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Stocklake — AI Stock Intelligence

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Get Market Assessment

get_market_assessment
Read-onlyIdempotent

Combined AI market assessment: macro regime + market outlook in a single call. Refreshed ~4x/day, weekdays only, during market hours (~2h apart) - dead overnight and on weekends, not a continuous 4-hourly cadence. Check regime_stale/outlook_stale below (which already account for the weekend gap) before treating either as current, especially on a Monday morning.

Two distinct perspectives returned together:

  • REGIME (RISK_OFF/CAUTIOUS/NEUTRAL/AGGRESSIVE): answers "how much equity risk to take" → use for position sizing and asset allocation decisions

  • OUTLOOK (POSITIVE/NEUTRAL/NEGATIVE): answers "which direction and sectors to trade" → use for sector preference and directional bias

Both share the same pipeline run so they are always in sync.

  • history_count: include last N prior assessments for each (0-3, default 0)

  • regime_*: risk posture fields - regime, risk_appetite_score (0-100 re-expression of regime, higher = current conditions support more risk-taking), regime_bias, regime_bias_note (plain sentence on whether current conditions favor long or short setups, or neither), regime_confidence, regime_rationale, key_risks, watch_for, vix_at_assessment, regime_updated_at, regime_stale

  • macro_score / regime_strength: macro_score is a real, continuous 0-100 read on how much risk the current environment supports (0=RISK_OFF/capital preservation, 100=AGGRESSIVE/risk-on) - the same underlying number regime buckets into 4 discrete categories, blending arithmetic inputs (VIX level, breadth oversold/overbought skew, SKEW-vs-VIX divergence, TD-exhaustion ratio) with regime_strength, the AI's own 1-10 read of regime conviction. Distinct from risk_appetite_score (a coarse 4-value lookup on regime alone) - macro_score is the real underlying number. Null on a pre-2026-08-26 assessment that predates this field. Not a call on any one stock.

  • macro_score_trend: {change_7d, change_30d, direction} - whether macro_score itself is improving/deteriorating/stable over the trailing 7/30 days, computed automatically (no history_count needed). A bare 33 doesn't tell you whether the environment is getting worse or just stabilized after a worse reading; this does. Either leg is null without enough history yet.

  • regime_stale / outlook_stale: true if that assessment is >72h old (the pipeline runs weekdays only, so a normal Fri→Mon gap does not trip this - only a genuinely broken/stuck pipeline does). null if the timestamp itself is missing.

  • indicators.macro_data: macro data (yield curve, Fed funds, cpi_index, unemployment, M2)

  • indicators.volatility_term_structure: VIX spot/3M/6M term structure + contango signal

  • indicators.market_sentiment: Fear & Greed index value and label

  • market_context: price/RSI/SMA200/perf snapshot of SPY/QQQ/IWM/TLT/GLD/VIX/TNX + sectors NOTE: point-in-time snapshot recorded when AI ran - not current prices (use get_market_pulse for the latest)

  • outlook_*: directional fields - outlook, outlook_conviction, equity_view, preferred_sectors, avoided_sectors, catalyst, outlook_key_risk, outlook_rationale, outlook_updated_at, outlook_stale

Pro tier only - AI pipeline cost attached. For informational purposes only. Not financial advice.

Input Schema

NameRequiredDescriptionDefault
history_countNoInclude the last N prior assessments for each of regime and outlook (0-3, default 0).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / history_count / description
      Added value: +"Include the last N prior assessments for each of regime and outlook (0-3, default 0)."
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already establish read-only/idempotent safety, yet the description adds substantial non-structured context: a ~4x/day weekday-only refresh cadence with dead weekends, the exact >72h staleness rule and why a Fri→Mon gap does not trip it, null semantics for missing timestamps, the Pro-tier cost attachment, and the caveat that market_context is a frozen snapshot. These are exactly the operational facts an agent needs and none are derivable from 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.

Conciseness4/5

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

Content is front-loaded: cadence and staleness caveats come before field enumeration, and the regime/outlook split is visually separated. It is long and has minor redundancy — staleness is explained once in the header and again in the field list, and macro_score is re-explained across two bullets — which costs it a point.

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 high-complexity composite tool with an output schema, the description covers cadence, staleness, tier gating, field-level meaning including the subtle macro_score vs risk_appetite_score distinction, and honest disclaimers. An agent can call and interpret this tool without opening the output schema.

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?

With only one optional parameter at 100% schema coverage, the baseline would be 3; the description earns an extra point by restating the 0-3/default-0 bound and, more usefully, clarifying that macro_score_trend computes automatically without history_count — a non-obvious interaction the schema cannot express.

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 ('Combined AI market assessment: macro regime + market outlook') and immediately splits it into two named perspectives with distinct decision uses. It also distinguishes itself from get_market_pulse by explicitly scoping market_context as a point-in-time snapshot rather than live prices.

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?

It gives explicit routing for each half — regime for position sizing/allocation, outlook for sector preference and directional bias — and tells the agent to check regime_stale/outlook_stale before treating values as current. It names one sibling alternative (get_market_pulse for latest prices) but offers no guidance on when not to call this tool at all (e.g., non-Pro tier, or topics better served by get_signals/get_sector_intelligence).

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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