"StockX" matching MCP tools:

• get_stocksA

  Stocklake — AI Stock IntelligenceA

  Batch stock data for up to 25 symbols in a single call — the same fields get_stock returns for the same key/symbol, so this is a true batch version, not a thinned-down scan. Returns a dict keyed by symbol. Missing symbols are omitted from the result. Each symbol in the batch counts as one call toward the daily limit. A request over 25 symbols is rejected outright (error: batch_too_large) rather than silently served on just the first 25 — split a larger list into multiple calls. Available to all tiers (fundamentals/indicators/company profile, free). Pro tier adds, per symbol, the same precomputed blocks get_stock adds — rating {score, direction, signals}, signals (per-indicator breakdown), relative_strength, market_risk {beta_spy_1y, corr_spy_1y}, and the minimum AI-narrative slice (ai_verdict, ai_headline, ai_score, ai_score_band). None of this costs a live AI call — it's all precomputed and just needs projecting. NOT included, even on pro — call get_stock(symbol) for stance_signals, or get_stock_research(symbol) for the full ai_summary text (summary/key_points/ risks/near_term/longer_term) plus cross-source news/insider/signal context. Response also carries `duplicates_collapsed`: how many input symbols normalized (case-folding, share-class aliasing e.g. "BRK.B"->"BRK-B") or literally repeated onto a symbol already counted elsewhere in this batch. requested - len(missing or []) - duplicates_collapsed == count always holds.

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• polymarket_arbitrageA

  China StocksA

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• resolution_auditA

  China StocksA

  Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources, at different times, with different precision — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.

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• polymarket_arbitrageA

  Hk StocksA

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• resolution_auditA

  Hk StocksA

  Extract the settlement clause of a single Polymarket or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at, the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "election outcome"), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's `description` field (fetched via polymarket_market) or Kalshi's `rules_primary` + `rules_secondary` fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass `market` as a Polymarket slug/URL or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources, at different times, with different precision — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.

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• get_stock_financialsA

  Stocklake — AI Stock IntelligenceA

  Full raw financial statements — balance sheet, income statement, and cash flow line items over multiple periods. This is the underlying statement data itself, not a derived summary — for the forensic-accounting scores computed FROM these statements (Altman Z / Piotroski F / Beneish M), see get_stock()'s forensic_scores block instead. Each returned statement is shaped {line_item_name: {period_end_iso: value}} — e.g. balance_sheet["Total Revenue"]["2025-12-31"] — so a caller gets every available period per line item and can compute its own trends/ deltas/CAGRs, not just read the latest value. period="annual" (default) returns up to 5 fiscal years — Yahoo's own real ceiling, some symbols return fewer. period="quarterly" returns up to roughly 7-8 of the most recent quarters. period="both" returns both blocks in one call. ~50-60 curated line items per statement (not a raw dump of every row Yahoo reports): balance sheet structure (assets/liabilities/equity/debt/ working capital), income statement (revenue through EPS), and cash flow (operating/investing/financing, free cash flow, buybacks, stock-based comp). Coverage genuinely varies by symbol and sector — a bank has no "Inventory" line, a company with no buyback program has no "Repurchase Of Capital Stock" entry. A missing line item means Yahoo doesn't report it for this company, not a fetch error. quarterly can come back null (with a quarterly_note) for a symbol whose real quarterly data isn't available — rare in practice; live coverage testing found real quarterly statements even for semi-annual-reporting Hong Kong names. annual/quarterly can both be entirely absent if this symbol hasn't yet been through the financials sync, or if it's not an equity (this tool has no data for ETFs/crypto/forex/indices). Pro tier only. For informational purposes only. Not financial advice.

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• kalshi_weather_edgeA

  China StocksA

  Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").

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• release_calendar_marketsA

  China StocksA

  JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are cached like every other pack (≤ 60s TTL) and there is no push/webhook — do not use this to try to beat a release, use it to see what is coming and what is already priced. CATEGORIES: econ (CPI, Employment Situation/jobs report, GDP, PCE, PPI, retail sales, housing starts, jobless claims — via fred_release_dates per known release_id, since FRED's own cross-release calendar mostly returns recent actuals, not future dates), fed (the next FOMC meeting's rate decision, via fomc_calendar), fda (PDUFA action dates + FDA advisory-committee meetings, via pdufa_catalysts / fda_adcom_calendar), sec (SEC final rules whose own DATES clause names an effective date in the window, via federal-register recent_rules — usually finds nothing in a short window since SEC rules typically take effect 30–60 days out, which is an accurate answer, not a bug), court (ALWAYS EMPTY today — court-listener has no forward-looking scheduled-hearing calendar, only filing/termination dates, so this category returns zero releases with unsupported:true rather than fabricate one). Omit `categories` or pass "all" for every category. MATCHING AND ITS HONESTY CONTRACT: every release is returned even when it has ZERO matched markets — a release is never dropped just because nothing on Polymarket or Kalshi resolves on it (most FDA/SEC releases will show markets:[]; that is signal, not a gap). Every matched market carries resolves_on_this_release: "true" (the venue's own close/end date sits within ~36h of the release AND the question passed a subject filter — econ and fed only), "likely" (same subject filter, but the venue closes days away from the release date), or "unclear" (a keyword hit with no date to anchor against — always true for the fda category, which has no ladder structure to check a date against). matched_by names the mechanism (a Kalshi series ticker, a Polymarket search query, or an FDA keyword probe) so a caller can judge the match rather than trust a label. scheduled_at carries both `utc` and `et`; econ releases use the standing BLS/Census 8:30am ET convention (FRED's calendar itself has no clock time), FOMC decisions use the 2:00pm ET convention, and FDA/SEC dates are date_only:true (no reliable clock time exists for either). DO NOT treat a matched market as a real arbitrage or a settled fact on its own — a market question sharing tokens with a release name is not proof it settles on that release's own published number. Call resolution_audit / resolution_diff (fleet #1909) on a specific market before sizing anything here. An empty window (zero releases across every requested category) returns error:"no_releases_in_window" with a widen-the-window hint rather than an empty array — econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.

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• polymarket_fill_riskA

  Hk StocksA

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L). FEES ARE NOT MODELLED HERE: vwap_fill_price/profit_usd are GROSS of Polymarket's own taker fee (rate 0.04-0.07 by category — see polymarket_edges/fees.ts), on top of which this tool prices depth-crossing cost; a thin-margin fill that looks clean here can still be net-negative after the fee.

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• kalshi_weather_edgeA

  Hk StocksA

  Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").

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• release_calendar_marketsA

  Hk StocksA

  JOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are cached like every other pack (≤ 60s TTL) and there is no push/webhook — do not use this to try to beat a release, use it to see what is coming and what is already priced. CATEGORIES: econ (CPI, Employment Situation/jobs report, GDP, PCE, PPI, retail sales, housing starts, jobless claims — via fred_release_dates per known release_id, since FRED's own cross-release calendar mostly returns recent actuals, not future dates), fed (the next FOMC meeting's rate decision, via fomc_calendar), fda (PDUFA action dates + FDA advisory-committee meetings, via pdufa_catalysts / fda_adcom_calendar), sec (SEC final rules whose own DATES clause names an effective date in the window, via federal-register recent_rules — usually finds nothing in a short window since SEC rules typically take effect 30–60 days out, which is an accurate answer, not a bug), court (ALWAYS EMPTY today — court-listener has no forward-looking scheduled-hearing calendar, only filing/termination dates, so this category returns zero releases with unsupported:true rather than fabricate one). Omit `categories` or pass "all" for every category. MATCHING AND ITS HONESTY CONTRACT: every release is returned even when it has ZERO matched markets — a release is never dropped just because nothing on Polymarket or Kalshi resolves on it (most FDA/SEC releases will show markets:[]; that is signal, not a gap). Every matched market carries resolves_on_this_release: "true" (the venue's own close/end date sits within ~36h of the release AND the question passed a subject filter — econ and fed only), "likely" (same subject filter, but the venue closes days away from the release date), or "unclear" (a keyword hit with no date to anchor against — always true for the fda category, which has no ladder structure to check a date against). matched_by names the mechanism (a Kalshi series ticker, a Polymarket search query, or an FDA keyword probe) so a caller can judge the match rather than trust a label. scheduled_at carries both `utc` and `et`; econ releases use the standing BLS/Census 8:30am ET convention (FRED's calendar itself has no clock time), FOMC decisions use the 2:00pm ET convention, and FDA/SEC dates are date_only:true (no reliable clock time exists for either). DO NOT treat a matched market as a real arbitrage or a settled fact on its own — a market question sharing tokens with a release name is not proof it settles on that release's own published number. Call resolution_audit / resolution_diff (fleet #1909) on a specific market before sizing anything here. An empty window (zero releases across every requested category) returns error:"no_releases_in_window" with a widen-the-window hint rather than an empty array — econ releases especially cluster on specific dates each month, so a 48h window often straddles a dead stretch.

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• pipeworx_feedbackA

  Hk StocksA

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

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• get_announcementsA

  CeylonCharts - Colombo Stocks Exchange (CSE) data for AI AgentsA

  Get general CSE company disclosures for a symbol — board changes, AGM/EGM notices, listings, trading suspensions/resumptions, name changes, and other regulatory announcements. For dividends, rights issues, or share splits specifically, use get_corporate_actions instead — it has the parsed, structured detail (amounts, ratios, ex-dates) that this tool's raw announcement text does not. Returns CSV (date,type,description,pdf_url), or JSON candidates if the input is ambiguous.

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• get_corporate_actionsA

  CeylonCharts - Colombo Stocks Exchange (CSE) data for AI AgentsA

  Get a calendar of corporate actions (dividends, rights issues, share splits) — announcement details, not just a price-adjustment factor. This is the tool for dividends/rights/splits specifically; for other company disclosures (board changes, AGM/EGM notices, trading suspensions, etc.), use get_announcements instead. Omit all filters to browse every corporate action in the dataset, past and future, oldest first; pass from=<today> to jump straight to upcoming ones, which is the most common need. Optionally scope to one company (matches every one of its share classes, not just one) and/or one kind. Returns CSV, one row per action. If the symbol is ambiguous, returns JSON candidates instead.

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• get_market_assessmentA

  Stocklake — AI Stock IntelligenceA

  Combined AI market assessment: macro regime + market outlook in a single call. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) — dead overnight and on weekends, not a continuous 4-hourly cadence. Check regime_stale/outlook_stale below (which already account for the weekend gap) before treating either as current, especially on a Monday morning. Two distinct perspectives returned together: - REGIME (RISK_OFF/CAUTIOUS/NEUTRAL/AGGRESSIVE): answers "how much equity risk to take" → use for position sizing and asset allocation decisions - OUTLOOK (POSITIVE/NEUTRAL/NEGATIVE): answers "which direction and sectors to trade" → use for sector preference and directional bias Both share the same pipeline run so they are always in sync. - history_count: include last N prior assessments for each (0-3, default 0) - regime_*: risk posture fields — regime, risk_appetite_score (0-100 re-expression of regime, higher = current conditions support more risk-taking), regime_bias, regime_bias_note (plain sentence on whether current conditions favor long or short setups, or neither), regime_confidence, regime_rationale, key_risks, watch_for, vix_at_assessment, regime_updated_at, regime_stale - macro_score / regime_strength: macro_score is a real, continuous 0-100 read on how much risk the current environment supports (0=RISK_OFF/capital preservation, 100=AGGRESSIVE/risk-on) — the same underlying number `regime` buckets into 4 discrete categories, blending arithmetic inputs (VIX level, breadth oversold/overbought skew, SKEW-vs-VIX divergence, TD-exhaustion ratio) with regime_strength, the AI's own 1-10 read of regime conviction. Distinct from risk_appetite_score (a coarse 4-value lookup on `regime` alone) — macro_score is the real underlying number. Null on a pre-2026-08-26 assessment that predates this field. Not a call on any one stock. - macro_score_trend: {change_7d, change_30d, direction} — whether macro_score itself is improving/deteriorating/stable over the trailing 7/30 days, computed automatically (no history_count needed). A bare 33 doesn't tell you whether the environment is getting worse or just stabilized after a worse reading; this does. Either leg is null without enough history yet. - regime_stale / outlook_stale: true if that assessment is >72h old (the pipeline runs weekdays only, so a normal Fri→Mon gap does not trip this — only a genuinely broken/stuck pipeline does). null if the timestamp itself is missing. - indicators.macro_data: FRED macro data (yield curve, Fed funds, cpi_index, unemployment, M2) - indicators.volatility_term_structure: VIX spot/3M/6M term structure + contango signal - indicators.market_sentiment: CNN Fear & Greed value and label - market_context: price/RSI/SMA200/perf snapshot of SPY/QQQ/IWM/TLT/GLD/VIX/TNX + sectors NOTE: point-in-time snapshot recorded when AI ran — not live prices (use get_market_pulse for live) - outlook_*: directional fields — outlook, outlook_conviction, equity_view, preferred_sectors, avoided_sectors, catalyst, outlook_key_risk, outlook_rationale, outlook_updated_at, outlook_stale Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• get_sector_intelligenceA

  Stocklake — AI Stock IntelligenceA

  AI-assessed sector intelligence: signal, cycle stage, rotation signal, drivers, alerts, and computed statistics per sector (RSI distribution, breadth, performance 1W/1M, top/bottom movers, historical percentiles). Pass a sector name for a single sector, or omit the parameter (or pass None) to get the latest assessment for all 11 sectors — the all-sectors call doubles as the rotation view: use sort_by_strength to rank LEADING-first for finding leading vs lagging sectors, and history_count for prior signal states per sector. - sort_by_strength: sort all-sectors output LEADING→LAGGING instead of alphabetical (all-sectors call only; ignored when a single sector is requested) - history_count: include last N prior signal states per sector, 0-3 (default 0; all-sectors call only) - sector_score / strength_score: sector_score is a real, continuous 0-100 read on this sector's relative strength/leadership (0=LAGGING, 100=LEADING) — the same underlying number `signal` buckets into 5 discrete categories, blending arithmetic inputs (RSI/perf percentiles, top-5 concentration, SMA200 breadth) with strength_score, the AI's own 1-10 read. Comparable across all 11 sectors on one absolute scale (not per-sector-relative). Null on a pre-2026-08-26 assessment that predates this field. Not a buy/sell call. - sector_score_trend: {change_7d, change_30d, direction} — whether this sector's score is improving/deteriorating/stable over the trailing 7/30 days, computed automatically. Single-sector calls only — this is the only trend view available for one sector at all (history_count only applies to the all-sectors call). Two sectors both reading STRONG/68 can be in opposite motion; this tells them apart. Either leg is null without enough history yet. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) — dead overnight and on weekends, not a continuous 4-hourly cadence. Check the returned updated_at before treating this as current, especially on a Monday morning or after a holiday. Available to pro tier only (AI pipeline costs). For informational purposes only. Not financial advice.

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• get_signalsA

  Stocklake — AI Stock IntelligenceA

  AI-screened stock signals recently surfaced by the Stocklake pipeline — sourced from news analysis, sector screening, and sentiment signals. Shows what the pipeline noticed in the last 24 hours (falling back to the most recent signals regardless of age if nothing has fired in that window — see `window` in the response). This reflects what the AI pipeline found, not whether Stocklake's own internal trading engine still holds it live — a signal it later dropped or let expire is still shown here, since that's a fact about our own trading state, not about the signal's informational value. Parameters: - direction: "POSITIVE" | "NEGATIVE" | "NEUTRAL" (default: all). NEUTRAL covers both a flat/undecided read AND a genuinely two-sided idea (real opposing bull/bear theses on the same symbol) — in the latter case signal_score is the STRONGER of the two sides (see signal_score below), so a high score alongside NEUTRAL means "real conviction here, just no directional consensus," not "nothing going on." The two-sided detail is in `rationale`. - min_signal_score: minimum composite signal score 0-100 (default 60) — a blend of conviction/confidence/flag_score, source track record, and real technical factors. This is the field to filter on. Always compared against a single number, including for NEUTRAL/two-sided ideas — a result is never returned below your threshold on both sides. - min_conviction, min_flag_score: DEPRECATED, ignored for filtering — kept in the signature only so existing callers don't hard-fail; ai.stocklake.dev's internal scoring retired the raw conviction/confidence/flag_score triad in favor of signal_score. Passing a non-default value here has no effect and is logged for a planned removal. - source: filter by signal source — "news" | "screener" | "sentiment" (default: all) - limit: max results to return (default 25, max 50). Each returned signal counts as one call toward your daily limit. Returns: - count: number of signals returned - window: "24h" (fresh activity found) or a fallback note when the last 24h had nothing and the response instead shows the most recent signals overall - signals[]: each with symbol, direction, signal_score (0-100), signal_score_band (human-readable label — "Weak"/"Moderate"/"Strong"/"Very Strong"), source, rationale, expires, flagged_at Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• get_market_assessmentA

  Stocklake — AI Stock IntelligenceA

  Combined AI market assessment: macro regime + market outlook in a single call. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) — dead overnight and on weekends, not a continuous 4-hourly cadence. Check regime_stale/outlook_stale below (which already account for the weekend gap) before treating either as current, especially on a Monday morning. Two distinct perspectives returned together: - REGIME (RISK_OFF/CAUTIOUS/NEUTRAL/AGGRESSIVE): answers "how much equity risk to take" → use for position sizing and asset allocation decisions - OUTLOOK (POSITIVE/NEUTRAL/NEGATIVE): answers "which direction and sectors to trade" → use for sector preference and directional bias Both share the same pipeline run so they are always in sync. - history_count: include last N prior assessments for each (0-3, default 0) - regime_*: risk posture fields — regime, risk_appetite_score (0-100 re-expression of regime, higher = current conditions support more risk-taking), regime_bias, regime_bias_note (plain sentence on whether current conditions favor long or short setups, or neither), regime_confidence, regime_rationale, key_risks, watch_for, vix_at_assessment, regime_updated_at, regime_stale - macro_score / regime_strength: macro_score is a real, continuous 0-100 read on how much risk the current environment supports (0=RISK_OFF/capital preservation, 100=AGGRESSIVE/risk-on) — the same underlying number `regime` buckets into 4 discrete categories, blending arithmetic inputs (VIX level, breadth oversold/overbought skew, SKEW-vs-VIX divergence, TD-exhaustion ratio) with regime_strength, the AI's own 1-10 read of regime conviction. Distinct from risk_appetite_score (a coarse 4-value lookup on `regime` alone) — macro_score is the real underlying number. Null on a pre-2026-08-26 assessment that predates this field. Not a call on any one stock. - macro_score_trend: {change_7d, change_30d, direction} — whether macro_score itself is improving/deteriorating/stable over the trailing 7/30 days, computed automatically (no history_count needed). A bare 33 doesn't tell you whether the environment is getting worse or just stabilized after a worse reading; this does. Either leg is null without enough history yet. - regime_stale / outlook_stale: true if that assessment is >72h old (the pipeline runs weekdays only, so a normal Fri→Mon gap does not trip this — only a genuinely broken/stuck pipeline does). null if the timestamp itself is missing. - indicators.macro_data: FRED macro data (yield curve, Fed funds, cpi_index, unemployment, M2) - indicators.volatility_term_structure: VIX spot/3M/6M term structure + contango signal - indicators.market_sentiment: CNN Fear & Greed value and label - market_context: price/RSI/SMA200/perf snapshot of SPY/QQQ/IWM/TLT/GLD/VIX/TNX + sectors NOTE: point-in-time snapshot recorded when AI ran — not live prices (use get_market_pulse for live) - outlook_*: directional fields — outlook, outlook_conviction, equity_view, preferred_sectors, avoided_sectors, catalyst, outlook_key_risk, outlook_rationale, outlook_updated_at, outlook_stale Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• get_sector_intelligenceA

  Stocklake — AI Stock IntelligenceA

  AI-assessed sector intelligence: signal, cycle stage, rotation signal, drivers, alerts, and computed statistics per sector (RSI distribution, breadth, performance 1W/1M, top/bottom movers, historical percentiles). Pass a sector name for a single sector, or omit the parameter (or pass None) to get the latest assessment for all 11 sectors — the all-sectors call doubles as the rotation view: use sort_by_strength to rank LEADING-first for finding leading vs lagging sectors, and history_count for prior signal states per sector. - sort_by_strength: sort all-sectors output LEADING→LAGGING instead of alphabetical (all-sectors call only; ignored when a single sector is requested) - history_count: include last N prior signal states per sector, 0-3 (default 0; all-sectors call only) - sector_score / strength_score: sector_score is a real, continuous 0-100 read on this sector's relative strength/leadership (0=LAGGING, 100=LEADING) — the same underlying number `signal` buckets into 5 discrete categories, blending arithmetic inputs (RSI/perf percentiles, top-5 concentration, SMA200 breadth) with strength_score, the AI's own 1-10 read. Comparable across all 11 sectors on one absolute scale (not per-sector-relative). Null on a pre-2026-08-26 assessment that predates this field. Not a buy/sell call. - sector_score_trend: {change_7d, change_30d, direction} — whether this sector's score is improving/deteriorating/stable over the trailing 7/30 days, computed automatically. Single-sector calls only — this is the only trend view available for one sector at all (history_count only applies to the all-sectors call). Two sectors both reading STRONG/68 can be in opposite motion; this tells them apart. Either leg is null without enough history yet. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) — dead overnight and on weekends, not a continuous 4-hourly cadence. Check the returned updated_at before treating this as current, especially on a Monday morning or after a holiday. Available to pro tier only (AI pipeline costs). For informational purposes only. Not financial advice.

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• get_signalsA

  Stocklake — AI Stock IntelligenceA

  AI-screened stock signals recently surfaced by the Stocklake pipeline — sourced from news analysis, sector screening, and sentiment signals. Shows what the pipeline noticed in the last 24 hours (falling back to the most recent signals regardless of age if nothing has fired in that window — see `window` in the response). This reflects what the AI pipeline found, not whether Stocklake's own internal trading engine still holds it live — a signal it later dropped or let expire is still shown here, since that's a fact about our own trading state, not about the signal's informational value. Parameters: - direction: "POSITIVE" | "NEGATIVE" | "NEUTRAL" (default: all). NEUTRAL covers both a flat/undecided read AND a genuinely two-sided idea (real opposing bull/bear theses on the same symbol) — in the latter case signal_score is the STRONGER of the two sides (see signal_score below), so a high score alongside NEUTRAL means "real conviction here, just no directional consensus," not "nothing going on." The two-sided detail is in `rationale`. - min_signal_score: minimum composite signal score 0-100 (default 60) — a blend of conviction/confidence/flag_score, source track record, and real technical factors. This is the field to filter on. Always compared against a single number, including for NEUTRAL/two-sided ideas — a result is never returned below your threshold on both sides. - min_conviction, min_flag_score: DEPRECATED, ignored for filtering — kept in the signature only so existing callers don't hard-fail; ai.stocklake.dev's internal scoring retired the raw conviction/confidence/flag_score triad in favor of signal_score. Passing a non-default value here has no effect and is logged for a planned removal. - source: filter by signal source — "news" | "screener" | "sentiment" (default: all) - limit: max results to return (default 25, max 50). Each returned signal counts as one call toward your daily limit. Returns: - count: number of signals returned - window: "24h" (fresh activity found) or a fallback note when the last 24h had nothing and the response instead shows the most recent signals overall - signals[]: each with symbol, direction, signal_score (0-100), signal_score_band (human-readable label — "Weak"/"Moderate"/"Strong"/"Very Strong"), source, rationale, expires, flagged_at Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• get_news_feedA

  Stocklake — AI Stock IntelligenceA

  Top AI-flagged news across all tracked stocks — the market-wide news briefing. Unlike get_stock_news (per-symbol), this scans the entire universe and returns the most notable articles ranked by signal_score, newest first within each score tier. Use this for: - Morning briefing: "what happened in the market this week?" - Catalyst scanning: "what news is driving moves right now?" - Event monitoring: "which stocks have high-impact news today?" - min_signal_score: minimum signal_score (0-100, default 60) used to SELECT articles server-side. Resolved per-article (stored/computed magnitude preferred over an unfiltered Mongo `$gte`, since a formal live signal doesn't exist for every article — see signal_score below), then filtered/sorted in Python. - days: look-back window in days (default 3, max 10) - limit: max articles returned (default 10, max 25) - Per article: symbol, title, published_at, ai_sentiment, ai_summary (full text), signal_score (0-100), signal_score_band (Weak/Moderate/Strong/ Very Strong) signal_score/signal_score_band: this symbol's LIVE signal score if a news-sourced signal was raised for it in the last 90 days (same number get_stock_news()/get_signals() report, kept in sync as that signal is re-scored — one $in query per distinct symbol in the result, not per article, so two articles about the same stock always show the same value); otherwise a per-article magnitude computed from THIS article's own sentiment/confidence/flag_score, so every article still gets a real, rankable number. Always a single number — for a symbol whose live signal is genuinely two-sided (real opposing bull/bear theses), this is the STRONGER of the two sides, same as get_signals()/get_stock_news(). There is deliberately no separate "news_score" field — one name for "how strong is this idea," whether it's backed by a formal signal or just this article's own classification. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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