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BlueWaterCorp

RiskModels

by BlueWaterCorp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

RiskModels Cross-Sectional Rankings

riskmodels_get_rankings
Read-only

Get a single stock's rank and percentile within its universe, sector, or subsector for a selected risk or return metric and window.

Instructions

Use when you want one stock's percentile rank within the universe, its sector or its subsector on a risk or return metric. Not for screening the whole universe (use riskmodels_screen_rankings). Returns, from GET /rankings/{ticker}, the stock's rank and percentile for the metric and window. Metrics: subsector_residual, sector_residual, gross_return, mkt_cap, er_l1, er_l2 or er_l3. Windows: 1d, 21d, 63d, 252d. End-of-day data, refreshed after each US market close. Price: $0.005 per call.

Input Schema

NameRequiredDescriptionDefault
cohortNoCohort: universe, sector or subsector, e.g. sector
metricNoMetric: subsector_residual, sector_residual, gross_return, mkt_cap, er_l1, er_l2 or er_l3, e.g. subsector_residual
tickerYesUS stock ticker symbol, case-insensitive, e.g. NVDA
windowNoWindow in trading days: 1d, 21d, 63d or 252d, e.g. 63d

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv1.0.12
    • changedInput schema / properties / cohort / description
      Previous value: -"universe, sector or subsector"New value: +"Cohort: universe, sector or subsector, e.g. sector"
    • changedInput schema / properties / metric / description
      Previous value: -"subsector_residual, sector_residual, gross_return, mkt_cap, er_l1, er_l2 or er_l3"New value: +"Metric: subsector_residual, sector_residual, gross_return, mkt_cap, er_l1, er_l2 or er_l3, e.g. subsector_residual"
    • changedInput schema / properties / ticker / description
      Previous value: -"Ticker symbol, e.g. NVDA"New value: +"US stock ticker symbol, case-insensitive, e.g. NVDA"
    • changedInput schema / properties / window / description
      Previous value: -"1d, 21d, 63d or 252d"New value: +"Window in trading days: 1d, 21d, 63d or 252d, e.g. 63d"
  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?

Beyond the readOnly/openWorld annotations, the description discloses the underlying endpoint, the returned fields (rank and percentile), data freshness (end-of-day, refreshed after each US market close), and cost ($0.005 per call). This is unusually rich operational context that annotations do not carry.

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 the use case and the sibling exclusion, then supporting detail. Four compact sentences, though the metric and window enumerations duplicate the schema and 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?

No output schema exists, and the description names the returned values (rank and percentile) plus endpoint and freshness. Safety is covered by annotations, so nothing an agent needs to invoke this correctly 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 schema already documents all four parameters, including valid metric and window values. The description restates the same metric and window enumerations and only loosely clarifies that the cohort determines the comparison group; extension beyond the schema is minimal, so the baseline 3 applies.

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 and resource ('one stock's percentile rank within the universe, its sector or its subsector on a risk or return metric') and names the sibling it is not (riskmodels_screen_rankings). An agent can distinguish this from screening without opening the 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?

Explicit when-to-use (single-stock rank/percentile) plus an explicit exclusion with a named alternative: 'Not for screening the whole universe (use riskmodels_screen_rankings)'. Routing is unambiguous.

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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