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RiskModels

by BlueWaterCorp
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TypeScript
Hybrid

RiskModels Residual Mean-Reversion Signal

riskmodels_get_residual_signal
Read-only

Aggregate L3 residual mean-reversion signals across a 1–100 stock basket, with optional weights and a minimum signal-quality quintile, using recent end-of-day data.

Instructions

Use when you want the L3 residual mean-reversion signal aggregated over a basket of 1-100 stocks. Not for the residual return series themselves (use riskmodels_get_residual_returns). Returns, from POST /signals/residual-reversion/basket, the basket-aggregated signal, with optional weights and a minimum signal-quality quintile; it describes recent residual moves and is not a forecast. End-of-day data, refreshed after each US market close. Price: $0.02 per call.

Input Schema

NameRequiredDescriptionDefault
tickersYes1-100 basket ticker symbols, e.g. ["AAPL","MSFT","NVDA"]
weightsNoWeights aligned to tickers, decimals, e.g. [0.5,0.3,0.2] (default equal weight)
signal_quality_min_quintileNoDrop names below this signal-quality quintile, integer 1-5, e.g. 3

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.0.12
    • changedInput schema / properties / signal_quality_min_quintile / description
      Previous value: -"Drop names below this signal-quality quintile (1-5)"New value: +"Drop names below this signal-quality quintile, integer 1-5, e.g. 3"
    • changedInput schema / properties / tickers / description
      Previous value: -"Basket tickers"New value: +"1-100 basket ticker symbols, e.g. [\"AAPL\",\"MSFT\",\"NVDA\"]"
    • changedInput schema / properties / weights / description
      Previous value: -"Optional weights, aligned to tickers"New value: +"Weights aligned to tickers, decimals, e.g. [0.5,0.3,0.2] (default equal weight)"
  2. First observedv1.0.11

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral context: it is a basket-aggregated signal, not a forecast, based on end-of-day data refreshed after US market close, with a stated price per call. 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.

Conciseness5/5

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

The description is front-loaded with the primary use case and exclusion, then adds return source, constraints, data freshness, and price. Every sentence earns its place with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description should ideally explain return values more concretely. It does describe the conceptual return (basket-aggregated signal) and important caveats, but stops short of specifying response shape or field structure. Still, it is largely complete for an agent to invoke the tool correctly.

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 schema already documents tickers, weights, and signal_quality_min_quintile in detail. The description mentions optional weights and a minimum signal-quality quintile, but adds no syntax or format meaning beyond the schema, making the baseline 3 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 gives a specific verb-resource pair: aggregating the L3 residual mean-reversion signal over a basket of 1-100 stocks. It explicitly distinguishes itself from the sibling tool riskmodels_get_residual_returns, so an agent can route correctly without opening either schema.

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?

It states when to use the tool ('Use when you want the L3 residual mean-reversion signal aggregated over a basket...') and when not to ('Not for the residual return series themselves (use riskmodels_get_residual_returns)'). This is explicit routing guidance with a named alternative.

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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