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Stocklake — AI Stock Intelligence

by dev.stocklake

Get Earnings Intelligence

get_earnings_intelligence
Read-onlyIdempotent

Upcoming earnings with AI context — AI scores, verdicts, and risk factors per stock. Combines the earnings calendar with AI pipeline data to surface which upcoming earnings events are worth monitoring.

Parameters:

  • days_ahead: look-ahead window in days (default 14, max 30)

  • sector: filter to one sector (e.g. "Technology")

  • min_ai_score: only return stocks with AI score >= this value, 0-100 (optional). Renamed 2026-08-24 from the retired 0-10 min_flag_score — gates on the same stock_ai_summary.py ai_score field the response already returns, rather than the raw legacy flag_score field, which is no longer part of this fleet's public vocabulary at all. Applied server-side before limit truncates the result — a stock with a qualifying score always counts against limit ahead of one without, rather than being cut off first for reporting later in the earnings window.

  • limit: max results to return (default 25, max 25). Each returned ticker counts as one call toward your daily limit — see the docs' rate-limit section.

Returns per stock (sorted by earnings_date ascending):

  • earnings_date: ISO UTC timestamp · is_estimate: whether date is estimated

  • symbol, name, sector, price, rsi, market_cap

  • eps_trailing, eps_forward (earnings expectations context)

  • ai_verdict (positive/neutral/negative, from nightly AI pipeline)

  • ai_score (0-100) / ai_score_band (Weak/Moderate/Strong/Very Strong) — stock_ai_summary.py's own composite score, same 0-100 scale/band convention as signals.signal_score but a distinct field/pipeline; null if this stock has no ai_summary doc yet.

  • ai_risks: top 2 AI-identified risk factors

  • analyst_rating, analyst_target

Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 25, max 25). Each returned ticker counts as one call toward your daily limit.
sectorNoFilter to one sector, e.g. "Technology".
days_aheadNoLook-ahead window in days (default 14, max 30).
min_ai_scoreNoOnly return stocks with AI score >= this value, 0-100 (optional). Renamed 2026-08-24 from the retired 0-10 min_flag_score — gates on the same stock_ai_summary.py ai_score field the response already returns. Applied server-side before limit truncates the result, so a qualifying stock always counts against limit ahead of one without.

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 the tool read-only and idempotent; the description goes further by explaining server-side filtering before truncation, per-result call counting against rate limits, Pro-tier availability, null behavior when no AI summary exists, sorting order, and the non-financial-advice disclaimer. This is rich behavior disclosure 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 front-loaded with a clear purpose and then uses structured sections for parameters and return values. It is somewhat long and partially duplicates schema parameter descriptions, but every section contributes useful information and no filler is present.

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 covers defaults, max values, filtering behavior, output content, sorting, disclaimer, rate-limit accounting, and tier requirements. An AI agent has enough context to call and interpret results correctly, especially with the output schema also available.

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 carries the core parameter definitions. The description still adds meaningful context beyond the schema for min_ai_score, including the legacy min_flag_score rename, the precise field it gates on, and the ordering nuance between filter application and limit truncation. Most other parameters are redundant with the schema, so a 5 is not warranted.

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 clearly states the tool's purpose: it combines the earnings calendar with AI pipeline data to surface upcoming earnings events with AI scores, verdicts, and risk factors. This distinguishes it from the raw get_earnings_calendar sibling and makes the resource and value proposition explicit.

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 implies when to use this tool: when an agent needs AI-contextualized earnings, such as scores, verdicts, and risk factors, rather than a plain earnings calendar. It does not explicitly name alternatives or say when not to use it, but the context is clear enough for selection.

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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