Jean — Molecular Biology & Genomics

calculate_rnaseq_power

Read-only

Sample size and power for an RNA-seq comparison of two groups, one gene at a time: the biological replicates per group needed to detect a fold change at a given sequencing depth and within-group coefficient of variation, or the power, detectable fold change, CV or α a fixed design gives.

Input Schema

Name	Required	Description
`n`	No	biological replicates in group 1 (and in group 2, unless `n2` is given).
`cv`	No	the biological coefficient of variation within a group: 0.4 or less for most genes in human studies, about 0.1 in inbred animals (Hart et al.).
`n2`	No	replicates in group 2, one per `n`. Advanced.
`cv2`	No	the CV within group 2, one per `cv`. Advanced.
`alpha`	No	α, the two-sided false-positive rate per gene.
`depth`	Yes	average depth of coverage of the gene: its expected read count per sample. Hart et al. found most genes at 0.1 per million mapped reads, so 4 at 40 million.
`power`	No	1 − β, the chance of detecting the change if it is real. No default: the target is yours to choose.
`_files`	No	The project: record references shaped like _subject. Their records become library documents a tool names in sequences/parts/document; a trace or alignment is found by file name.
`effect`	No	the fold change to detect; 2 and 0.5 are the same change.
`_subject`	No	The record to act on: exactly one of sequence, accession (+source), sample (an id from list_samples) or file ({name, text} or {name, base64}); record_index picks one record of several.
`solve_for`	No	the quantity to solve. Left unset, it is the one of n, cv, effect, alpha and power that was not given. Values: n (n per group), cv (CV), effect (fold change), alpha (α), power (power).

Output Schema

Name	Required	Description
`grid`	Yes	every design in the grid: each quantity, and the solved one
`curve`	Yes	the solved quantity along the first quantity given as a list, one line per value of the second
`rCall`	Yes	the R call that reproduces the grid
`value`	Yes	the solved value, when the design is a single cell
`solved`	Yes	which quantity was solved

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context beyond that: the computation is per-gene and analytical (no count simulation), and it is explicitly a two-group comparison, which shapes how an agent should interpret and report results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence that front-loads the core purpose before the two solve modes. Every clause carries information, though the long colon-delimited construction is harder to parse than a short lead sentence followed by a mode breakdown would be.

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 an 11-parameter statistical tool with nested objects and an output schema, the description supplies the conceptual model an agent needs to choose inputs and interpret what is being solved. Return values need not be described because an output schema exists, and the specialized project-context parameters are covered by the schema itself.

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 the baseline is 3, but the description goes further by expressing the model relationship among depth, within-group CV, fold change, α, and replicates/power — explaining how the parameters are jointly constrained rather than listed independently. It omits any mention of the project-binding parameters (_files, _subject) but those are documented in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource (sample size and power for an RNA-seq two-group comparison) and scopes it precisely with 'one gene at a time', which separates it from a whole-transcriptome simulation. It does not, however, explicitly name the closest siblings (simulate_rnaseq_power, calculate_power, plan_rnaseq_experiment), so the distinction must be inferred.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Two usage modes are implied — solve for replicates given depth/CV/effect, or solve for power/effect/CV/α given a fixed design — but there is no explicit 'use this when…, use simulate_rnaseq_power or calculate_power when…' routing. An agent must read the solve_for enum in the schema to understand the branching, so guidance is implied rather than stated.

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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