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Stocklake — AI Stock Intelligence

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

A4.6/5.0
Behavior5/5

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

Annotations declare read-only/idempotent/non-destructive behavior, and the description adds substantial extra context: pro-tier gating, silent ignores and fallbacks on free/guest tiers, daily-limit counting, the AI-score field provenance, as well exactly which fields each tier receives. No statement contradicts 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?

Well structured and front-loaded: purpose, parameters, returns, tier behavior, and next-step routing. It is long, but the tool is complex. It loses the top score because it repeats a good deal of what the input schema already spells out and includes internal implementation details like stock_ai_summary.py and the retired flag_score renaming that could be trimmed.

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?

For a complex, tiered, filter-heavy tool with 18 optional parameters, the description covers what is returned, what counts against quotas, pro/free differences, silent resource fallbacks, and which sibling tool to call next. An agent has everything needed to invoke it correctly, and the output schema covers the remaining return-shape details.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already carries most parameter meaning. The description still adds value by grouping the filter dimensions into conceptual families, providing practical worked examples for RSI, performance, P/E, and volume screens, and explaining cross-parameter behaviors like presets influencing default sort_by. This is a step beyond the schema baseline.

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 uses a concrete verb-resource pair — "Filter and rank stocks" — and scopes the tool to the Stocklake universe. The stated dimensions (fundamentals, technicals, AI signals) and the follow-up to get_stock_research make it clear this is the screening/ranking tool, not the single-stock or news/history sibling.

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?

It clearly defines when this tool is used: screen and rank the broad universe with filters/presets, with pro-tier behaviors explicitly called out. It also tells the agent to call get_stock_research for deeper per-result research. It does not fully contrast against siblings like get_stocks, get_market_movers, or get_signals, so it stops just short of a 5.

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

A4.3/5.0
Disambiguation4/5

Most tools carve out clearly distinct jobs: per-stock quotes, batch quotes, earnings calendars, market pulse, news, signals, screener, watchlist. A few pairs—notably get_stock vs get_stocks and get_market_assessment vs get_market_pulse—are close enough that an agent would need to read descriptions carefully to pick the right one.

Naming Consistency5/5

All tool names follow a uniform get_<snake_case_noun> pattern with no mixed conventions. The singular/plural pair get_stock and get_stocks is a deliberate batch-version distinction rather than an inconsistency, and compounds like earnings_intelligence vs market_assessment remain predictable.

Tool Count4/5

With 17 tools, the server is slightly over the ideal 3-15 range, but the count is well-justified by the breadth of the stock-intelligence domain: per-stock, batch, historical, market-level, earnings, insider, news, signals, screeners, and watchlists all have a dedicated entry point. It is a larger surface area than ideal, but none of the tools feel redundant or purely decorative.

Completeness4/5

The tool surface gives agents full read-oriented workflows: single-stock data, batch lookups, historical prices, indicator history, news, research bundles, insider activity, market health, earnings, signals, movers, sector intelligence, and screening all exist. The main gap is discovery—there is no symbol/name search tool beyond the screener category filters, and historical earnings-surprise data is absent—but agents can work around both via existing tools.

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