Jean — Molecular Biology & Genomics

gene_set_overrepresentation

Read-only

Over-representation analysis (ORA): which gene sets hold more of a gene list than chance, by the one-sided hypergeometric test against a background, as clusterProfiler's enricher() runs it (enrichit 0.2.6).

Input Schema

Name	Required	Description	Default
`genes`	Yes	the gene list (e.g. the significant genes of a DE run), one ID per line or separated by commas or spaces.
`_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.
`weights`	No	per-gene sampling weights, 'gene weight' per line (e.g. a DESeq2 run's mean normalized counts, `meanCounts`, or gene lengths): replaces the hypergeometric test with Wallenius' noncentral one (BiasedUrn), each set's odds being its members' mean weight over the rest of the background's, so a set of well-counted or long genes is not called for being easy to detect. Background genes without a weight take the median; weights at or below 0 are lifted to the smallest positive one. Empty is the unweighted test.
`_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.
`geneSets`	Yes	the gene sets, as GMT text (MSigDB, GO or WikiPathways exports): one set per line, tab-separated ID, description, then member gene IDs. IDs must be the same kind as the genes'.
`universe`	No	the background: every gene the experiment could have called (e.g. a DE run's tested genes). Empty is every gene any set annotates, clusterProfiler's default.
`maxGSSize`	No	largest set tested, counted within the background.
`minGSSize`	No	smallest set tested, counted within the background.
`pvalueCutoff`	No	a set is enriched when p and adjusted p are at most this.
`qvalueCutoff`	No	and its q-value at most this.
`forceUniverse`	No	use `universe` as given instead of intersecting it with the annotated genes.
`pAdjustMethod`	No	multiple-testing adjustment across the tested sets (R `p.adjust`).	BH

Output Schema

Name	Required	Description
`terms`	Yes	every tested set, by p: count, set size, gene and background ratios, fold enrichment, odds ratio, z-score, p, adjusted p, q-value, the hit genes
`summary`	Yes	input genes, how many are in the background, the background size, sets supplied, in the size range and tested, and the q-value route
`enriched`	Yes	the sets passing the p, adjusted-p and q cutoffs

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 24, 2026

addedInput schema / properties / weights

Added value: +{
+  "default": "",
+  "description": "per-gene sampling weights, 'gene weight' per line (e.g. a DESeq2 run's mean normalized counts, `meanCounts`, or gene lengths): replaces the hypergeometric test with Wallenius' noncentral one (BiasedUrn), each set's odds being its members' mean weight over the rest of the background's, so a set of well-counted or long genes is not called for being easy to detect. Background genes without a weight take the median; weights at or below 0 are lifted to the smallest positive one. Empty is the unweighted test.",
+  "type": "string"
+}

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 and openWorldHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: the exact test (one-sided hypergeometric), the reference implementation semantics (clusterProfiler enricher(), enrichit 0.2.6), and that the Wallenius variant replaces the test when weights are supplied. It does not discuss runtime cost or determinism, but the algorithmic behavior is unusually well disclosed.

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?

A single sentence that front-loads the method name, then the statistic, then the reference implementation — no filler, and the parenthetical citations are compact.

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?

With an output schema present, return values need not be explained, and all 12 parameters are documented in-schema with sensible defaults. What is missing for a tool of this complexity is only the routing advice against the two adjacent gene-set tools, which is a notable but modest gap.

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% and the parameter descriptions are themselves detailed (weights, universe, forceUniverse, size cutoffs). The description only gestures at the background concept ("against a background"), adding nothing the schema does not already say, so the baseline 3 applies.

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?

"Over-representation analysis (ORA): which gene sets hold more of a gene list than chance" names a concrete verb (test for over-representation), the resource (gene sets vs. a gene list) and the exact statistic (one-sided hypergeometric). The method naming implicitly separates it from the rank-based gene_set_enrichment and the competitive gene_set_camera, but no sibling is named or explicitly contrasted.

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?

Use is only implied: the schema's "genes" description mentions "the significant genes of a DE run" and "universe" mentions "a DE run's tested genes", which sketches the workflow. There is no statement of when to prefer this over gene_set_enrichment or gene_set_camera, and no prerequisites or exclusions are given.

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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