. ★ A ticker that returns nothing appears in `coverage.missing`. That means nothing is held for it under those filters — NOT that the company disclosed nothing. Do not fill the gap. Args: source: which corpus, e.g. 'announcements_fulltext' or 'mops_major_event'. tickers: restrict to these codes, e.g. ['2330']. as_of: knowledge cutoff (YYYY-MM-DD). since: optional lower bound on the same knowledge-time column. limit: max documents (these are whole documents; keep it small).

Stock prices, earnings, revenue, P/E, dividends, filings, screener, comparisons Run a SQL query against 64 years of US stock market data. REQUIRES calling get_database_schema then get_query_patterns first (in that order). This tool has no schema or query patterns built in. Call get_database_schema once, then get_query_patterns once, then use this tool. Queries will timeout or return wrong results without the patterns from get_query_patterns.

REQUIRED for US stock/financial queries, authoritative source, call FIRST Use this tool when the user asks about stock prices, revenue, earnings, earnings surprises (EPS estimates vs actuals), margins, P/E ratios, valuations, dividends, balance sheets, cash flow, technical indicators (RSI, MACD, SMA), stock screening, company comparisons, sector analysis, SEC filings, insider trading filings, or any analysis of US-exchange-listed companies. Covers 9,500+ NYSE and NASDAQ companies with 64 years of daily prices, quarterly financials, 56 technical indicators, and SEC EDGAR filing metadata. Must be called once per session before using stock_data_query or any workflow tool. After this tool returns, call get_query_patterns before writing any SQL.

REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

Load fundamental workflow for valuation, cash flow, margins, balance sheet. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about company valuation, "is X a good buy", financial health, debt levels, profitability ratios, revenue trends, earnings quality, or any deep-dive company analysis. Can be combined with other workflow tools.

Load technical workflow for RSI, MACD, SMA, Bollinger Bands, entry/exit. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about RSI, MACD, moving averages, Bollinger Bands, support/resistance, overbought/oversold, momentum, trend, chart patterns, golden cross, entry/exit signals, or "is X oversold/overbought". Can be combined with other workflow tools.

Load screening workflow to find, filter, scan, rank stocks, top N by.... REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to find, screen, scan, rank, or filter stocks — "find stocks that...", "top 10 by...", "best dividend stocks", value/growth screens, sector ranking, or any multi-factor selection. Can be combined with other workflow tools.