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Stocklake — AI Stock Intelligence

by dev.stocklake.api

Get Market Movers

get_market_movers
Read-onlyIdempotent

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 (e.g. 1.0 = $1B+)

Returns per stock: symbol, name, sector, price, change_pct, volume, rsi, market_cap, 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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoResults 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.
categoryNoWhich movers category to return: "gainers" | "losers" | "most_active" | "all" (default "all" = all 3 categories).all
min_market_cap_bNoFilter to stocks above this market cap in billions of dollars, e.g. 1.0 = $1B+.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already mark the tool read-only/idempotent/non-destructive. The description adds valuable behavioral detail: daily-limit counting per category, atr_pct omissions when data is missing/corrupted, and Pro-tier AI fields being precomputed with no extra AI cost. These go well beyond the structured 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 opens with a one-line purpose summary, then uses bullets for parameters and a terse return-field list. The AI-narrative justification is slightly promotional but remains short and relevant. Overall well-structured with minimal waste.

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?

With an output schema present, the description doesn't need to explain return values, yet it still covers edge cases (atr_pct omitted), quota accounting, and tier differences. It also points to get_stock_research for follow-up. Nothing needed for a correct call is missing.

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?

Schema description coverage is 100% for all three parameters. The description simply restates the same defaults and formats (category values, limit max 20, min_market_cap_b in billions) without adding new semantic information. Baseline 3 applies.

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?

Description states 'Top market movers from the Stocklake universe — gainers, losers, most active.' This clearly identifies the resource and categories, distinguishing it from data-retrieval siblings. It also names get_stock_research as the alternative for deeper research, reinforcing the scope.

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?

Provides explicit routing: 'For the full research bundle on any one mover, call get_stock_research(symbol).' It also explains tier availability, category modes, and daily-limit implications, giving clear context for when the tool is appropriate. It doesn't enumerate all sibling distinctions, but the named alternative is strong evidence.

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