Skip to main content
Glama
Sign In
rigor-mcp

rigor-mcp

by mrnh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

levene_test

Read-onlyIdempotent

Tests equality of population variances across two or more groups using the robust Brown-Forsythe median-based variant. Helps decide whether to assume equal variances for t-tests or validate ANOVA assumptions.

Instructions

Test whether two or more groups have equal population variances (homogeneity of variance) -- use this to decide equal_var for two_sample_t_test, or to sanity-check one_way_anova's equal-variance assumption. Uses the Brown-Forsythe variant (deviations from each group's median), more robust to non-normal data than the original mean-based Levene's test. Returns the same shape as one_way_anova (it's computed as one internally, on absolute deviations from each group's median).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "groups": "<groups>"
    },
    "name": "levene_test"
  }
}

Input Schema

NameRequiredDescriptionDefault
alphaNosignificance level for the test (and any confidence interval); default 0.05
groupsYesone list of observations per group; at least 2 groups, each with at least 2 observations

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.0

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses the Brown-Forsythe variant, explains that deviations are taken from group medians, notes robustness to non-normal data, and clarifies the internal computation path as one_way_anova applied to absolute deviations. This provides meaningful behavioral context.

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 compact but information-dense. The first sentence establishes the core purpose, the second adds usage framing, and the third explains the statistical variant and output shape. Each sentence contributes necessary information with no 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?

The description covers purpose, method, robustness considerations, and its relationship to one_way_anova. Since there is no output schema, the mention that it returns the same shape as one_way_anova helps, though it could be clearer by explicitly naming the Levene F-statistic and p-value.

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?

The input schema already documents both alpha and groups with clear descriptions, and coverage is 100%. The description adds broader context about groups, but does not substantially add parameter-level meaning beyond what the schema already provides.

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 clearly states the tool tests whether two or more groups have equal population variances (homogeneity of variance). It also distinguishes itself from related statistical tests by explicitly mentioning its role in supporting two_sample_t_test and one_way_anova.

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?

The description gives explicit usage context: use it to decide the equal_var parameter for two_sample_t_test or to sanity-check one_way_anova's equal-variance assumption. It does not explicitly state when not to use it or name alternative variance-comparison approaches, so it misses full exclusionary guidance.

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

Deploy Server

Other Tools