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rigor-mcp

rigor-mcp

by mrnh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

eta_squared

Read-onlyIdempotent

Computes effect size for one-way ANOVA: proportion of total variance explained by group membership. Use after ANOVA to quantify how much group differences account for variability.

Instructions

Effect size for a one-way ANOVA: proportion of total variance explained by group membership. Use alongside one_way_anova, which tells you whether groups differ but not how much of the variance that accounts for. Rough guidance: ~0.01 small, ~0.06 medium, ~0.14 large. Biased upward for small samples -- prefer omega_squared when that matters. Returns a float in [0, 1].

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": "eta_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

A4.3/5.0
Behavior4/5

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

The description warns that the statistic is biased upward for small samples, which is a behavioral trait not covered by annotations. It also specifies the output range [0,1]. However, it does not discuss edge cases such as missing data or unbalanced group sizes, so transparency is not fully exhaustive.

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 concise and well-structured: it states the definition, usage guidance, interpretation thresholds, a caution about bias, and the return type, all in a compact multi-sentence format with no superfluous content.

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 simple statistical function, the description covers all necessary context: definition, interpretation, relationship to other tests, a caveat about bias, and the output type. It provides sufficient information for an agent to understand the tool's role and limitations without needing additional details.

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 provides a detailed description for the 'groups' parameter, including the requirement of at least two groups and the structure of one list per group. The tool description adds no further parameter-specific information, so the baseline score of 3 for high schema coverage 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?

The description clearly states that this tool computes the effect size for a one-way ANOVA as the proportion of total variance explained by group membership. It explicitly differentiates from one_way_anova, which tests for group differences but does not quantify variance explained, making the purpose unambiguous.

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 advises using this tool alongside one_way_anova and notes a preference for omega_squared in small samples due to upward bias. This provides concrete guidance on when to use the tool and when to consider an alternative, though it could be more explicit about the exact conditions for preferring omega_squared.

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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