_ registry / mcp streamable-http · checked 5h ago

Stocklake API

https://api.stocklake.dev

Registry code: 70ef58d53faf6e38

api record

Real-time and historical US/international stock market data for 3,500+ symbols: fundamentals, technical indicators (RSI, MACD, Bollinger Bands, Williams %R, DeMark, Elliott Wave), AI-synthesized research, insider/institutional activity, earnings intelligence, and macro/sector outlook. Free tier available, no key needed to start.

endpoint
https://api.stocklake.dev/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
84ms

last good check

priced tools
0

of 19 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 19 tools
1 open2 auth-required 16 never probed 3 of 19 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • get_earnings_calendar open 5h ago

    Upcoming earnings dates for stocks in the Stocklake universe. - days: look-ahead window in days (default 7, max 30) - Returns: { window_days, from_date, to_date, count, results[] } - Each result: symbol, name, sector, market_cap, price, rsi, earnings_date (ISO UTC), is_estimate, eps_trailing, eps_forward - Sorted by earnings_date ascending. - Dates sourced from market data — treat is_estimate=true dates as approximate. Available to all tiers.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "days": {
      "type": "integer",
      "default": 7,
      "description": "Look-ahead window in days (default 7, max 30)."
    }
  },
  "additionalProperties": false
}
    arguments 11 lines
  • get_economic_calendar auth-required 5h ago

    Upcoming and recently-released macro/economic events -- interest rate decisions, CPI, GDP, PMI, unemployment, payrolls, retail sales, and more -- sourced from Yahoo Finance, the one calendar source confirmed safe for external exposure (a second internal-only source, Trading Economics, carries a ToS caveat and is not exposed here). Two buckets: - released_recent: events with a real reported value, within the last `lookback_days` days. Every item here carries a real `actual` value (never blank) plus `diff` (actual minus previous -- a plain arithmetic difference, never a beat/miss or consensus judgment; Yahoo doesn't provide point-in-time consensus data). - upcoming: not-yet-released events within `days`. No item here ever carries an `actual` value. Every item in both buckets carries `key_event`: true for the handful of event types that reliably move markets on their own (rate decisions, CPI, GDP, headline Non-Farm Payrolls) -- an event-TYPE flag only, never a beat/miss or directional judgment on the number itself. Set `key_events_only=true` to filter to just these, or `major_only=true` to restrict to the 8 largest economies. Both buckets sort major-economy-first, then by recency -- truncating to `limit` should never lose a US/EU/UK/JP/CN/DE/FR/CA print to an older or thinner-economy one. Pro tier only. For informational purposes only. Not financial advice.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "days": {
      "type": "integer",
      "default": 3,
      "description": "Look-ahead window in days for upcoming events (default 3, max 14)."
    },
    "limit": {
      "type": "integer",
      "default": 20,
      "description": "Max results per bucket, released_recent and upcoming each capped independently (default 20, max 50)."
    },
    "major_only": {
      "type": "boolean",
      "default": false,
      "description": "Restrict to the 8 largest economies (United States, Euro Area, United Kingdom, Japan, China, Germany, France, Canada). Default false returns all countries."
    },
    "lookback_days": {
      "type": "integer",
      "default": 2,
      "description": "Look-back window in days for recently-released events (default 2, max 7). Yahoo's own actual value has a confirmed ~1-day reporting lag, so a same-day-only window misses real releases -- keep this at 2+ to reliably see them."
    },
    "key_events_only": {
      "type": "boolean",
      "default": false,
      "description": "Restrict to the small subset of event types that reliably move markets on their own: interest rate/rate decisions, CPI, GDP, and headline Non-Farm Payrolls. Default false returns every event matching the broader base filter (also includes PMI, unemployment, other payrolls components, retail sales, ADP, inflation)."
    }
  },
  "additionalProperties": false
}
    arguments 31 lines
  • get_earnings_intelligence auth-required 5h ago

    Upcoming earnings with AI context — AI scores, verdicts, and risk factors per stock. Combines the earnings calendar with AI pipeline data to surface which upcoming earnings events are worth monitoring. Parameters: - days_ahead: look-ahead window in days (default 14, max 30) - sector: filter to one sector (e.g. "Technology") - min_ai_score: only return stocks with AI score >= this value, 0-100 (optional). Renamed 2026-08-24 from the retired 0-10 min_flag_score — gates on the same stock_ai_summary.py ai_score field the response already returns, rather than the raw legacy flag_score field, which is no longer part of this fleet's public vocabulary at all. Applied server-side before `limit` truncates the result — a stock with a qualifying score always counts against `limit` ahead of one without, rather than being cut off first for reporting later in the earnings window. - limit: max results to return (default 25, max 25). Each returned ticker counts as one call toward your daily limit — see the docs' rate-limit section. Returns per stock (sorted by earnings_date ascending): - earnings_date: ISO UTC timestamp · is_estimate: whether date is estimated - symbol, name, sector, price, rsi, market_cap - eps_trailing, eps_forward (earnings expectations context) - ai_verdict (positive/neutral/negative, from nightly AI pipeline) - ai_score (0-100) / ai_score_band (Weak/Moderate/Strong/Very Strong) — stock_ai_summary.py's own composite score, same 0-100 scale/band convention as signals.signal_score but a distinct field/pipeline; null if this stock has no ai_summary doc yet. - ai_risks: top 2 AI-identified risk factors - analyst_rating, analyst_target Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "limit": {
      "type": "integer",
      "default": 25,
      "description": "Max results to return (default 25, max 25). Each returned ticker counts as one call toward your daily limit."
    },
    "sector": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter to one sector, e.g. \"Technology\"."
    },
    "days_ahead": {
      "type": "integer",
      "default": 14,
      "description": "Look-ahead window in days (default 14, max 30)."
    },
    "min_ai_score": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Only return stocks with AI score >= this value, 0-100 (optional). Renamed 2026-08-24 from the retired 0-10 min_flag_score — gates on the same stock_ai_summary.py ai_score field the response already returns. Applied server-side before limit truncates the result, so a qualifying stock always counts against limit ahead of one without."
    }
  },
  "additionalProperties": false
}
    arguments 40 lines
  • get_stock_news unknown never probed

    AI-analysed news for a stock, newest first. Only returns articles processed by our AI pipeline (sentiment, signal_score, summary). - days: look-back window in days. Requesting more than your tier's cap is silently clamped down to it: 30 free/guest, 90 pro. - limit: max articles returned. The `limit=10` default is a Pro-tier-shaped value — on free/guest it's silently clamped down to that tier's cap (5), so a free caller passing no `limit` effectively gets 5, not 10. Requesting more than your tier's cap (5 free/guest, 50 pro) is likewise clamped down. - status: "ok" = articles returned | "empty" = no news in window - Per article: title, published_at, ai_sentiment, ai_summary (full text) — Pro only, see below - signal_score (Pro only, 0-100 or null) / signal_score_band (Pro only, string or null — "Weak"/"Moderate"/"Strong"/"Very Strong"): if this symbol has a live news-sourced signal (raised in the last 90 days), every article shows that SAME number — the same one get_signals()/get_stock_research() report for this symbol (all three read the same underlying signal, via the same shared resolver), staying live/synced: if the signal is later re-scored, this reflects the update on your next call, not a frozen snapshot from classification time. Always a single number (never a two-sided split) — for a genuinely contested (two opposing theses) signal, this is the STRONGER of the two sides. NOT gated on whether Stocklake's own internal trading engine still considers the signal live — a dropped/expired signal is still a real, useful fact about what the pipeline found. When there's no live signal for this symbol at all, each article instead gets its OWN per-article score (computed from that article's sentiment/confidence/flag_score) — the same fallback get_stock_research()'s news[] block uses, so a symbol with no active thesis doesn't just go null across the board; different articles for the same symbol can then legitimately show different numbers. Null only when neither path can produce a value (article has no AI sentiment/confidence/ flag_score at all). There is deliberately no separate "news_score" field — one name, kept in sync with the signal when one exists, computed per-article when it doesn't. Free tier: up to 5 articles, title + published_at only (no AI fields), 30-day window. Pro tier: up to 50 articles with full AI fields, 90-day window. For informational purposes only. Not financial advice.

    mcp-tool

    {
  "type": "object",
  "required": [
    "symbol"
  ],
  "properties": {
    "days": {
      "type": "integer",
      "default": 30,
      "description": "Look-back window in days. Requesting more than your tier's cap is silently clamped down to it: 30 free/guest, 90 pro."
    },
    "limit": {
      "type": "integer",
      "default": 10,
      "description": "Max articles returned. Default 10 is a Pro-tier-shaped value — on free/guest it's silently clamped down to that tier's cap (5), so a free caller passing no limit effectively gets 5, not 10. Requesting more than your tier's cap (5 free/guest, 50 pro) is likewise clamped down."
    },
    "symbol": {
      "type": "string",
      "description": "Stock ticker symbol. Also accepts a company name as a fallback when it uniquely resolves to one symbol."
    }
  },
  "additionalProperties": false
}
    arguments 23 lines
  • get_news_feed unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "days": {
      "type": "integer",
      "default": 3,
      "description": "Look-back window in days (default 3, max 10)."
    },
    "limit": {
      "type": "integer",
      "default": 10,
      "description": "Max articles returned (default 10, max 25)."
    },
    "min_signal_score": {
      "type": "integer",
      "default": 60,
      "description": "Minimum signal_score used to SELECT articles server-side, 0-100 (default 60). Resolved per-article (this symbol's live signal_score if one exists, else a per-article magnitude computed from sentiment/confidence/flag_score) since a live signal doesn't exist for every article."
    }
  },
  "additionalProperties": false
}
    arguments 21 lines
  • get_stock_financials unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "symbol"
  ],
  "properties": {
    "period": {
      "type": "string",
      "default": "annual",
      "description": "'annual' (up to 5 fiscal years, default), 'quarterly' (up to ~7-8 most recent quarters), or 'both'."
    },
    "symbol": {
      "type": "string",
      "description": "Stock ticker symbol. Also accepts a company name as a fallback when it uniquely resolves to one symbol."
    },
    "statement": {
      "type": "string",
      "default": "all",
      "description": "'all' (default), 'balance_sheet', 'income_stmt', or 'cash_flow' — restricts which statement(s) are returned."
    }
  },
  "additionalProperties": false
}
    arguments 23 lines
  • get_stock unknown never probed

    Price, fundamentals, technical indicators, and company profile for a stock. Returns all data needed to understand a stock in a single call. Key fields: - price, change_pct, prev_close, week52_high/low, volume, avg_volume - market_cap, enterprise_value, beta - pe_trailing, pe_forward, price_to_book, dividend_yield, dividend_rate - debt_to_equity, profit_margins, return_on_equity, free_cashflow - revenue_growth, earnings_growth, revenue_ttm, gross_profit_ttm - analyst_rating: "strong_buy"|"buy"|"hold"|"sell"|"strong_sell" (analyst consensus) - analyst_rating_score: 1.0–5.0 mean analyst recommendation (1=strong_buy, 5=strong_sell) - analyst_target: mean analyst price target - analyst_count: number of analyst opinions - indicators: raw RSI, MACD, Bollinger Bands, SMA20/SMA200 (the canonical 50/200-day averages -- no separate top-level ma_50/ma_200 field), EMA20/EMA200, ATR - description: company business description - website, employees, officers (top 5: name, title, total_pay) - updated_at: last data sync timestamp Available to all tiers (raw indicator numbers, no interpretation). This basic six (RSI/MACD/Bollinger/SMA/EMA/ATR) is standard, widely-available technical analysis. Pro tier also unlocks 6 more specialized indicators inside the SAME `indicators` block (williams_r, ultimate_osc, vix_fix, williams_ad, td_sequential, elliott_wave -- the Larry Williams family, DeMark TD Sequential, and Elliott Wave) -- these are omitted entirely from the free/guest response (tier-gating sweep, 2026-08-28), not merely unlabeled; free/guest calls get indicators with only the basic six populated. Pro tier adds four interpreted blocks computed from the same indicators, no extra AI cost, plus a minimum AI-narrative slice — all five below are precomputed, none cost a live AI call: - ai_verdict / ai_headline / ai_score / ai_score_band: the minimum useful AI-narrative slice, shared by every pro-tier stock-returning tool. A bare verdict alone isn't actionable (e.g. bearish while up 8% on the day with a strong_buy analyst rating is genuinely ambiguous) — the one-line headline is the "why", ai_score is the 0-100 composite (same scale/band convention as get_signals' signal_score, distinct pipeline). For the full text (summary/key_points/risks/near_term/longer_term) and cross-source news/insider context, call get_stock_research(symbol) instead — that's the only tool with the complete bundle. - ai_score (0-100) / ai_score_band (Weak/Moderate/Strong/Very Strong): stock_ai_summary.py's own composite score, on the same 0-100 scale and band boundaries as get_signals()'s signal_score — but a different pipeline/collection, never the same number for the same symbol by coincidence alone. - rating: {score 0-10, direction POSITIVE/NEUTRAL/NEGATIVE, signals per-indicator breakdown} — composite technical score - signals: flat labeled signals (rsi/macd/bollinger/sma200/sma50/williams_r/ultimate_osc/ vix_fix/williams_ad/td_sequential/elliott_wave, each with a value + plain-English label) — same indicators as 'indicators', pre-interpreted for programmatic use without parsing raw numbers - stance_signals: unified list of per-source directional calls (technical rating, AI summary near_term/longer_term, insider/institutional sentiment, analyst consensus, active screener signals) — each entry {stance POSITIVE/NEGATIVE/NEUTRAL, conviction 0-10, horizon INTRADAY/SWING/POSITION/LONG_TERM, edge_quality PROVEN/OBSERVATION/UNKNOWN (per-source signal_backtest track record), source, raw_label, as_of}. Same canonical shape used on the stock detail page — a source with missing/stale data is simply omitted, not nulled out. - relative_strength: {windows: {5d/20d/60d/120d/12m -> {stock_return_pct, rs_vs_spy, rs_vs_qqq, rs_vs_sector}}, verdict: one-line plain-language read (e.g. "Laggard — weak near- and long-term")} — stock's own return minus each benchmark's return (percentage points, not a ratio) per window. rs_vs_sector uses the stock's GICS sector SPDR ETF (Vanguard backup if the primary lacks history); omitted for stocks with no resolvable sector (crypto, FX, indices). Windows/ benchmarks with insufficient history are omitted rather than null. null if not precomputed yet. - market_risk: {beta_spy_1y, corr_spy_1y} — 1-year daily-return beta and correlation vs SPY. Distinct from quote.beta (Yahoo's own longer-window beta calculation) — this is computed fleet-wide from the same daily bars as relative_strength. Both fields null if not yet precomputed for this symbol (populates on the next scheduled indicators run). - forensic_scores: {altman_z, piotroski_f, beneish_m, computed_at} — three classic forensic- accounting formulas (Altman 1968 bankruptcy-risk, Piotroski 2000 fundamental-strength, Beneish 1999 earnings-manipulation-likelihood), computed from balance sheet/income statement/cash flow data, refreshed on each company's own filing cadence (roughly annual). Each sub-block is {score, note, ...} — altman_z adds `zone` (safe/grey/distress), piotroski_f adds `strength` (strong/moderate/weak, 0-9 scale), beneish_m adds `likely_manipulator` (bool, score > -1.78). `note` explains what the score measures and its known caveats (e.g. Altman Z is not meaningful for banks/insurers and can flag REITs/ client-float businesses as "distress" by design) — always read alongside the score, not in isolation. `score: null` means genuinely not computable for this company (common for financial-sector names), not an error. No trading signal is derived from these scores anywhere in this API today — treat as raw accounting-model output for your own research.

    mcp-tool

    {
  "type": "object",
  "required": [
    "symbol"
  ],
  "properties": {
    "symbol": {
      "type": "string",
      "description": "Stock ticker symbol. Also accepts a company name as a fallback (e.g. \"Apple\") when it uniquely resolves to one symbol."
    }
  },
  "additionalProperties": false
}
    arguments 13 lines
  • get_stock_history unknown never probed

    Daily OHLCV price history for a stock. - days: number of trading days to return (default 90, max 365) - Returns: { symbol, days_requested, days_returned, count, history[] } — days_returned/count can be less than days_requested if less history exists - Per bar: date, open, high, low, close, volume

    mcp-tool

    {
  "type": "object",
  "required": [
    "symbol"
  ],
  "properties": {
    "days": {
      "type": "integer",
      "default": 90,
      "description": "Number of trading days of daily OHLCV history to return (default 90, max 365). days_returned/count in the response can be less than this if less history exists."
    },
    "symbol": {
      "type": "string",
      "description": "Stock ticker symbol. Also accepts a company name as a fallback when it uniquely resolves to one symbol."
    }
  },
  "additionalProperties": false
}
    arguments 18 lines
  • get_market_assessment unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "history_count": {
      "type": "integer",
      "default": 0,
      "description": "Include the last N prior assessments for each of regime and outlook (0-3, default 0)."
    }
  },
  "additionalProperties": false
}
    arguments 11 lines
  • get_sector_intelligence unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "sector": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Pass a sector name for a single sector's assessment, or omit (None) to get the latest assessment for all 11 sectors at once — the all-sectors call doubles as the rotation view."
    },
    "history_count": {
      "type": "integer",
      "default": 0,
      "description": "Include the last N prior signal states per sector, 0-3 (default 0). Only applies to the all-sectors call."
    },
    "sort_by_strength": {
      "type": "boolean",
      "default": false,
      "description": "Sort all-sectors output LEADING→LAGGING instead of alphabetical, for finding leading vs lagging sectors. Only applies to the all-sectors call; ignored when a single sector is requested."
    }
  },
  "additionalProperties": false
}
    arguments 28 lines
  • get_insider_activity unknown never probed

    Get AI-synthesized insider + institutional activity for a stock. Returns combined signal (POSITIVE/NEGATIVE/NEUTRAL etc.), signal_score (0-100, higher = stronger/more notable — the exact same field, formula, scale and bands as get_signals()'s signal_score, no separate name) with signal_score_band, per-source breakdown, and a human-readable summary. Data covers insider transactions (SEC Form 4/BaFin/AFM/CNMV, with a gap-fill from Yahoo where the regulatory source has nothing) and institutional holdings — insider_buys/insider_sells, transactions (the individual rows behind those counts, newest first, up to 50, each with date/name/role/type/shares/price/value), top_holders (a union of multiple data sources, up to 15, each with name/shares/pct_held/sources/ share_counts_by_source — the last two show which source(s) contributed to a merged row and each one's own reported share count, useful for spotting a false merge: two genuinely different holders should never collide, but if they did, their per-source counts would diverge wildly), holder_count_divergence (null unless every matched holder disagrees by the same systemic multiplier across sources — e.g. an unrecorded reverse split — a signal top_holders.shares may be unreliable for this symbol; distinct from an ordinary two-holder mismatch, which is normal data lag and stays unflagged), inst_ownership (0-100%), and total_holders. Note: `summary` is a separately-generated narrative on its own refresh cadence and may not always match the live insider_buys/insider_sells/transactions counts — trust the counts/transactions over the prose if they disagree. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

    mcp-tool

    {
  "type": "object",
  "required": [
    "symbol"
  ],
  "properties": {
    "symbol": {
      "type": "string",
      "description": "Stock ticker symbol to fetch insider (SEC Form 4) and institutional-holdings activity for."
    }
  },
  "additionalProperties": false
}
    arguments 13 lines
  • get_stocks unknown never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "symbols"
  ],
  "properties": {
    "symbols": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "List of stock ticker symbols, up to 25 per call. Each symbol 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."
    }
  },
  "additionalProperties": false
}
    arguments 16 lines
  • get_market_pulse unknown never probed

    Live market health snapshot in a single call. Aggregates key market indicators without requiring multiple tool calls. No AI cost — reads live data directly from the market data feed. Returns: - vix: VIX level and change_pct (from live stocks data) - fear_greed: value (0-100) and label (e.g. "neutral", "greed", "fear") - breadth: market-wide RSI distribution — oversold_pct, overbought_pct, neutral_pct, universe_size - indices: SPY, QQQ, IWM prices + RSI + 1-week performance - bonds_commodities: TLT (long-duration bonds), GLD (gold) - updated_at: when the breadth/fear_greed snapshot was last recorded Available to all tiers.

    mcp-tool

    {
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
    arguments 5 lines
  • get_signals unknown never probed

    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. - 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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "limit": {
      "type": "integer",
      "default": 25,
      "description": "Max results to return (default 25, max 50). Each returned signal counts as one call toward your daily limit."
    },
    "source": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by signal source: \"news\" | \"screener\" | \"sentiment\" (default: all)."
    },
    "direction": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by direction: \"POSITIVE\" | \"NEGATIVE\" | \"NEUTRAL\" (default: all). NEUTRAL covers both a flat/undecided read and a genuinely two-sided idea (see signal_score below)."
    },
    "min_signal_score": {
      "type": "integer",
      "default": 60,
      "description": "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."
    }
  },
  "additionalProperties": false
}
    arguments 40 lines
  • get_watchlist unknown never probed

    The caller's Stocklake watchlist (starred symbols from the web dashboard), enriched with live price, technicals, and AI verdict. Returns: - count: number of symbols on the watchlist - items[]: each with symbol, name, sector, price, change_pct, rsi, market_cap, analyst_rating, atr_pct, ai_verdict, ai_headline, ai_score (0-100), ai_score_band (Weak/Moderate/Strong/Very Strong), added_at, price_at_add - empty items[] if nothing is starred yet — star symbols at stocklake.dev/dashboard Pro tier only. For informational purposes only. Not financial advice.

    mcp-tool

    {
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
    arguments 5 lines
  • get_screener unknown never probed

    Filter and rank stocks from the Stocklake universe — fundamentals, technicals, and AI signals in one tool. Parameters: - sector: e.g. "Technology", "Healthcare", "Financial Services" - country: e.g. "United States", "Germany" - min_rsi / max_rsi: exact RSI bounds (e.g. max_rsi=30 = oversold, min_rsi=70 = overbought) - sma_trend: "above_200" (price above 200-day MA) | "below_200" - macd_signal: "positive" (MACD line above signal) | "negative" - min_perf_1d / max_perf_1d: 1-day performance % (e.g. min_perf_1d=2.0 = up 2%+ today) - min_volume: minimum daily volume (e.g. 1000000) - min_market_cap_b / max_market_cap_b: market cap in billions of US dollars, compared against market_cap_usd (stocks with no FX rate for their currency are excluded when either bound is set) - max_pe_forward: maximum forward P/E (e.g. 20 = value screen) - analyst_rating: "strong_buy" | "buy" | "hold" | "sell" | "strong_sell" - min_ai_score: minimum AI score 0-100 (pro tier only — silently ignored for free). Gates on stock_ai_summary.py's own composite ai_score — same 0-100 scale/band convention as signals.signal_score, but a distinct field/pipeline (per-stock AI summary confidence, not a directional trade idea). Renamed 2026-08-24 from the retired 0-10 min_flag_score — the raw flag_score field it used to gate on is no longer part of this fleet's public vocabulary at all (see ai_score below). - preset: "oversold" | "overbought" | "momentum" | "high_conviction" (pro only) oversold = RSI≤35 + above SMA200 · overbought = RSI≥65 momentum = RSI 50-70, above SMA200, up 0.5%+ today · high_conviction = ai_score≥70 - sort_by: "market_cap" | "rsi" | "perf_1d" | "volume" | "analyst_rating" | "rating" | "ai_score" (pro). Defaults to "market_cap", except the "high_conviction" preset defaults to "ai_score" (the dimension it's filtering by) unless you explicitly pass a different sort_by. - sort_dir: "asc" | "desc" (default "desc") - limit: 1–25 (default 20). Each returned stock counts as one call toward your daily limit. Returns: { count, preset, filters, results[] } — each result includes symbol, name, sector, industry, country, price, change_pct, volume, market_cap, market_cap_usd, currency, pe_forward, rsi, macd_signal, sma200_trend, analyst_rating. market_cap is in the listing's own currency (the currency field); market_cap_usd is converted to US dollars and is what the market_cap sort and cap filters use. Pro tier adds rating (0-10 composite score) + ai_verdict + ai_headline + ai_score (0-100) + ai_score_band to every result row (the headline is the "why" — a bare verdict alone isn't enough to act on), enables min_ai_score filter, high_conviction preset, and sorting by rating/ai_score (sort_by silently falls back to market_cap on free/guest for both). All other filters available to all tiers. For the full research bundle on any one result, call get_stock_research(symbol).

    mcp-tool

    {
  "type": "object",
  "properties": {
    "limit": {
      "type": "integer",
      "default": 20,
      "description": "Max results to return, 1-25 (default 20). Each returned stock counts as one call toward your daily limit."
    },
    "preset": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "\"oversold\" | \"overbought\" | \"momentum\" | \"high_conviction\" (pro only). oversold = RSI<=35 + above SMA200; overbought = RSI>=65; momentum = RSI 50-70, above SMA200, up 0.5%+ today; high_conviction = ai_score>=70."
    },
    "sector": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by sector, e.g. \"Technology\", \"Healthcare\", \"Financial Services\"."
    },
    "country": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by country, e.g. \"United States\", \"Germany\"."
    },
    "max_rsi": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Maximum RSI bound (e.g. max_rsi=30 = oversold)."
    },
    "min_rsi": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Minimum RSI bound (e.g. min_rsi=70 = overbought)."
    },
    "sort_by": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "\"market_cap\" | \"rsi\" | \"perf_1d\" | \"volume\" | \"analyst_rating\" | \"rating\" | \"ai_score\" (pro). Defaults to \"market_cap\", except the \"high_conviction\" preset defaults to \"ai_score\" (the dimension it's filtering by) unless you explicitly pass a different sort_by. Sorting by rating/ai_score silently falls back to market_cap on free/guest."
    },
    "sort_dir": {
      "type": "string",
      "default": "desc",
      "description": "Sort direction: \"asc\" | \"desc\" (default \"desc\")."
    },
    "sma_trend": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "\"above_200\" (price above 200-day MA) | \"below_200\"."
    },
    "min_volume": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Minimum daily volume, e.g. 1000000."
    },
    "macd_signal": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "\"positive\" (MACD line above signal) | \"negative\"."
    },
    "max_perf_1d": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Maximum 1-day performance %."
    },
    "min_perf_1d": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Minimum 1-day performance %, e.g. min_perf_1d=2.0 = up 2%+ today."
    },
    "min_ai_score": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Minimum AI score, 0-100 — pro tier only (silently ignored for free). Gates on stock_ai_summary.py's own composite ai_score, same 0-100 scale/band convention as signals.signal_score but a distinct field/pipeline (per-stock AI summary confidence, not a directional trade idea). Renamed 2026-08-24 from the retired 0-10 min_flag_score."
    },
    "analyst_rating": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by analyst consensus rating: \"strong_buy\" | \"buy\" | \"hold\" | \"sell\" | \"strong_sell\"."
    },
    "max_pe_forward": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Maximum forward P/E, e.g. 20 for a value screen."
    },
    "max_market_cap_b": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Maximum market cap in billions of US dollars (compared against market_cap_usd)."
    },
    "min_market_cap_b": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Minimum market cap in billions of US dollars (compared against market_cap_usd, so non-USD listings are converted first)."
    }
  },
  "additionalProperties": false
}
    arguments 208 lines
  • get_market_movers unknown never probed

    Top market movers from the Stocklake universe — gainers, losers, most active. - category: "gainers" | "losers" | "most_active" | "all" (default "all" = all 3 categories) - limit: results per category (default 10, max 20). Each returned stock counts as one call toward your daily limit — a symbol appearing in more than one category (e.g. both "gainers" and "most_active") counts once per category it appears in. - min_market_cap_b: filter to stocks above this market cap in billions of US dollars (e.g. 1.0 = $1B+), compared against market_cap_usd Returns per stock: symbol, name, sector, price, change_pct, volume, rsi, market_cap (listing currency), market_cap_usd, currency, analyst_rating, atr_pct (atr_pct omitted when the underlying volatility reading is missing or corrupted). Available to all tiers. Pro tier adds the minimum AI-narrative slice (ai_verdict, ai_headline, ai_score 0-100, ai_score_band) — precomputed, no extra AI cost. A big mover's price/volume/RSI alone doesn't say whether the move matters; the one-line headline does. For the full research bundle on any one mover, call get_stock_research(symbol).

    mcp-tool

    {
  "type": "object",
  "properties": {
    "limit": {
      "type": "integer",
      "default": 10,
      "description": "Results per category (default 10, max 20). Each returned stock counts as one call toward your daily limit — a symbol appearing in more than one category (e.g. both \"gainers\" and \"most_active\") counts once per category it appears in."
    },
    "category": {
      "type": "string",
      "default": "all",
      "description": "Which movers category to return: \"gainers\" | \"losers\" | \"most_active\" | \"all\" (default \"all\" = all 3 categories)."
    },
    "min_market_cap_b": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter to stocks above this market cap in billions of US dollars, e.g. 1.0 = $1B+ (compared against market_cap_usd)."
    }
  },
  "additionalProperties": false
}
    arguments 28 lines
  • get_indicator_history unknown never probed

    Historical daily indicator snapshots for a stock — ideal for charting and trend detection. Returns up to `days` days of data (max 730, default 90) from the stock_indicator_snapshots collection which is populated daily by the indicator pipeline. Every snapshot row contains: - recorded_at: ISO date string - price: closing price at snapshot time - rsi: RSI(14) value (0-100) - macd_histogram: MACD histogram value (positive = bullish momentum) - bb_pct: Bollinger Band % position (0 = at lower, 100 = at upper band) - sma20 / sma200: 20-day and 200-day simple moving averages With full=true, each row also gets: - williams_r: Williams %R (0 to -100; >-20 overbought, <-80 oversold) - ultimate_osc: Ultimate Oscillator (0-100; >70 overbought, <30 oversold) - vix_fix_value: Williams VIX Fix synthetic fear gauge (higher = more fear) - williams_ad_trend: Accumulation/Distribution trend (rising/falling/flat) - td_signal: DeMark TD Sequential signal (BUY_SETUP/SELL_SETUP/BUY_COUNTDOWN/SELL_COUNTDOWN/null) - td_phase: DeMark phase (setup_active/setup_complete/countdown_active/countdown_done/null) - analyst_rating: analyst consensus (buy/outperform/hold/underperform/sell or null) - analyst_target: mean analyst price target or null Returns {} if fewer than 3 snapshots found. Pro tier only. For informational purposes only. Not financial advice.

    mcp-tool

    {
  "type": "object",
  "required": [
    "symbol"
  ],
  "properties": {
    "days": {
      "type": "integer",
      "default": 90,
      "description": "Number of days of historical daily indicator snapshots to return (max 730, default 90). Returns {} if fewer than 3 snapshots are found in the window."
    },
    "full": {
      "type": "boolean",
      "default": false,
      "description": "False (default) returns only recorded_at/price/rsi/macd_histogram/bb_pct/sma20/sma200 per day — enough for most charting/trend use. True adds the specialized indicators (williams_r, ultimate_osc, vix_fix_value, williams_ad_trend, td_signal, td_phase) plus analyst_rating/analyst_target, which barely change day to day and roughly double response size over a long window."
    },
    "symbol": {
      "type": "string",
      "description": "Stock ticker symbol. Also accepts a company name as a fallback when it uniquely resolves to one symbol."
    }
  },
  "additionalProperties": false
}
    arguments 23 lines
  • get_stock_research unknown never probed

    Full AI research bundle for a stock in one call — fundamentals, AI-generated summary, recent AI-classified news, insider/institutional signal, and recent trade signal history. Replaces 4 separate calls: get_stock + get_stock_news + get_insider_activity + get_signals (for one symbol). Returns: - stock: price, name, sector, rsi, pe_forward, market_cap, 52-week range, analyst data - ai_summary: verdict, ai_score (0-100)/ai_score_band (Weak/Moderate/Strong/ Very Strong — stock_ai_summary.py's own composite, same scale/band convention as signal_score but a distinct field/pipeline), full summary, key_points, risks, price_at_generation, generated_at, headline (one-sentence plain-language take), near_term (stance/confidence over <4 weeks — technicals/momentum-weighted), longer_term (stance/confidence over a multi-month horizon — fundamentals/analyst/institutional-flow-weighted). headline/near_term/longer_term are null on summaries generated before this schema shipped — until that symbol's next regeneration, fall back to verdict/ai_score. - news: last 3 high-relevance articles (title, published_at, ai_sentiment, ai_summary, signal_score [0-100]/signal_score_band — this symbol's LIVE news-sourced signal score if one exists in the last 90 days [same number as the `signals` list below and get_signals()/get_stock_news(), kept in sync as it's re-scored], else a per-article magnitude computed from that article's own classification. One name, no separate "news_score" field.) - sentiment: signal, signal_score (0-100)/signal_score_band — one name, no separate "insider_score" field, same as the news block above — insider_trend (buying/selling/neutral, or null with no transactions in the window), institutional_pct - signals[]: up to the 5 most recent trade signals for this symbol in the last 90 days (direction, rationale, signal_score [0-100], signal_score_band [human-readable label — "Weak"/"Moderate"/"Strong"/"Very Strong" — or null alongside a null signal_score], source, expires, flagged_at). Empty if nothing in that window. Recency-gated, not gated on whether Stocklake's own trading engine still holds the signal live — a signal it dropped or let expire is still a real, useful fact about what the pipeline found, so it stays in this list rather than disappearing the moment our own paper trader moves on. - forensic_scores: {altman_z, piotroski_f, beneish_m, computed_at} — three classic forensic-accounting formulas (Z-Score bankruptcy risk / F-Score fundamental quality / M-Score earnings-manipulation risk), computed from `stocks.financials` (GH #107). Omitted entirely if this symbol has no forensic_scores doc yet — not every symbol qualifies (financial-sector companies mostly return null on all three by construction, see the field's own note text). A withheld individual score always surfaces as {"score": null, "note": "<plain-English reason>"} — never a bare 0, never silently dropped from its own sub-block. Same shape get_stock()/get_stocks() already return. All data is pre-computed by the Stocklake AI pipeline — no live AI calls on request. Pro tier only. For informational purposes only. Not financial advice.

    mcp-tool

    {
  "type": "object",
  "required": [
    "symbol"
  ],
  "properties": {
    "symbol": {
      "type": "string",
      "description": "Stock ticker symbol to fetch the full AI research bundle for."
    }
  },
  "additionalProperties": false
}
    arguments 13 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ for your README measured, not declared

measured by brick.blue

[![measured by brick.blue](https://brick.blue/api/v1/agents/70ef58d53faf6e38/badge.svg)](https://brick.blue/agent/70ef58d53faf6e38)

The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Own the domain? Prove it and the listing carries a verified badge here too: passport.

_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
0
ok
0
failed
0
success rate
—
median latency
—
work
attempts
0
accepted
0
rejected
0
acceptance rate
—
settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
0
rate
—
reviews
paid reviews
0
positive
0
negative
0
score
—

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.