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Stocklake — AI Stock Intelligence

by dev.stocklake.api

Screen Stocks

get_screener
Read-onlyIdempotent

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

  • 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, pe_forward, rsi, macd_signal, sma200_trend, analyst_rating. 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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return, 1-25 (default 20). Each returned stock counts as one call toward your daily limit.
presetNo"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.
sectorNoFilter by sector, e.g. "Technology", "Healthcare", "Financial Services".
countryNoFilter by country, e.g. "United States", "Germany".
max_rsiNoMaximum RSI bound (e.g. max_rsi=30 = oversold).
min_rsiNoMinimum RSI bound (e.g. min_rsi=70 = overbought).
sort_byNo"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_dirNoSort direction: "asc" | "desc" (default "desc").desc
sma_trendNo"above_200" (price above 200-day MA) | "below_200".
min_volumeNoMinimum daily volume, e.g. 1000000.
macd_signalNo"positive" (MACD line above signal) | "negative".
max_perf_1dNoMaximum 1-day performance %.
min_perf_1dNoMinimum 1-day performance %, e.g. min_perf_1d=2.0 = up 2%+ today.
min_ai_scoreNoMinimum 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_ratingNoFilter by analyst consensus rating: "strong_buy" | "buy" | "hold" | "sell" | "strong_sell".
max_pe_forwardNoMaximum forward P/E, e.g. 20 for a value screen.
max_market_cap_bNoMaximum market cap in billions of dollars.
min_market_cap_bNoMinimum market cap in billions of dollars.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description discloses meaningful behavioral details: each returned stock counts toward the daily limit, pro-tier parameters are 'silently ignored' or 'silently fall back' for free/guest tiers, and the high_conviction preset changes the default sort. It also traces the history of the renamed min_ai_score parameter, which is valuable context that annotations cannot provide.

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 the complexity of an 18-parameter screener with tiering and presets justifies the length. It is front-loaded with a clear one-line purpose, and parameters are grouped readably. A few passages, such as the min_ai_score provenance note, are dense but earn their place by preventing confusion with a similarly named field.

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, the description is remarkably complete: it covers all filters, preset behaviors, tiered availability, silent fallbacks, return shape, quota semantics, and the recommended follow-up tool. The presence of an output schema means return values need not be restated, and the description fills the behavioral gaps around tiers and counting.

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

Parameters5/5

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

Although the schema already documents 100% of parameters, the description adds substantial semantics: concrete examples for RSI bounds, performance percentages, volume, market cap, and P/E; preset definitions; tier-gated behavior; and the note that high_conviction defaults sort_by to ai_score. This goes well beyond the baseline set by schema descriptions.

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 description opens with a specific verb and resource: 'Filter and rank stocks from the Stocklake universe' and immediately distinguishes this as a multi-factor screening tool covering 'fundamentals, technicals, and AI signals'. This clearly separates it from siblings like get_stock (single stock), get_market_movers, or get_signals.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool — for filtering and ranking across the universe — and explicitly directs users to a specific sibling: 'For the full research bundle on any one result, call get_stock_research(symbol).' It does not comprehensively compare against all possible sibling tools, but the guidance is concrete and actionable.

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

All Stocklake — AI Stock Intelligence toolsInspect
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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