Stocklake — AI Stock Intelligence

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

Name	Required	Description
`limit`	No	Max results to return (default 25, max 25). Each returned ticker counts as one call toward your daily limit.
`sector`	No	Filter to one sector, e.g. "Technology".
`days_ahead`	No	Look-ahead window in days (default 14, max 30).
`min_ai_score`	No	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. Applied server-side before limit truncates the result, so a qualifying stock always counts against limit ahead of one without.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description goes well beyond that: it discloses the 'Pro tier only — AI pipeline cost attached', states the informational purpose, explains the server-side filtering order relative to limit, details the nullability of ai_score (absence of ai_summary doc), and traces the field provenance to stock_ai_summary.py. This adds rich behavioral context that the annotations do not cover.

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 fairly long but well-structured with sections for purpose, parameters, return fields, and caveats. It front-loads the core purpose in the first sentence, and all sentences carry substantive information. While it could be tightened, the organization and lack of filler make it efficient for its complexity.

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 (4 optional parameters, a rich output schema, and multiple caveats), the description covers all essential aspects: what it does, what data it returns, parameter semantics, cost implications, and informational limitations. The return field list and the note about sorting and nullability make it complete for an agent to call correctly without external documentation.

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 description coverage is 100%, so the schema already documents each parameter. The description adds value by explaining the min_ai_score renaming history and the ordering effect with limit, and clarifies that all parameters are optional. It provides extra nuance beyond the schema's literal descriptions, earning above the baseline of 3.

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 states a specific verb and resource ('Upcoming earnings with AI context — AI scores, verdicts, and risk factors per stock') and clearly distinguishes it from plain earnings calendars by emphasizing the AI pipeline integration. The phrase 'Combines the earnings calendar with AI pipeline data' makes the unique value explicit, differentiating it from the sibling get_earnings_calendar.

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

Usage Guidelines3/5

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 you need AI context on earnings), but it does not explicitly state when NOT to use it or recommend a sibling alternative like get_earnings_calendar for calendar-only needs. While it clarifies parameters and return behavior, it lacks explicit routing guidance, leaving the choice to inference.

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.

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