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rigor-mcp

rigor-mcp

by mrnh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

cramers_v

Read-onlyIdempotent

Measure the association strength after a chi-square test of independence by converting the chi-square statistic into a normalized effect size from 0 to 1 that is comparable across tables of any shapes.

Instructions

Effect size for a chi-squared test of independence (Cramer, 1946), normalized to [0, 1] regardless of table shape so it's comparable across tables of different sizes, unlike the raw chi-squared statistic. Call after chi_square_independence, passing its statistic and the same table's n/rows/cols. Returns a float in [0, 1]; rough guidance for a 2x2 table: ~0.1 small, ~0.3 medium, ~0.5 large -- the threshold shifts for larger tables.

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": {
      "chi2_statistic": "<chi2_statistic>",
      "n": "<n>",
      "rows": "<rows>",
      "cols": "<cols>"
    },
    "name": "cramers_v"
  }
}

Input Schema

NameRequiredDescriptionDefault
nYestotal number of observations in the table
colsYesnumber of columns in the table
rowsYesnumber of rows in the table
chi2_statisticYesthe chi-squared statistic from chi_square_independence on the same table

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.3.0
    • addedInput schema / properties / chi2_statistic / description
      Added value: +"the chi-squared statistic from chi_square_independence on the same table"
    • addedInput schema / properties / cols / description
      Added value: +"number of columns in the table"
    • addedInput schema / properties / n / description
      Added value: +"total number of observations in the table"
    • addedInput schema / properties / rows / description
      Added value: +"number of rows in the table"
  2. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With annotations already setting readOnlyHint and idempotentHint to true, the description adds valuable behavioral context: it returns a float in [0, 1], provides interpretation thresholds for 2x2 tables, and notes that thresholds shift for larger tables. This exceeds annotation coverage without contradiction.

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 appropriately sized, front-loaded with the definition, followed by usage context and interpretation guidance. Every sentence contributes value without redundancy, earning a top score.

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?

Given the presence of an output schema and comprehensive annotations, the description covers purpose, usage, and interpretation well. It is complete enough for an agent to select and invoke the tool correctly, with minor gaps around edge cases (e.g., zero rows/columns) being non-essential.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the relationship between chi2_statistic and chi_square_independence, and clarifies that rows/cols come from 'the same table,' which helps disambiguate parameter usage beyond the schema's individual descriptions.

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 defines Cramér's V as an effect size for a chi-squared test, normalized to [0, 1] for comparability across table sizes. It uses specific verbs and resources, distinguishing it from the raw chi-squared statistic and aligning well with its role among statistical sibling tools.

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?

Explicitly instructs when to use: 'Call after chi_square_independence, passing its statistic...' and differentiates from raw chi-squared. While it doesn't explicitly list when not to use it or name alternative effect sizes like cohens_d, the guidance is clear and contextual.

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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