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RiskModels Rankings Screen

riskmodels_screen_rankings
Read-only

Rank and screen US stocks by metric, cohort, and window, filtering by percentile, decile, or sector to return high- or low-ranking rows.

Instructions

Use when you want the names in the universe that rank highest or lowest on one metric, with optional percentile, decile and sector filters. Not for one stock's rank (use riskmodels_get_rankings). Returns, from POST /rankings/screen, the ranked rows that pass the filters; metrics, cohorts and windows are the same as for one stock's rank. End-of-day data, refreshed after each US market close. Price: $0.25 per call.

Input Schema

NameRequiredDescriptionDefault
as_ofNoRanking date (default latest), format YYYY-MM-DD, e.g. 2026-06-30
limitNoMaximum rows, integer, e.g. 25 (default 100)
cohortYesCohort: universe, sector or subsector, e.g. universe
decileNoRestrict to one decile, integer 1-10, e.g. 10
metricYesMetric: subsector_residual, sector_residual, gross_return, mkt_cap, er_l1, er_l2 or er_l3, e.g. er_l3
windowYesWindow in trading days: 1d, 21d, 63d or 252d, e.g. 21d
sector_filterNoRestrict to one sector, by sector ETF ticker, e.g. XLK
min_percentileNoMinimum percentile, 0-100, e.g. 90

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed9 schema fields changedv1.0.12
    • changedInput schema / properties / as_of / description
      Previous value: -"As-of date (YYYY-MM-DD)"New value: +"Ranking date (default latest), format YYYY-MM-DD, e.g. 2026-06-30"
    • addedInput schema / properties / as_of / pattern
      Added value: +"^\\d{4}-\\d{2}-\\d{2}$"
    • changedInput schema / properties / cohort / description
      Previous value: -"universe, sector or subsector"New value: +"Cohort: universe, sector or subsector, e.g. universe"
    • changedInput schema / properties / decile / description
      Previous value: -"Restrict to a decile (1-10)"New value: +"Restrict to one decile, integer 1-10, e.g. 10"
    • changedInput schema / properties / limit / description
      Previous value: -"Max rows (default 100)"New value: +"Maximum rows, integer, e.g. 25 (default 100)"
    • 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. er_l3"
    • changedInput schema / properties / min_percentile / description
      Previous value: -"Minimum percentile filter"New value: +"Minimum percentile, 0-100, e.g. 90"
    • changedInput schema / properties / sector_filter / description
      Previous value: -"Restrict to a sector"New value: +"Restrict to one sector, by sector ETF ticker, e.g. XLK"
    • 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. 21d"
  2. First observedv1.0.11

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint, destructiveHint=false, openWorldHint), so the bar is lower. The description adds genuinely useful operational context beyond them: end-of-day data refreshed after each US market close, a $0.25 per-call price, and what gets returned (the ranked rows passing the filters). It stops short of describing pagination or auth, but adds real value over the structured fields.

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 when-to-use clause, then the sibling exclusion, then return/freshness/cost details. Dense but every sentence carries information; there is no redundant repetition of the title or name.

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?

No output schema exists, so the description carries the return-value burden and does explain that it returns the ranked rows that pass the filters, plus freshness and pricing. For an eight-parameter screening tool this is largely complete, though row shape and pagination semantics remain implicit.

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 eight parameters, making the baseline 3. The description only conceptually groups the filters ('percentile, decile and sector filters') and cross-references the metric/cohort/window definitions used by riskmodels_get_rankings; it adds little syntax or format detail beyond the schema.

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: the names in the universe ranked highest or lowest on one metric with optional filters. It explicitly distinguishes itself from the sibling riskmodels_get_rankings ('Not for one stock's rank'), so an agent can tell them apart 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?

Opens with an explicit when-to-use ('Use when you want the names in the universe that rank highest or lowest'), names the condition selecting the alternative sibling, and identifies the endpoint (POST /rankings/screen). No exclusions are left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.