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rigor-mcp

rigor-mcp

by mrnh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

omega_squared

Read-onlyIdempotent

Calculate unbiased omega-squared effect size for one-way ANOVA, correcting for small-sample bias by subtracting variance explained by chance.

Instructions

Effect size for a one-way ANOVA, less biased than eta_squared for small samples since it subtracts out the variance explained by chance alone. Use alongside one_way_anova. Can be slightly negative when the true effect is near zero -- that's expected, not an error.

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": "omega_squared"
  }
}

Input Schema

NameRequiredDescriptionDefault
groupsYesone list of observations per group; at least 2 groups

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.3.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds meaningful behavioral nuance beyond that: it subtracts chance variance, can yield slightly negative values near zero effect, and explains that this is expected rather than an error. This is valuable interpretive 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?

Three tight sentences, front-loaded with the core definition, then guidance and edge-case explanation. No filler or repetition of schema or annotation details.

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 single-parameter, read-only statistic tool with an output schema present, the description is complete: it states what it calculates, when to use it, how it relates to a sibling, and how to interpret unusual output. No critical context 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?

The input schema fully documents the single 'groups' parameter with a clear description ('one list of observations per group; at least 2 groups'), so the description does not need to add more. The tool description adds contextual meaning by tying it to one-way ANOVA, but the schema carries the parameter semantics burden.

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?

Description clearly identifies the tool as computing an effect size for one-way ANOVA, using a specific verb and resource. It explicitly distinguishes itself from eta_squared by noting it is less biased for small samples, which separates it from a sibling tool.

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 clear context: use alongside one_way_anova and prefer over eta_squared for small samples. It does not enumerate explicit exclusion cases, but the guidance is sufficiently directional for an AI agent to select it appropriately among siblings.

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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