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rigor-mcp

rigor-mcp

by mrnh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

benjamini_hochberg_correction

Read-onlyIdempotent

Adjust p-values to control false discovery rate in multiple testing. Use Benjamini-Hochberg correction for less conservative results than Bonferroni.

Instructions

Adjust a batch of p-values for multiple comparisons, controlling the false discovery rate. Less conservative than Bonferroni; the standard choice when testing many hypotheses at once.

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": "benjamini_hochberg_correction"
  }
}

Input Schema

NameRequiredDescriptionDefault
alphaNofalse discovery rate 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: +"false discovery rate 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

A4/5.0
Behavior3/5

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 no additional behavioral detail, which is acceptable given the annotations.

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?

Concise two-sentence description with no redundant information; clearly structured and easy to parse.

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?

Sufficiently complete for the function's simplicity; provides purpose, comparison to alternative, and parameter context without needing output 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?

Schema descriptions cover both parameters fully, and the description does not add extra nuance beyond what is already stated for p_values and alpha.

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?

Clearly states the tool adjusts p-values for multiple comparisons while controlling the false discovery rate, and distinguishes it from Bonferroni as less conservative.

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?

Mentions it is the standard choice for many hypotheses and compares to Bonferroni, though it could be more explicit about when to prefer it over other FDR methods.

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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