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jean — Molecular Biology & Genomics

discover_reference_stability

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Ask whether the genes used to normalise expression (reference or housekeeping genes, such as GAPDH, ACTB or B2M) are more or less variable across human tissues than genes of the same abundance, on GTEx v8 median expression. Each candidate's standard deviation of log2(TPM + pseudocount) across the chosen tissues is ranked among the genes nearest it in mean expression (1,000 by default, from the 19,107 protein-coding genes GTEx measures), giving one p for 'more variable than its peers' and one for 'steadier', each corrected across the candidates. The four rankers the field runs (geNorm M, NormFinder, BestKeeper, comparative ΔCt, and RefFinder's consensus) also order the candidates against each other, reading the tissues as samples. A ranker always finds a most stable gene; the null says whether any candidate is stable at all. Tissue medians hide the variation between people within a tissue, so this answers 'across tissues', not 'across samples of one tissue'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
alphaNofalse discovery rate at which a candidate is called more variable, or steadier, than its peers.
genesYesthe candidate reference genes, as current HGNC symbols (GAPD is GAPDH).
_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.
tissuesNoGTEx tissue names to compare across, three or more; empty means all 54.
_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.
correctionNoBenjamini–Hochberg (independent or positively dependent tests) or Benjamini–Yekutieli (any dependence; less power). Values: BH (Benjamini–Hochberg), BY (Benjamini–Yekutieli).BH
neighboursNohow many genes nearest in mean expression each candidate is ranked among. The smallest p it can give is 1 / (neighbours + 1).
pseudocountNoadded to TPM before the logarithm, so a tissue with no expression has one. Advanced.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYesthe full result
stableYesthe candidates steadier than their expression peers at the chosen false discovery rate
rankersYesgeNorm M, NormFinder, BestKeeper, comparative ΔCt and the RefFinder consensus over the candidates
unstableYesthe candidates more variable across tissues than their expression peers at the chosen false discovery rate
candidatesYesevery candidate's mean and SD of log2 expression, the expression band of its peers, its percentile among them and both p and q

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

With annotations already declaring readOnlyHint=true, openWorldHint=false, and destructiveHint=false, the description adds substantial methodological context: the ranking procedure, p-value correction, ranker outputs, null interpretation, and the limitation that tissue medians obscure within-tissue variability. It goes well beyond the annotation-level safety profile.

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?

The description front-loads the purpose and then explains method, rankers, null behavior, and scope caveats. It is dense but mostly earns its length; a few methodological details could be tightened, but it is appropriately sized for an 8-parameter statistical discovery tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema is present and the input schema has 100% description coverage, the description need not detail return values or every parameter. It supplies enough conceptual and methodological context for an agent to understand what question the tool answers and when its result is applicable.

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%, so the schema already explains genes, tissues, alpha, correction, neighbours, and pseudocount in detail. The description reinforces concepts like pseudocount and neighbour ranking but does not add parameter-specific semantics beyond what the schema provides, so the baseline of 3 applies.

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 gives a precise verb-plus-resource purpose: it asks whether reference/housekeeping genes are more or less variable across human tissues than abundance-matched genes on GTEx v8 median expression. It also distinguishes the scope from sibling analyses by stating it answers 'across tissues', not 'across samples of one tissue'.

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?

It clearly states the applicable scope and the key limitation: tissue medians hide within-tissue variation, so the tool answers an across-tissue question rather than a within-tissue one. It does not explicitly name or route to a sibling alternative such as rank_reference_genes, so it falls short of full when-to-use/when-not guidance.

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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