Is Stocklake — AI Stock Intelligence MCP server safe?

Findings (2)

• lowUnusually long tool description (over 2,000 characters)poison.long-description

  tool get_stock: …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.…

• lowNo source repository listedmaint.no-repo