Stocklake — AI Stock Intelligence

Get Sector Intelligence

get_sector_intelligence

Read-onlyIdempotent

AI-assessed sector intelligence: signal, cycle stage, rotation signal, drivers, alerts, and computed statistics per sector (RSI distribution, breadth, performance 1W/1M, top/bottom movers, historical percentiles). Pass a sector name for a single sector, or omit the parameter (or pass None) to get the latest assessment for all 11 sectors — the all-sectors call doubles as the rotation view: use sort_by_strength to rank LEADING-first for finding leading vs lagging sectors, and history_count for prior signal states per sector.

sort_by_strength: sort all-sectors output LEADING→LAGGING instead of alphabetical (all-sectors call only; ignored when a single sector is requested)
history_count: include last N prior signal states per sector, 0-3 (default 0; all-sectors call only)

Refreshed every ~4 hours by the market intelligence pipeline. Available to pro tier only (AI pipeline costs). For informational purposes only. Not financial advice.

Input Schema

TableJSON Schema

Name	Required	Description
`sector`	No	Pass a sector name for a single sector's assessment, or omit (None) to get the latest assessment for all 11 sectors at once — the all-sectors call doubles as the rotation view.
`history_count`	No	Include the last N prior signal states per sector, 0-3 (default 0). Only applies to the all-sectors call.
`sort_by_strength`	No	Sort all-sectors output LEADING→LAGGING instead of alphabetical, for finding leading vs lagging sectors. Only applies to the all-sectors call; ignored when a single sector is requested.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context by disclosing the ~4-hour refresh cadence, pro-tier restriction, and informational-only disclaimer. It also clarifies parameter scope (single vs all-sectors). No contradictions with 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 moderately sized but well-structured: it opens with the core purpose, then explains parameters in a bulleted list, followed by operational details. It is front-loaded with the main behavior and prioritizes the most critical information. A bit verbose in places, but every sentence earns its place.

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 has an output schema (not shown here), the description explains the returned data components, parameter nuances, refresh frequency, access tier, and disclaimer. It covers all necessary aspects for an agent to call it correctly. Nothing critical 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%, so the input schema already documents each parameter thoroughly. The description restates the parameter behavior (e.g., 'only applies to all-sectors call') but does not add new meaning beyond what the schema provides. Thus baseline 3 is appropriate.

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 provides AI-assessed sector intelligence with specific components like signal, cycle stage, rotation signal, drivers, alerts, and computed statistics. It differentiates from sibling tools by focusing on sector-level analysis. The verb 'get' is paired with the resource 'sector intelligence', and the dual mode (single vs all sectors) is 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 gives clear guidance on when to pass a sector name versus omit it, and explains the all-sectors call as a rotation view. It also notes the sort_by_strength and history_count parameters are only for the all-sectors call. However, it does not explicitly contrast this tool with alternatives like get_market_assessment or get_signals, so the when-not-to-use guidance is implied rather than stated.

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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