jean — Molecular Biology & Genomics

discover_reference_concordance

Read-only

Ask whether a reference-gene stability ranking holds across conditions, and which candidate genes rank well everywhere rather than in one tissue or treatment. Each condition (the Group column of a long-format Cq table) is ranked by geNorm on its own samples; the conditions are then the blocks of a Friedman test and Kendall's W says how far they agree (0 independent orders, 1 the same order everywhere), with an asymptotic χ² p and a Monte Carlo p from shuffling each condition's ranks. Returns every gene's rank in every condition, its mean rank (the meta-ranking) and its best and worst. geNorm cannot order its two most stable genes, so they share rank 1.5 in every condition. Use rank_reference_genes for one condition's ranking by all four algorithms.

Input Schema

TableJSON Schema

Name	Required	Description
`seed`	No	seed for the shuffles. Each permutation draws from its own stream, so one can be re-run alone. Advanced.
`_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.
`_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.
`cq_table`	Yes	the long-format table `Target,Sample,Group,Cq`, one row per measurement; Group is the condition each sample was measured in, and every gene must be measured in every sample.
`permutations`	No	shuffles of the ranks within each condition. The smallest p is 1 / (permutations + 1).

Output Schema

TableJSON Schema

Name	Required	Description
`p`	Yes	Monte Carlo p of W against within-condition shuffles
`w`	Yes	Kendall's coefficient of concordance across conditions
`genes`	Yes	every gene, most stable on average first: its rank in each condition, mean rank, best and worst
`result`	Yes	the full result
`nullModel`	Yes	the null, stated with every run
`conditions`	Yes	each condition's geNorm order with M

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the readOnly/non-destructive annotations, the description discloses substantial behavioral traits: per-condition geNorm ranking, Friedman blocking, Kendall's W interpretation (0 independent vs 1 identical), both an asymptotic X² p and a Monte Carlo p, and the geNorm tie behavior (two most stable genes share rank 1.5). These are non-obvious semantics an agent could not get from annotations or schema.

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 is long and method-dense, but it is front-loaded with the core question and each statistical clause (Friedman, Kendall's W, asymptotics, Monte Carlo) earns its place by explaining output interpretation. Slightly heavy for its size, but not padded.

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?

With an output schema, full parameter coverage and read-only annotations, everything an agent needs is present: the input format, the statistical procedure, the output semantics, and the routing alternative. Nothing material is missing.

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 adds real meaning beyond the schema — it clarifies that the Group column of the long-format Cq table defines each condition, that every gene must be measured in every sample, and that the smallest achievable permutation p is 1/(permutations+1). It goes past restating schema fields.

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 specific verb and resource — testing whether a reference-gene stability ranking holds across conditions — and immediately distinguishes itself from the sibling rank_reference_genes by scoping (across conditions vs one condition). An agent can tell what this tool does and how it differs without opening either schema.

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

Usage Guidelines5/5

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

It explicitly names the alternative tool (`rank_reference_genes` for one condition's ranking by all four algorithms) and the condition that selects this tool instead (asking whether ranks agree across conditions). The when-to-use routing is spelled out, not left to inference.

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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