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rigor-mcp

rigor-mcp

by mrnh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

bonferroni_correction

Read-onlyIdempotent

Adjust p-values for multiple comparisons to control family-wise error rate, preventing false positives when any significant result matters.

Instructions

Adjust a batch of p-values for multiple comparisons, controlling the family-wise error rate. Conservative; use when any false positive among the batch is costly.

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": {
      "p_values": "<p_values>"
    },
    "name": "bonferroni_correction"
  }
}

Input Schema

NameRequiredDescriptionDefault
alphaNofamily-wise significance level to control; default 0.05
p_valuesYesthe batch of p-values to adjust

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.3.0
    • addedInput schema / properties / alpha / description
      Added value: +"family-wise significance level to control; default 0.05"
    • addedInput schema / properties / p_values / description
      Added value: +"the batch of p-values to adjust"
  2. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond those: it says the correction is conservative and that it controls the family-wise error rate. This informs the agent about the tradeoff without repeating annotation properties.

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 two sentences long, front-loaded with the main action, and every phrase earns its place. It includes a behavioral caveat without padding, making it an example of efficient, structured documentation.

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?

For a simple two-parameter statistical computation tool with read-only annotations, the description provides enough functional context for the agent to select and invoke it. It could have added explicit mention that the output is adjusted p-values indistinguishable from a list, but the implied result is clear from the tool name and description.

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 coverage is 100%, so the parameters are already well documented in the schema. The tool description reinforces the idea of a batch of p-values and family-wise error rate, but does not add new parameter-specific syntax or edge-case information beyond what the schema 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 identifies the tool's purpose with a specific verb ('adjust') and resource ('a batch of p-values'), and states the goal of controlling the family-wise error rate. It is easy to distinguish this from siblings because it explicitly calls out the conservative Bonferroni correction and the false-positive tradeoff.

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 usage guidance: 'use when any false positive among the batch is costly.' It implies a contrast with less conservative alternatives but does not explicitly name or exclude Benjamini-Hochberg. This is clear context with acceptable room for refinement.

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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