Stocklake — AI Stock Intelligence

Get Stock Research

get_stock_research

Read-onlyIdempotent

Full AI research bundle for a stock in one call — fundamentals, AI-generated summary, recent AI-classified news, insider/institutional signal, and recent trade signal history. Replaces 4 separate calls: get_stock + get_stock_news + get_insider_activity + get_signals (for one symbol).

Returns:

stock: price, name, sector, rsi, pe_forward, market_cap, 52-week range, analyst data
ai_summary: verdict, ai_score (0-100)/ai_score_band (Weak/Moderate/Strong/ Very Strong — stock_ai_summary.py's own composite, same scale/band convention as signal_score but a distinct field/pipeline), full summary, key_points, risks, price_at_generation, generated_at, headline (one-sentence plain-language take), near_term (stance/confidence over <4 weeks — technicals/momentum-weighted), longer_term (stance/confidence over a multi-month horizon — fundamentals/analyst/institutional-flow-weighted). headline/near_term/longer_term are null on summaries generated before this schema shipped — until that symbol's next regeneration, fall back to verdict/ai_score.
news: last 3 high-relevance articles (title, published_at, ai_sentiment, ai_summary, signal_score [0-100]/signal_score_band — this symbol's LIVE news-sourced signal score if one exists in the last 90 days [same number as the signals list below and get_signals()/get_stock_news(), kept in sync as it's re-scored], else a per-article magnitude computed from that article's own classification. One name, no separate "news_score" field.)
sentiment: signal, signal_score (0-100)/signal_score_band — one name, no separate "insider_score" field, same as the news block above — insider_trend (buying/selling/neutral, or null with no transactions in the window), institutional_pct
signals[]: up to the 5 most recent trade signals for this symbol in the last 90 days (direction, rationale, signal_score [0-100], signal_score_band [human-readable label — "Weak"/"Moderate"/"Strong"/"Very Strong" — or null alongside a null signal_score], source, expires, flagged_at). Empty if nothing in that window. Recency-gated, not gated on whether Stocklake's own trading engine still holds the signal live — a signal it dropped or let expire is still a real, useful fact about what the pipeline found, so it stays in this list rather than disappearing the moment our own paper trader moves on.

All data is pre-computed by the Stocklake AI pipeline — no live AI calls on request. Pro tier only. For informational purposes only. Not financial advice.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	Yes	Stock ticker symbol to fetch the full AI research bundle for.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, and the description adds significant operational context: no live AI calls, pre-computed pipeline data, recency-gated trade signals that persist even if the internal trader dropped them, null fallback for pre-schema summaries, and explicit score-field naming/sync conventions. This goes far 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured: a front-loaded summary, a clear replacement statement, detailed labeled return blocks, and explicit edge cases. Every sentence contributes operational or selection-relevant information, and the bulleted layout makes the complexity scannable.

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?

This is a composite tool with multiple return blocks, score band conventions, null cases, sync behavior, and signal retention nuances. The description documents all of these clearly. With an output schema also present, an agent has everything needed to call the tool correctly and interpret its results.

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?

The input schema already covers the single symbol parameter with 100% description coverage. The tool description reinforces that symbol is a stock ticker for the research bundle but does not add new parameter semantics. Baseline 3 is appropriate because the schema carries the full weight.

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+resource: 'Full AI research bundle for a stock in one call' and enumerates exactly what it returns: fundamentals, AI summary, news, insider/institutional signal, and trade signal history. It explicitly names the four sibling tools it replaces, so an agent can immediately distinguish it from get_stock, get_stock_news, get_insider_activity, and 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 Guidelines5/5

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

The description explicitly says it replaces get_stock + get_stock_news + get_insider_activity + get_signals for one symbol, giving a clear selection rule versus alternatives. It also notes the Pro tier restriction and that data is pre-computed, which helps an agent decide when this tool is appropriate.

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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