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rigor-mcp

rigor-mcp

by mrnh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

one_sample_t_test

Read-onlyIdempotent

Test if a sample's mean differs from a hypothesized value (mu0) using a one-sample t-test. Provides t-statistic, p-value, confidence interval, and assumption checks.

Instructions

Test whether a sample's mean differs from a hypothesized value mu0. Returns the t-statistic, degrees of freedom, two-tailed p-value, a confidence interval for the mean, and any assumption warnings.

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": {
      "data": "<data>",
      "mu0": "<mu0>"
    },
    "name": "one_sample_t_test"
  }
}

Input Schema

NameRequiredDescriptionDefault
mu0Yesthe hypothesized population mean to test the sample against
dataYesthe sample; one number per observation
alphaNosignificance level for the test (and any confidence interval); default 0.05

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.3.0
    • addedInput schema / properties / alpha / description
      Added value: +"significance level for the test (and any confidence interval); default 0.05"
    • addedInput schema / properties / data / description
      Added value: +"the sample; one number per observation"
    • addedInput schema / properties / mu0 / description
      Added value: +"the hypothesized population mean to test the sample against"
  2. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotent/destructive, and the description adds behavioral detail about the return values (t-statistic, df, p-value, CI, assumption warnings), which goes beyond annotations. No 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 a single sentence that front-loads the purpose and lists return values efficiently. No filler words.

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?

All essential information is covered: purpose, key inputs, return values, and safety profile via annotations. No output schema exists, but the description explicitly lists the outputs. Minor omission of specific assumption checks but 'assumption warnings' covers it.

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%, with each parameter (data, mu0, alpha) explained in the schema. The description does not add additional parameter meaning beyond restating mu0 as the hypothesized value, so baseline 3.

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 a sample's mean differs from a hypothesized value mu0, using a specific verb and resource. It distinguishes from siblings like two_sample_t_test and paired_t_test by explicitly saying 'a sample's mean'.

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 implies the use case: testing a single sample against a known value, which differentiates from sibling tools. However, it does not explicitly mention alternatives or when not to use it, so a 4 is appropriate.

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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