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751,118 tools. Updated 2026-11-06 12:52

"Stock Market" matching MCP tools:

  • [RECEIPTS] Per-non-crypto-asset-class forward-return scoreboard (asset_class = stock | index | metal | commodity). Measured on that class's own rows + baseline (stock excess vs SP500; index/metal/commodity absolute). Intel-only: the tradeable badge is informational, non-crypto is not auto-traded yet. status=accruing until a (type,direction) reaches the min sample. Same data as REST /proof?asset_class=. For the crypto board use get_performance or REST /proof. Not financial advice.
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  • [RECEIPTS] Per-non-crypto-asset-class forward-return scoreboard (asset_class = stock | index | metal | commodity). Measured on that class's own rows + baseline (stock excess vs SP500; index/metal/commodity absolute). Intel-only: the tradeable badge is informational, non-crypto is not auto-traded yet. status=accruing until a (type,direction) reaches the min sample. Same data as REST /proof?asset_class=. For the crypto board use get_performance or REST /proof. Not financial advice.
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  • Individual disclosed congressional stock trades, newest disclosure first. The paging tool: use it when get_member or get_stock's 20 most recent trades are not enough, or to walk the whole record. Filter by ticker, by member_id, by both (one member's trades in one stock), or by neither (every trade). Each row has the ticker and asset name, purchase/sale/exchange, trade date, disclosure date, days between them, whether that passed the 45-day STOCK Act window, the reported amount range, the owner (self, spouse, dependent child), the member, and a link to the original House Clerk PDF or Senate eFD filing. `total` is the full match count, so keep raising `offset` by `limit` until you reach it. Access: needs the agent token from a free Coldpine account, sent as 'Authorization: Bearer <token>'; without it the call returns setup steps instead of data. Read-only, cached for up to an hour.
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  • Show which tracked 13F managers disclosed a stock, their weights, changes and history. Exact stock/ETF ticker (share-class punctuation accepted); searches full books, including small positions and exits. quarters=1 (latest per manager) to 12. Report dates may differ by manager and are always returned. No matches means not disclosed by these managers, not no institutional ownership. Partial data is explicit. For a complete stock report prefer get_ticker_deep_dive, which includes latest 13F ownership automatically. limit=1..100; later pages require the returned snapshot_version with next_offset and the original filters.
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  • Get the stocks whose daily price returns are most (or least) correlated with one stock — Pearson correlation of daily log returns on comparable raw closes (dividends excluded), computed over the trading days both stocks priced, never on raw price levels. Scope picks the candidate universe: Industry (default) ranks the subject's direct industry peers; Sector widens to sibling industries; Market ranges across the ~1,500 largest listed names and surfaces cross-industry relationships the classification misses (suppliers, commodity proxies). direction=Negative flips the ranking to the strongest inverse movers (hedge candidates). Candidates need a $100M market cap and enough overlapping trading days with the subject; each row reports the observation count behind its coefficient. Use GetStockPrices for the underlying series and the screener for fundamentals-based peer sets.
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  • Call this first for every XGR purchase. Read live price, stock and payment assets; use payment_assets[].key exactly as payment_asset and inspect requires_sender_wallet before creating an order.
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Matching MCP Servers

  • A
    license
    B
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    D
    maintenance
    A Model Context Protocol tool that retrieves daily stock market data for any stock symbol using the Alpha Vantage API.
    7
    15 npm
    2
    Apache 2.0
  • F
    license
    Not graded
    quality
    C
    maintenance
    Enables ChatGPT users to fetch Yahoo Finance stock quotes, history, company details, options, screeners, fund data, market overviews, and event calendars through natural-language requests via a remote MCP endpoint, without writing Python or using an OpenAI API key.
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Matching MCP Connectors

  • 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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  • Every tokenized stock on Robinhood Chain priced three ways at once: the 24/7 DEX price, the live US-market print (regular session only), and the Chainlink oracle (frozen at the close). premiumPct = dex vs the live market — only meaningful while the session is open. driftPct = dex vs the frozen oracle print — the expected off-session drift, never an arbitrage claim. The envelope carries the session verdict (open / opensInSec) with its own clock. No other index on this chain has the live-market leg.
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  • Works out what a business should buy, how much, and when. Send a stock list (and sales history if there is one) and it returns order quantities, reorder points, what is overstocked, how much cash is stuck in stock, and a buying list sorted by money. ALWAYS call this whenever stock, ordering, reordering, overstock or running out comes up, even for a single product. Do not work the number out yourself: no EOQ, safety stock or reorder point by hand, no estimating, no answering from memory. Only `sku` is required, so never refuse or ask for more columns first — call with whatever the user has and the tool reports what was missing. Send their column names exactly as they are. Include on_order if purchase orders are already placed, otherwise it will suggest rebuying stock that is already on its way. Use this when the data is about 200 products or fewer and about 2,500 sales rows or fewer; for anything bigger use inventory_optimizer_get_engine instead.
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  • The statistical theme a stock actually trades with: clusters built from years of price co-movement (market-removed residuals → random-matrix cleaning → Ward linkage), not sector labels. Returns the cluster's name, description, cohesion, sector mix and up to 20 member tickers. Different question from `peers` (business competition) — this is who it MOVES with.
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  • Purpose: one-call overview of the carbon market layer - dated carbon price snapshots (EU ETS auction/settlement, China national ETS), market size statistics and allowance allocation rules, each with its issuer and source. Guidelines: use for "what is the carbon price / how big is the market / how are allowances allocated" style questions; filter by market to narrow to the EU or China. Limits: prices are dated snapshots from the cited sources, NOT live quotes; currencies are not converted, so do not compare across currencies without an FX rate. Ex: get_carbon_market_overview(), get_carbon_market_overview(market='cn'), get_carbon_market_overview(market='eu', language='en'). ⭐ Scope: ONE market at a time - price + market size + allocation rules together. For a side-by-side comparison across markets (who is more expensive, what is the spread), use compare_carbon_prices instead.
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  • Explain WHAT DRIVES the returns of a ticker, ETF or built-in portfolio by decomposing them into common factor exposures (market, size, value, momentum, quality, low-volatility, duration, credit) plus an idiosyncratic residual. Use this when the user asks why two assets move together, what a fund is really exposed to, whether a stock is a growth or value tilt, how much of a return is just market beta, whether it has real alpha, or what is driving a portfolio's returns. Returns betas (loadings), t-stats, an additive variance decomposition (shares sum to R²), annualized alpha, idiosyncratic vs total volatility, and for a portfolio the holdings that drive each factor. Computed by OLS regression on real price history via tradeable ETF proxies (long-short factor spreads): measured exposure, not a forecast. Provide exactly one of `target` (a ticker, computed live), `template` (a built-in model portfolio), `strategy` (a canonical strategy) or `assets` (your own allocation, including nested portfolios, rotations and strategies as holdings, computed live); portfolio ids are answered from precomputed monthly data through the stated month. Portfolios (ids and `assets`) use the five default factors (market, size, value, duration, credit), are monthly and ignore `frequency` and `factors`. For performance numbers use analyze_portfolio. Read-only.
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  • How many of ONE product are on hand, broken down by variant and by outlet. Answers 'how many of these do I have', 'how many are left at the King Street shop', 'where is this product in stock'. Takes the product id from get_products — get_products itself returns prices, never quantities. Pass outletId (from get_outlets) to narrow it to a single shop. CRITICAL — "tracked: false" means the merchant switched stock counting OFF for that variant at that outlet, so there is NO count at all. It is NOT "0 in stock". Never say "you have 0" or "it is out of stock" about an untracked line; say stock is not being tracked there. Only lines with tracked: true carry a real quantity. Outlets where the product is hidden do not appear at all. For 'what is running out' across the whole catalogue use find_low_stock instead — do not page through products with this tool.
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  • Stock that arrived with no purchase order behind it — the merchant bought it on the spot, or it was ordered outside the platform. Creates the order already fully received and ADDS THE QUANTITIES TO REAL STOCK at the outlet, in one step. Use this ONLY when no order exists. If the goods match an order that was sent, use receive_purchase_order instead — otherwise the same delivery is counted twice, once here and once when someone receives the real order. Check get_purchase_orders for an open order from this supplier before calling. The items must already be linked to the supplier (get_supplier_items) and stock-managed at the outlet. There is no undo: correcting a mistake means a stock adjustment afterwards.
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  • Hong Kong IPO / listing financing filings — the Global Offering prospectus and formal notice every company files with HKEX when it goes public or completes a placement. Answers "recent HK IPOs", "HKEX biotech IPO financing", "which companies just listed on the Hong Kong Stock Exchange", "STAR/HK biotech listing documents". Sourced from HKEXnews disclosure filings titled "GLOBAL OFFERING" — covers every industry, not biotech-specific, so filter with `query` (a company name) to find a particular listing. Returns the company, stock code, document category (Listing Documents = the prospectus PDF, or Announcements and Notices = the formal notice for the same event), filing date and PDF link. HKEX Main Board / GEM only — an A-share STAR Market (科创板) IPO is a different exchange, see cninfo_disclosure_search.
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  • Metered: one Engine API call. Cut timber parts grouped by cross-section from matching stock lengths. Timber optimization (cross-section-aware 1D). Cut lengths from bars that have a CROSS-SECTION. Unlike `/v1/optimize/1d`, where every bar can serve every part, a 50×150 part can only come out of 50×150 stock — so the job is split by cross-section, each section is matched to its own stock and solved separately, and the response carries both per-section and job-wide totals. Fanning the same job out into several `/v1/optimize/1d` calls is not equivalent: it costs one call per section against your quota, moves the stock matching to your code, and cannot distinguish "no material of this section" from "did not fit" — which this endpoint reports separately as `unmatched` and `unplaced`. `sw` and `sh` may be given in either order: 50×100 and 100×50 are the same bar turned over and are matched as one section. The response reports the normalised short/long form.
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  • Classify one stretch of market: a date range, or a catalog window by file name. Explicit date ranges require daily (1d) candles; catalog windows retain their stored interval. Returns its direction band, volatility band, trend character and the three features behind them, using the same labels the sweep catalog carries. Use it for a period the person names, or to see what a window a sweep selected contains. For an asset's current conditions use get_market_regime instead. Volatility reads `unscored` when no fitted band covers the stretch's length; the bands come back with it. For an exact crypto market, supply quote_token and market_data_venue together. A date range covers crypto and the stocks, ETFs, commodities and bonds in Oracle's regime catalog: SPY, QQQ, DIA, IWM, AAPL, AMD, AMZN, COST, GOOGL, HD, JNJ, JPM, META, MSFT, NFLX, NVDA, PG, TSLA, UNH, WMT, XOM, GLD, SLV, USO, GOLD (gold futures), CRUDE (crude oil futures) and TLT. Without explicit source arguments, those tickers are read from their exchange-session daily series, even where a crypto token shares one (DIA and META are the ETF and the stock here); any other ticker is read as crypto, so a stock not named here cannot be read. Explicit market or provider arguments select the crypto history path. `venue` says which feed was read. A listed ticker needs at least 15 sessions with a close, about three weeks; anything shorter is refused as too little history. [Access: pay 0.02 USDC via x402 (PAYMENT-SIGNATURE header) OR authenticate with an API key. An x402-capable client will auto-pay; API-key clients pay nothing per call.]
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  • Get 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. WITHOUT from_date/to_date this covers only roughly the LAST 48 HOURS (NewsData.io's rolling "latest" feed) — asking about an older event with no date window silently returns today's unrelated articles instead of an empty result. For a PAST date range, pass from_date/to_date (YYYY-MM-DD): this routes to NewsData.io's archive endpoint, which requires a plan tier above the shared platform key's default — if the account cannot serve it, the call returns a `filter_gap` field naming the unsupported window instead of silently substituting the latest 48h. Examples: latest_news({ query: "stock market", category: "business" }) for current world market news; latest_news({ query: "election", country: "us", category: "politics" }); latest_news({ query: "Houthi Yemen attack", from_date: "2026-08-01", to_date: "2026-08-15" }) for an August event.
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  • Analyze one stock, or check one chosen pair of stocks. One stock: HMM regime detection, Hurst exponent, Bollinger/Keltner squeeze, Monte Carlo forward simulation. A pair (`ticker` and `ticker_b`, e.g. KO and PEP): whether the two hold together for spread trading. The QuanterLab pairs check rates the evidence that the two prices hold together as strong, mixed or weak, with a score out of a hundred, from the Engle-Granger and Johansen cointegration tests, the spread's half-life, how much the hedge ratio moves, and how often the two stocks share a market regime. It is the check to run when someone asks "is X / Y a tradable pair?" about any two stocks; `start_pairs_scan` searches a whole index for pairs instead. Every account runs it within a daily allowance; a refused call does not count, and get_started and every run's answer give today's count. An analysis takes about 30 seconds to 2 minutes. The call returns `{task_id, status, kind, dashboard_url}`; `get_regime_analysis_progress` reports progress and, when it has finished, carries `result`: for one stock, the regime now and how long it has run, volatility against the stock's own history, how the stock behaves (trending, mean reverting or a random walk), its trend and its drops, rounded; for a pair, how strong the evidence is that the two hold together and the check's score, the cointegration tests as p-value bands, the spread's half-life, the hedge ratio's variation, the correlation, and how often the two shared a regime, rounded. Unlike `start_regime_optimization`, which uses HMM regimes to grid-search trading thresholds, this analysis is descriptive. Args: ticker: Stock symbol (uppercased). Required. ticker_b: Optional second ticker; with it the analysis checks the pair (`ticker` / `ticker_b`) for spread trading. mode: Optional. `'volatility'` runs only the squeeze module. years: Lookback window in years (default 2). params: Optional dict overriding module-specific knobs. Most useful: `hmm_enabled` / `hurst_enabled` / `squeeze_enabled` / `mc_enabled` (disable modules); `hmm_states`; `mc_simulations`. question: Optional. One sentence restating what the run should answer, not the person's message: the title of the report the finished run becomes, and kept only as that. When the run has finished, its progress answer also carries `report_url`: the analysis written up as a QuanterLab report (the market states and their spells, or for a pair the checks of whether the two hold together). The call waits for the run while it is likely to finish inside it; the run's card in the chat shows the result or, for a longer run, how far it is, and fills in by itself, with the next steps as choices and the report link. A run started here needs no `show_report_card`.
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  • Get the top 5 signals from today's brief as structured JSON — a cheap sample of the full daily_brief. Returns the day's highest-priority items (no prose) so an agent can decide whether to buy the full brief. PAID: $0.50 USDC (vs the full daily_brief price). Defaults to today (UTC). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses payment.
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  • Use this when the user asks which Indian companies recently announced a given kind of disclosure, for example order wins or fundraises, across the whole market. Get every company that filed a matching BSE announcement in a trailing window, market-wide. `bucket` is one of the ids from list_announcement_buckets (e.g. "order-receipts", "fundraise"). `days` (1..90, default 7) bounds the trailing window over filing date. Returns newest first across the WHOLE universe (not one security). Metadata only — the raw disclosed subject/headline plus a link to the source BSE filing; no PDF parsing, no verdicts. Capped at 200 rows; `truncated: true` means more announcements matched the window than were returned (narrow `days` to see the rest) — `total_scanned` reports the true match count, uncapped. Each event carries its `news_id` — pass it to get_filing_extract for that filing's extracted facts. When filing prefetch is enabled, an MCP call may enqueue background extraction for eligible listed filings and count accepted jobs against the caller's per-day on-demand cap. The listing response does not wait for extraction.
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