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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. Leave one of n, cv, effect, alpha and power out (or name it in solve_for). Every quantity takes a list, and the answer is the whole grid, so depth against replicates is one call. RNASeqPower 1.52.0 (Hart et al. 2013), reproduced: a negative binomial count with Poisson sequencing noise 1/depth plus biological variance CV².

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNobiological replicates in group 1 (and in group 2, unless `n2` is given).
cvNothe 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.).
n2Noreplicates in group 2, one per `n`. Advanced.
cv2Nothe CV within group 2, one per `cv`. Advanced.
alphaNoα, the two-sided false-positive rate per gene.
depthYesaverage 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.
powerNo1 − β, the chance of detecting the change if it is real. No default: the target is yours to choose.
_filesNoThe 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.
effectNothe fold change to detect; 2 and 0.5 are the same change.
_subjectNoThe 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_forNothe 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

TableJSON Schema
NameRequiredDescriptionDefault
gridYesevery design in the grid: each quantity, and the solved one
curveYesthe solved quantity along the first quantity given as a list, one line per value of the second
rCallYesthe R call that reproduces the grid
valueYesthe solved value, when the design is a single cell
solvedYeswhich quantity was solved

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true, destructiveHint=false, and openWorldHint=false. The description adds meaningful behavioral context: the RNASeqPower 1.52.0 method, a negative-binomial count model with Poisson sequencing noise and biological CV², and grid-return behavior, though auth/rate limits are not relevant.

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?

Four dense sentences, front-loaded with the purpose and then the key invocation rules and method. No filler; every clause carries statistical or usage information.

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 complex 11-parameter statistical tool with an output schema and full schema description coverage, the description covers the core computation and the solve-for mode. It leaves the _files/_subject record plumbing and sibling routing to the schema and other tools, which is a minor gap.

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 each parameter is already documented in detail. The description adds the cross-parameter rule for omitting one of n, cv, effect, alpha, or power and the vectorization behavior that the answer is the whole grid, which is useful meaning beyond the individual schema fields.

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 first sentence specifies the exact computation (sample size and power for a two-group RNA-seq comparison, one gene at a time) and names the statistical model, so the agent knows the domain. It does not explicitly name or contrast with the sibling calculate_power, so it is clear but not sibling-differentiating.

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?

The description explains the invocation pattern — leave one of n, cv, effect, alpha or power out, or name it in `solve_for` — and notes that inputs are vectorized into a grid. It does not say when to choose this over calculate_power, estimate_effect_size, or plan_rnaseq_experiment, so alternative selection is left implicit.

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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