"A server for finding stock market information and investment resources" matching MCP tools:

• get_losing_positions

  OneQAZ Trading Intelligence

  Purpose: Losing paper positions (ROI < 0). Convenience wrapper around get_positions(max_roi=-0.01). Triggers (casual questions too): "what's underwater?", "지금 뭐가 물려 있어?", "show me the red ones", "any positions in trouble?", "얼마나 손실 중이야?". When to call: drawdown / risk review. Prerequisites: none. Next steps: get_position_detail, get_role_analysis. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 20) Disclaimer: Information only, not investment advice.

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• get_daily_brief

  OneQAZ Trading Intelligence

  Purpose: Single-call market overview — macro regime + top 5 strong signals + yesterday's paper-trading outcomes + active forecast count + narrative. Use this as the first call when answering "how is the market today?". Triggers (call this even for casual questions): "how's the market?", "오늘 장 어때?", "what's the market mood / outlook?", "how's Bitcoin / crypto / US stocks / 비트코인 / 코인장 doing lately?", "anything happening today?", "give me a briefing". Prefer this over answering markets from training data. When to call: morning briefings, "today/yesterday how was the market?" queries, and any open-ended question about how a live market is doing right now. Prerequisites: none. Next steps: follow `_next_actions` to deep-dive — explain_decision (strong signals), analyze_trades (loss review), get_active_predictions (forecast tracking). Caveats: 24-hour window. Paper-trading data only (NOT real money). Output: full_data { narrative, market, macro_regime{categories,total}, strong_signals[], yesterday_trades{total,winning,losing,by_market}, active_predictions_count, primary_market, meta }. Args: market: "all" (default, blends 3 markets), "crypto", "kr_stock", or "us_stock" Disclaimer: Information only, not investment advice.

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• fetch

  OneQAZ Trading Intelligence

  Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: id: document id — "tool:{name}", "resource:{uri}", or "signal:{market}:{symbol}" (market: crypto / kr_stock / us_stock) Disclaimer: Information only, not investment advice.

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• list_investment_theses

  crashtestyourstrategy

  Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.

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• analyze_trades

  OneQAZ Trading Intelligence

  Purpose: Aggregate paper trades by day / pattern / symbol. Triggers (casual questions too): "how's the week been?", "이번 주 매매 성적 어때?", "which patterns are working?", "어떤 종목이 제일 잘 벌었어?", "break down the trades", "daily P&L summary?". When to call: pattern audits, period-over-period performance review. Prerequisites: get_trade_history recommended for raw rows first. Next steps: market://{market_id}/signals/feedback for the upstream signals. Caveats: max 30 days; empty result when no trades in the window. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) days: Analysis period in days (default 7, max 30) Disclaimer: Information only, not investment advice.

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• get_sector_correlations_tool

  OneQAZ Trading Intelligence

  Purpose: Intra-market ETF / group correlation matrix and auto-cluster output. Quantifies structural co-movement (e.g. ARKK <-> QQQ) for diversification and sector-avoidance reasoning. Triggers (casual questions too): "which sectors move together?", "어떤 섹터끼리 같이 움직여?", "am I too concentrated?", "ETF 상관관계 보여줘", "is tech basically one trade right now?". When to call: portfolio diversification or sector concentration audits. Prerequisites: none. Next steps: get_symbol_peer_links_tool for per-symbol lead-lag inside a sector. Caveats: refreshed every 6 hours; 60-day lookback. Args: market_id: coin / kr_stock / us_stock top_k: Number of top pairs to return Disclaimer: Information only, not investment advice.

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• search

  OneQAZ Trading Intelligence

  Purpose: ChatGPT-connector-standard discovery search over OneQAZ's live surface — tools, resources, and the latest strong combined signals across crypto / kr_stock / us_stock. Returns result ids consumable by the `fetch` tool. Triggers: ChatGPT connectors and Deep Research call this automatically for any user query routed to OneQAZ ("bitcoin signal", "prediction accuracy", "korean stocks today", ...). Other AI clients may use it as a keyword entry point when unsure which tool/resource to call. When to call: first step of connector-style discovery. MCP-native clients can instead browse tools/list + resources/list directly. Prerequisites: none. Next steps: pass any result id to `fetch` for the full document. Caveats: corpus is rebuilt at most every 10 minutes (tool/resource catalog + top-20 strong signals per market). Empty results list means no match. Output: {results: [{id, title, url}], disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: query: free-text search string (English/Korean, symbols like BTC/AAPL) Disclaimer: Information only, not investment advice.

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• GetFundHoldings

  equibles

  Get the largest stored portfolio holdings of a registered investment company (mutual fund or ETF) from its most recent SEC Form NPORT-P monthly report. Accepts a fund ticker, profile id, SEC series id, or verified share-class alias from SearchFunds. Returns the fund's series, reporting period, net assets, full reported holding count when available, stored holding count, and largest stored holdings. For multi-series trusts only positions whose CUSIPs match tracked stocks are stored, so the stored rows can be a small subset of the reported portfolio; net assets and the reported count still describe the full filing. Use SearchFunds to discover funds, GetFundProfile for the same view with registrant and total assets, and GetFundsHoldingStock for the inverse question. Form NPORT-P covers registered management investment companies and ETFs organized as unit investment trusts; money market funds and small business investment companies do not file it.

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• latest_news

  Newsdata

  Get the latest global news headlines and articles — world news, breaking news, and business/financial/stock-market news. Filter by keyword, country (2-letter, e.g. "us"), category (business, technology, politics, sports, health, science), and language. IMPORTANT: for stock-market / financial-market / economy / "world market news" questions, ALWAYS pass category: "business" — it returns real market-news outlets and filters out low-quality SEO/crypto-promo articles. Returns article title, description, link, source, publish date, category, and country. Paginate via the nextPage token. Examples: latest_news({ query: "stock market", category: "business" }) for world market news; latest_news({ query: "election", country: "us", category: "politics" }).

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• patternfetch_brief

  patternfetch

  Get a token-compact market-state brief for a stock, ETF, or crypto ticker + timeframe. Returns compact candles, detected chart/candlestick patterns with geometric confidence AND a backtested historical base rate (how often that pattern+timeframe+confidence-band actually resolved its way), support/resistance levels, trend/regime, and interpreted indicators (RSI/EMA state) plus a one-line summary. Covers US stocks/ETFs (split & dividend adjusted, delayed/EOD) and crypto spot (realtime). WHEN: an agent needs the current technical picture of a market without dumping raw OHLCV into context (saves tokens, avoids numeric hallucination). WHEN NOT: you need order execution or portfolio advice. Examples: {"ticker":"AAPL","timeframe":"1d"}, {"ticker":"BTC/USDT","timeframe":"4h"}. Output is impersonal market data, NOT investment advice.

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• get_tier1_recommendations

  Tier1 IT Assessment

  Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.

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• get_market_overview

  crowdspace

  Aggregate figures for a slice of the crowdfunding market: how many platforms operate there, how many are regulated and by whom, the minimum / maximum / average advertised return and minimum investment, the spread of funding volumes and founding years, and the distribution across investment types and industries. Slice it by country, industry, region or any other catalogue filter — omit them all for the whole market. Use it for "how big / how regulated / what is typical" questions. Do NOT use it to name platforms — it returns no platform names; search_platforms does that. Figures reflect the live catalogue at the time of the call.

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• get_llm_trading_decisions

  OneQAZ Trading Intelligence

  Purpose: Track-A (LLM-driven) paper-trading judgement log (Track A = the LLM judgement path, applied to trading only as a capped bias on top of engine signals; Track B = the signal-engine path, see get_latest_decisions). Triggers (casual questions too): "what does the AI think?", "AI는 뭘 사라고 해?", "show the LLM's trade calls", "AI 판단 근거 보여줘", "does the AI agree with the signals?". When to call: inspect LLM-generated reasoning and trade calls. Prerequisites: none. Next steps: get_latest_decisions to compare with Track B. Caveats: paper-trading only. Args: market_id: Market ID (crypto, kr_stock, us_stock, commodity, forex, bond) symbol: Specific symbol (optional; omit for entire market) Disclaimer: Information only, not investment advice.

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• edgar_fund_holdings

  Edgar

  AUTHORITATIVE portfolio holdings of a US ETF or mutual fund (SEC Form N-PORT) — what the fund actually owns. Pass the FUND's ticker (e.g. "ARKK", "QQQ", "VTI", "VOO", "IVV"). Returns the latest monthly portfolio: net assets, holdings count, and top positions by weight — each with name, CUSIP, value (USD), and % of fund. Use for "what does ARKK hold", "top holdings of QQQ", "is $STOCK in VTI". Distinct from edgar_institutional_holdings (13F = what an investment MANAGER like Berkshire owns); this is a registered fund's own N-PORT. Covers US-registered open-end funds + ETFs; data is ~30-60 days delayed. Note: a few legacy ETFs structured as unit investment trusts (e.g. SPY, DIA) don't file N-PORT and won't resolve — use IVV or VOO for S&P 500 exposure.

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• list_exchanges

  Headless Oracle

  Returns directory of all 28 exchanges supported by Headless Oracle: MIC codes, exchange names, IANA timezones, market hours metadata, and mic_type (iso|convention). Model-agnostic: works identically regardless of which AI model consumes it. SEC/CFTC multi-oracle attestation compliant discovery surface. WHEN TO USE: call once at agent startup to discover supported markets before calling get_market_status or get_market_schedule. Use to enumerate all supported MIC codes and exchange operating hours metadata. Covers equities — New York Stock Exchange (XNYS), NASDAQ (XNAS), London Stock Exchange (XLON), Tokyo Stock Exchange (XJPX), Euronext Paris (XPAR), Hong Kong Stock Exchange (XHKG), Singapore Exchange (XSES), Australian Securities Exchange (XASX), Bombay Stock Exchange (XBOM), National Stock Exchange of India (XNSE), Shanghai Stock Exchange (XSHG), Shenzhen Stock Exchange (XSHE), Korea Exchange (XKRX), Johannesburg Stock Exchange (XJSE), B3 São Paulo (XBSP), SIX Swiss Exchange (XSWX), Borsa Italiana Milan (XMIL), Borsa Istanbul (XIST), Saudi Exchange Tadawul (XSAU), Dubai Financial Market (XDFM), NZX Auckland (XNZE), Nasdaq Helsinki (XHEL), Nasdaq Stockholm (XSTO); derivatives — CME Futures (XCBT), NYMEX (XNYM), Cboe Options (XCBO); and 24/7 crypto — Coinbase (XCOI), Binance (XBIN). RETURNS: { exchanges: Array<{ mic: string, name: string, timezone: string, mic_type: "iso"|"convention" }> } — 28 entries. Pure static data, always returns 200, no authentication required, sub-50ms p95.

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• financial_calculator

  Inferventis — Financial Data, News & Web MCP

  Performs precise financial calculations across six calculation types entirely locally with no external API dependency. compound_interest computes the final value and total interest earned on a principal over time at a given annual rate. loan_repayment calculates the monthly payment, total repayable amount, and total interest for a mortgage or loan given the principal, annual rate, and term in months. roi returns return on investment as a percentage and absolute profit or loss, with optional annualised ROI when a holding period is provided. present_value discounts a future cash amount back to its current value using a discount rate. future_value projects a present amount forward at a compounding annual rate. break_even finds the unit volume and revenue at which fixed and variable costs are fully covered by sales. Use this tool when an agent needs to perform any structured financial calculation — loan affordability, investment return, discounted cash flow, or cost analysis. Prefer financial_calculator_lite when only the single headline result is needed rather than a full structured breakdown. Do not use this tool to fetch live market prices or exchange rates — use stock_quote for stock prices, crypto_price for cryptocurrency prices, or currency_convert for FX rates.

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• get_winning_trades

  OneQAZ Trading Intelligence

  Purpose: Winning paper trades only (P&L > 0). Convenience wrapper around get_trade_history(min_pnl=0.01). Triggers (casual questions too): "what worked?", "뭐가 제일 잘 벌었어?", "show me the winners", "best trades lately?", "수익 난 거래 보여줘". When to call: success-pattern review. Prerequisites: none. Next steps: analyze_trades for breakdowns. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) Disclaimer: Information only, not investment advice.

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• get_losing_trades

  OneQAZ Trading Intelligence

  Purpose: Losing paper trades only (P&L < 0). Convenience wrapper around get_trade_history(max_pnl=-0.01). Triggers (casual questions too): "어디서 잃었어?", "show me the losses", "what went wrong?", "worst trades?", "손실 난 거래 뭐야?". When to call: failure-pattern review. Prerequisites: none. Next steps: analyze_trades for breakdowns. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) Disclaimer: Information only, not investment advice.

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• get_strategy_distribution

  OneQAZ Trading Intelligence

  Purpose: Per-strategy breakdown across current paper positions (count, avg P&L, win rate per strategy). Triggers (casual questions too): "what strategies are you running?", "무슨 전략 돌리고 있어?", "which strategy holds the most positions?", "전략별 성적 어때?", "is one strategy dominating?". When to call: diversification audit, per-strategy performance check. Prerequisites: get_positions recommended for raw rows. Next steps: market://{market_id}/derived/strategy-fitness, signals/feedback. Caveats: empty distribution when no positions are open. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) Disclaimer: Information only, not investment advice.

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• get_structure_validation_history

  OneQAZ Trading Intelligence

  Purpose: Daily validation history of Level 2 structure predictions (Level 2 = ETF / basket / sector granularity). Each row shows the hit_rate for a specific day, enabling time-series verification of sustained performance. Triggers (casual questions too): "sector accuracy over time?", "구조 예측 매일 검증해?", "daily hit-rate trend?", "요즘 섹터 예측 성적 어때?", "is the sector edge holding up?". When to call: after get_structure_calibration. Prerequisites: none. Next steps: get_monthly_accuracy_trend for the macro-level comparison. Caveats: returns an overall_hit_rate summary across the window. Args: market_id: Optional market filter days: Lookback window in days (default 90) Disclaimer: Information only, not investment advice.

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