"Resources for Analyzing Stock Market Trends" matching MCP tools:

• get_market_scheduleA

  Headless OracleA

  Returns holiday-aware trading session schedule with next open/close UTC timestamps for any of 28 exchanges. Model-agnostic: works identically regardless of which AI model consumes it. SEC/CFTC multi-oracle attestation compliant (pairs with get_market_status signed receipts). WHEN TO USE: planning trade execution windows; checking market hours, trading hours, and exchange operating hours; verifying holiday calendar and holiday closures; checking for early closes; scheduling market-dependent tasks; determining session status before capital commitment. Includes lunch break windows (session status): Tokyo Stock Exchange XJPX (11:30–12:30 JST), Hong Kong Stock Exchange XHKG (12:00–13:00 HKT), Shanghai Stock Exchange XSHG and Shenzhen Stock Exchange XSHE (11:30–13:00 CST). Covers Middle Eastern markets — Saudi Exchange/Tadawul (XSAU) and Dubai Financial Market (XDFM) use Fri–Sat weekend, Sunday is a trading day — and 24/7 crypto (Coinbase XCOI, Binance XBIN: always open). RETURNS: { mic, name, timezone (IANA), queried_at, current_status: "OPEN"|"CLOSED"|"UNKNOWN", next_open (UTC ISO8601 or null), next_close (UTC ISO8601 or null), lunch_break: {start, end} | null, settlement_window, data_coverage_years }. NOT cryptographically signed — does not reflect real-time circuit breaker halts or KV overrides. For authoritative signed status use get_market_status. Fail-closed: if this tool is unreachable, the agent MUST NOT execute the trade. LATENCY: sub-100ms p95 (pure schedule computation, no signing).

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• patternfetch_multiA

  patternfetchA

  Get a multi-timeframe market-state view for one stock, ETF, or crypto ticker in a single call: a token-compact brief for each requested timeframe (default 1h, 4h, 1d) PLUS a cross-timeframe alignment read — whether the trends across timeframes agree or diverge, with the split spelled out (e.g. "1h up / 4h up / 1d down"). WHEN: an agent wants to know if a setup is confirmed across horizons or conflicting between them, without making 3 separate brief calls. WHEN NOT: you only care about one timeframe (use brief). The alignment/divergence is impersonal DESCRIPTIVE data, not a signal to act on. Example: {"ticker":"BTC/USDT","timeframes":["1h","4h","1d"]}. Not investment advice.

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• get_cross_market_correlationA

  OneQAZ Trading IntelligenceA

  Purpose: Cross-market lead-lag relationships and decoupling events. Shows how markets influence each other (correlations) and when they diverge (decoupling, e.g. BTC up while stocks down). Triggers (casual questions too): "do crypto and stocks move together?", "코인이랑 주식이 따로 노나?", "any decoupling lately?", "시장끼리 상관관계 어때?", "is BTC tracking the Nasdaq?". When to call: when analyzing macro regime changes or divergent signals. Prerequisites: none. Next steps: get_macro_influence_map for the static causal hypotheses. Caveats: correlation data may be empty until enough regime changes accumulate. Args: source_market: Optional source market filter target_market: Optional target market filter Disclaimer: Information only, not investment advice.

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• get_historical_pricesA

  Signal8A

  Get historical OHLCV price candles for a stock. Supports daily, weekly, and monthly resolutions. Use period shorthand (1M, 3M, 6M, 1Y, 5Y, ALL) or explicit from/to UNIX timestamps. Default is 1 year of daily candles. Use this to compute price returns, chart price history, or analyze volume trends over time.

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• narrative_historyA

  whale-insightsA

  Get daily narrative/sector history — top crypto market sectors ranked by market cap change %, strength, and token count over up to 90 days — Daily historical narrative strength per market sector (e.g. DeFi, Layer 2, AI, Meme Coins) from CoinGecko Categories. One row per day per sector: market cap change %, strength score (0-100), token count in sector, daily rank, and top tokens. Filter by ?sector= for a single sector trend. Useful for identifying which narratives are accelerating or fading. DB-backed, 5-min cache. Powered by narrative_daily table (365d retention, permanent monthly archive). — Use this for daily historical data; use the corresponding live snapshot tool for current conditions and the monthly tool for long-term trends.

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• get_trendingA

  blobpediaA

  Show which CELESTIA content is trending on blobpedia this week (most-viewed CIPs, forum threads, docs, etc.). This is a Celestia content-engagement signal only. It is NOT token price or market trends (for trending coins, gainers, or TIA price movement defer to the price/market MCP such as CoinGecko) and NOT general web or crypto-wide trends (defer to a web-search MCP). Only fire when the user is asking what is popular within Celestia content. Zero arguments.

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• get_etf_exposuresA

  TipRanksA

  Returns the list of ETFs that hold a specific stock, sorted by holding market value (largest first). Args: ticker: Stock ticker (e.g. 'MSFT', 'AAPL') page: Page number (default 1) page_size: Results per page (default 20, max 100) Returns per ETF: etfTicker, etfCompanyName, etfCategory, aum, expenseRatio, holdingWeight (% of ETF in this stock), holdingMarketValue ($), holdingNumOfShares, weightInAsset (% of stock's outstanding shares held by this ETF), etfAnalystConsensusScore, etfSmartScore. Also returns totalCount (total ETFs holding the stock).

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• screen_stocksA

  finance-mcp-serverA

  Filter and screen stocks based on financial criteria like market cap range, sector, P/E ratio thresholds, dividend yield, or revenue growth. Returns matching ticker symbols with key metrics. Use for value investing, growth stock identification, or portfolio rebalancing analysis.

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• get_trade_signalA

  AlgoVault — Crypto Quant Trade CallsA

  Returns a composite verdict — BUY SELL HOLD trade call with confidence and market regime — for one crypto or tokenized-stock perpetual futures. One asset only; whole-market scan: scan_trade_calls. Read-only: live exchange APIs, no orders. Verified track record: get_track_record or performance://signal-performance; on-chain verified merkle anchor. [ALIAS] This tool is an alias of get_trade_call — same behavior, kept for backward compatibility. Prefer get_trade_call for new integrations.

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• govcon_agency_landscapeA

  Govcon IntelA

  Get contracting activity and market insights for a federal agency (e.g., 'Department of Defense', 'NASA'). Returns spending trends, recent awards, SBIR stats, and top contractors by volume.

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• GetOnBalanceVolumeA

  equiblesA

  On-Balance Volume (OBV) for a stock. Running cumulative volume that adds the bar's volume on up-closes, subtracts on down-closes, and stays flat on equal closes. Useful for confirming or diverging from price trends with volume flow. OBV is anchored at 0 on the first bar of the requested range, so absolute values shift with startDate and are not comparable across calls - read the slope and divergences, not the level.

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• stock_data_queryA

  Shibui FinanceA

  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.

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• stock_data_queryA

  Shibui FinanceA

  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.

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• screen_stocksA

  Finance MCP ServerA

  Filter and screen stocks based on financial criteria like market cap range, sector, P/E ratio thresholds, dividend yield, or revenue growth. Returns matching ticker symbols with key metrics. Use for value investing, growth stock identification, or portfolio rebalancing analysis.

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• list_exchangesA

  Headless OracleA

  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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• get_ticker_earningsA

  TipRanksA

  Upcoming and recent earnings dates for a specific stock — scheduled report dates with EPS/revenue estimates where available. Read-only, no side effects. Use this to find when a company reports. For past actual-vs-estimate results use get_earnings_history; for a cross-market schedule use get_earnings_calendar.

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• get_deal_scoreA

  OpenDealer MCP ServerA

  Get AI-powered deal scoring and market insights for a vehicle. Returns comprehensive analysis including: • Deal score (1-100) with rating (Great, Good, Fair, Poor) • Price comparison vs market average • Days on lot analysis • Price history and trends • Similar vehicles in the market Core step in vehicle_dossier, budget_coach, price_drop_sniper, and dealer_crawl playbooks. Pair with get_vehicle_history and check_recalls for buy/no-buy answers.

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• get_market_overviewA

  US Stocks & Market DataA

  [US Stock Info] Get a snapshot of the overall US market: major indices (S&P 500, NASDAQ, Dow), the VIX volatility index, US Treasury yields, commodities (gold, WTI crude), and the USD/KRW rate. Use this for questions about the market as a whole rather than a single company, e.g. "how is the US market doing", "what happened overnight", "is the market up today". Takes no arguments. Also reports whether the US regular session is currently open, so a closed-market response can be described as the last trading day's close.

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• hood_gaps_liveA

  Otto Data — Robinhood ChainA

  Live view of the current off-hours session: which tickers trade at the largest premium/discount vs their last close. Returns 'regular' when the stock market is open.

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• create_cldA

  vgraph-mcpA

  Create a CLD diagram as PNG image. A Causal Loop Diagram (CLD) is a Systems Thinking tool that maps feedback loops between variables, showing how a change in one variable causes changes in others. It reveals reinforcing dynamics (exponential growth or decline) and balancing dynamics (stabilisation toward equilibrium). You provide VGL (Vithanco Graph Language) code using the CLD notation and the tool renders it to an PNG image. ## Core Concepts A CLD consists of **variables** (called Stocks) connected by **causal links** with polarity: - **same** (`s`): when A increases, B increases; when A decreases, B decreases. Drawn as a solid arrow. - **opposite** (`o`): when A increases, B decreases; when A decreases, B increases. Drawn as a dashed arrow. A **feedback loop** is a closed chain of causal links returning to the starting variable: - **Reinforcing loop (R)**: even number of `opposite` edges (including zero). Drives exponential growth or decline — a snowball effect. - **Balancing loop (B)**: odd number of `opposite` edges. Drives the system toward equilibrium — a thermostat effect. ## How to Build a CLD 1. Identify the key variables (stocks) in the system — things whose value can increase or decrease. 2. For each pair of causally related variables, determine the polarity: does an increase in A cause B to increase (same) or decrease (opposite)? 3. Trace closed loops and classify them as reinforcing or balancing using the counting rule. 4. Give the diagram a title that frames the system boundary. ## VGL Syntax ``` vgraph <id>: CLD "<title>" { <nodes and edges> } ``` ### Node Types - `Stock` — a variable whose value changes over time (blue circle). Examples: Population, Revenue, Stress, Trust. ``` node <id>: Stock "<label>" ``` ### Edges CRITICAL: You MUST specify the edge type (`: same` or `: opposite`) on every edge. Both edge types connect Stock to Stock, so the type CANNOT be inferred — omitting it will cause an error. ``` edge <from_id> -> <to_id>: same edge <from_id> -> <to_id>: opposite ``` ## Identifying Feedback Loops To classify a loop, trace a closed path back to the starting variable and count the `opposite` edges: - **0 opposite edges** → Reinforcing (R): Population → Birth Rate → Population (more people → more births → even more people) - **1 opposite edge** → Balancing (B): Population → Death Rate → Population (more people → more deaths → fewer people) Rule: even count = reinforcing, odd count = balancing. ## Complete Example ``` vgraph populationCLD: CLD "Population Dynamics" { node population: Stock "Population" node births: Stock "Birth Rate" node deaths: Stock "Death Rate" node resources: Stock "Available Resources" edge population -> births: same edge births -> population: same edge population -> deaths: same edge deaths -> population: opposite edge population -> resources: opposite edge resources -> births: same } ``` **Loop analysis:** - **R1 (Reinforcing):** Population → Birth Rate → Population — 0 opposite edges. More people produce more births, which increases population. Growth spiral. - **B1 (Balancing):** Population → Death Rate → Population — 1 opposite edge. More people means more deaths, which reduces population. Death regulation. - **B2 (Balancing):** Population → Available Resources → Birth Rate → Population — 1 opposite edge (population → resources). More people deplete resources, reducing birth rate. Resource constraint. ## Rules 1. ALWAYS specify the edge type (`: same` or `: opposite`) — it cannot be inferred 2. Stock labels should be nouns or noun phrases representing measurable quantities that can increase or decrease (e.g. "Population", "Revenue", "Stress Level" — not "People are born" or "Increasing") 3. Every Stock MUST connect to at least one other Stock — no isolated variables 4. Think in terms of "if A increases, what happens to B?" to determine same vs opposite polarity 5. Use meaningful IDs (population, revenue, stress — not n1, n2, n3) 6. Keep the diagram focused on one system — the title should frame the boundary 7. Aim for closed loops — a CLD without any feedback loop is just a causal chain and misses the point of systems thinking 8. Prefer 3–6 variables per loop for clarity — larger loops are hard to trace and verify

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• market_research_briefA

  gapup-mcpC

  Generate a structured, sourced market research brief on any market, sector or industry. Returns a machine-readable note with six sections: an executive overview, a market-size estimate (with assumptions and sources — no invented figures), key players, demand & technology trends, risk factors, and a traceable source list. When to use this tool: an agent needs to assess a new market, validate a business opportunity, prepare a pitch, or benchmark a sector before a strategic decision. Data is assembled live from keyless public sources: Wikipedia (sector context), World Bank (macro GDP/population for market sizing), REST Countries (geo context). Fields that cannot be sourced are marked 'unavailable' rather than estimated. Inputs: topic (required), geo and sector (optional refinements).

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