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Stocklake — AI Stock Intelligence

Get Stock

get_stock
Read-onlyIdempotent

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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesStock ticker symbol. Also accepts a company name as a fallback (e.g. "Apple") when it uniquely resolves to one symbol.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark this as read-only/idempotent, and the description adds substantial behavioral detail: tier-gated omission behavior, precomputed vs. live data, null-vs-omitted conventions, pipeline differences between get_signals and this tool, and conditional availability of relative_strength and market_risk. This goes far beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-organized with a clear lead sentence, grouped field lists, and tier-based sections. It is front-loaded with purpose and scope. A few illustrative asides and repeated pipeline-explanation details go slightly beyond what is strictly necessary, so it is not maximally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, one parameter, rich annotations, and available output schema, the description is exceptionally complete. It covers tiering, field semantics, null handling, alternative tool routing, and distinctions between computed values, leaving little for an agent to guess.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter, symbol, is fully documented in the input schema including the company-name fallback. Schema coverage is 100%, so the description adds no additional parameter-level meaning. Baseline 3 is appropriate because the schema carries the burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence names a specific resource ('a stock') and a precise scope: price, fundamentals, technical indicators, and company profile. It explicitly contrasts itself with get_stock_research and get_signals, so an agent can distinguish this snapshot tool from its siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly states this is the single-call source for understanding a stock, and explicitly directs the agent to get_stock_research for the full narrative bundle. It also documents tier-based availability, giving conditions under which the same tool returns different or additional content.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct targets—single stock, batch, news, history, insider activity, screener, market pulse, and so on. The closest pairs are get_earnings_calendar vs get_earnings_intelligence and get_market_assessment vs get_market_pulse, which overlap in raw inputs but differ in AI interpretation; the descriptions provide enough clarification to avoid serious misselection.

Naming Consistency5/5

All 17 tools follow a consistent get_<resource> snake_case pattern, with hierarchical names like get_stock, get_stocks, get_stock_news, get_stock_history, and get_stock_research making related resources obvious. There are no mixed naming conventions or vague verb choices.

Tool Count4/5

At 17 tools, the set is slightly above the ideal range, but the scope is broad and each tool has a plausible role in an AI stock intelligence workflow. The convenience aggregations like get_stocks and get_stock_research add some redundancy but remain purposeful rather than bloated.

Completeness5/5

The surface is comprehensive for a read-only AI stock intelligence domain: single-stock fundamentals and technicals, batch lookups, price history, per-stock and market-wide news, insider activity, screening, market pulse, macro assessment, sector intelligence, earnings, signals, and watchlist enrichment. No major operational dead ends are apparent.

Resources