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)
sector_score / strength_score: sector_score is a real, continuous 0-100 read on this sector's relative strength/leadership (0=LAGGING, 100=LEADING) - the same underlying number signal buckets into 5 discrete categories, blending arithmetic inputs (RSI/perf percentiles, top-5 concentration, SMA200 breadth) with strength_score, the AI's own 1-10 read. Comparable across all 11 sectors on one absolute scale (not per-sector-relative). Null on a pre-2026-08-26 assessment that predates this field. Not a buy/sell call.
sector_score_trend: {change_7d, change_30d, direction} - whether this sector's score is improving/deteriorating/stable over the trailing 7/30 days, computed automatically. Single-sector calls only - this is the only trend view available for one sector at all (history_count only applies to the all-sectors call). Two sectors both reading STRONG/68 can be in opposite motion; this tells them apart. Either leg is null without enough history yet.

Refreshed ~4x/day, weekdays only, during market hours (~2h apart) - dead overnight and on weekends, not a continuous 4-hourly cadence. Check the returned updated_at before treating this as current, especially on a Monday morning or after a holiday. Available to pro tier only (AI pipeline costs). For informational purposes only. Not financial advice.

Input 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

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 12, 2026

changedInput schema / properties / sector / description

Previous value: -"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."New value: +"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."

Changed3 schema fields changedAug 25, 2026

addedInput schema / properties / history_count / description

Added value: +"Include the last N prior signal states per sector, 0-3 (default 0). Only applies to the all-sectors call."

addedInput schema / properties / sector / description

Added value: +"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."

addedInput schema / properties / sort_by_strength / description

Added value: +"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."

Changed2 schema fields changedJul 25, 2026
- addedInput schema / properties / history_count
```
Added value: +{
+  "default": 0,
+  "type": "integer"
+}
```
- addedInput schema / properties / sort_by_strength
```
Added value: +{
+  "default": false,
+  "type": "boolean"
+}
```
First observedJun 3, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Goes well beyond the annotations (which already declare readOnly/idempotent/non-destructive) with refresh cadence ('~4x/day, weekdays only, dead overnight and on weekends'), a staleness warning to check updated_at, a pro-tier access restriction, and null semantics for assessments predating 2026-08-26. It also frames sector_score as 'not a buy/sell call', setting expectations about interpretation.

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?

Front-loaded with purpose, then a tight bulleted parameter section, then operational caveats — a good structure. It is long and carries minor redundancy ('For informational purposes only. Not financial advice.' alongside 'Not a buy/sell call'), and the sector_score paragraph could be trimmed.

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 three optional parameters, an output schema, and rich annotations, the description covers everything an agent needs: mode selection, parameter interactions, data freshness, tier gating, and null/edge cases. Nothing material is missing for correct invocation.

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 already 100%, so the schema documents all three parameters; the description largely restates their behavior (all-sectors-only, ignored for single sector). Where it adds real value is the distinction between sector_score (continuous 0-100, absolute cross-sector scale) and strength_score (AI's 1-10 read), plus the trend object shape — though those are output fields rather than parameters.

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?

States a specific verb+resource ('AI-assessed sector intelligence') and enumerates exactly what it returns (signal, cycle stage, rotation signal, drivers, alerts, computed stats), so an agent can distinguish it from get_market_assessment or get_signals without opening a schema. The dual single-sector/all-sectors scope is stated up front.

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?

Explicitly says to pass a sector name for one sector and omit/None for all 11, and names the intended use of each mode ('the all-sectors call doubles as the rotation view'). It also states the exact conditions for sort_by_strength, history_count, and sector_score_trend, including that the trend view exists only for single-sector calls.

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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