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

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

Schema Changelog

Changes observed during successful MCP inspections.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context: refresh cadence (~4x/day weekdays during market hours, not continuous), staleness risk overnight/weekends, pro-tier gating, sector_score semantics (continuous 0-100, comparable across sectors, null pre-2026-08-26), and the explicit 'Not a buy/sell call' disclaimer. No contradiction 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 long but information-dense and front-loaded: the core purpose and the single-vs-all decision come first, then parameter semantics, then refresh/availability caveats. Every sentence earns its place; the bulleted parameter explanations are scannable. Slightly verbose in the sector_score explanation, but justified given the semantic subtlety.

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?

For a read-only intelligence tool with an output schema, the description covers the key operational facts an agent needs: how to request single vs all sectors, how to rank by strength, how to get trend context, refresh cadence, staleness caveats, and access restrictions. The output schema handles return-value details, so nothing critical is missing.

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 baseline is 3. The description adds real value beyond the schema: it explains that sector_score is a continuous 0-100 read underlying the 5 discrete signal buckets, that sector_score_trend is single-sector-only and distinguishes two sectors with the same score, and that history_count/sort_by_strength are all-sectors-only. It doesn't exhaustively document every output field, but the schema already does that.

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 precise verb+resource statement: 'AI-assessed sector intelligence' enumerating signal, cycle stage, rotation signal, drivers, alerts, and computed statistics. It clearly distinguishes the single-sector call from the all-sectors rotation view, and the sibling list shows no overlapping tool that does sector intelligence, so an agent can select it confidently.

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 tells the agent when to pass a sector name vs omit it, and frames the all-sectors call as the rotation view with sort_by_strength for LEADING-first ranking. It also states refresh cadence, weekday/market-hours limitations, pro-tier availability, and the caveat to check updated_at — all concrete usage guidance beyond the schema.

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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