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jean — Molecular Biology & Genomics

validate_gene_trait

Read-only

Put a hypothesis of the form "gene G is involved in trait T" through POPPER-style sequential falsification on two public tables, each test fixed in advance and run in a fixed order. (1) GTEx v8 expression: either G's co-expression across the 54 tissues with the trait's own gene(s), such as IL2 for IL-2 production, or G's expression in named tissues against its own elsewhere. (2) The GWAS Catalog: how many studies place an association inside G for traits in the named GWAS Catalog categories. Each p is G's rank among the 19,107 protein-coding genes GTEx measures, so it is exact under the null that G is just another gene, with no model of how expression or GWAS hits are distributed. Each p becomes an e-value (κ calibrator), the running product is the evidence, and testing stops at the first product above 1/α, which is POPPER's own sequential stop. Benchmarked on POPPER's TargetVal (Schmidt et al. 2022 CRISPR screens): it holds Type-I below α and has far less power than an agent writing its own tests, because two tables answer fewer questions; the result says which tests were run and what each could and could not show.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geneYesthe hypothesis gene, as its current HGNC symbol (protein-coding).
alphaNothe level the sequential test stops and decides at: supported when the product of e-values exceeds 1/α.
kappaNoκ of the p-to-e calibrator e = κ·p^(κ−1). Advanced.
_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.
tissuesNotissue test: GTEx tissue names where involvement implies higher expression, such as Whole Blood or Spleen.
_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.
categoriesYesGWAS Catalog parent categories an involved gene's associations should fall in, such as Immune system disorder.
trait_genesNoco-expression: the trait's own gene(s) as HGNC symbols, such as IL2 for IL-2 production.
expression_testNowhich GTEx test: co-expression with the trait's own gene(s), or expression in named tissues against the gene's own elsewhere. One only, because both read GTEx and one table is one piece of evidence. Values: coexpression (Co-expression with the trait's gene), tissue (Expression in named tissues).coexpression

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
eYesthe stopped product of e-values: the evidence against "G is just another gene"
testsYesevery falsification test in its registered order: what it reads, its statistic, G's value, its rank among the genes and its p, and whether it was run before the stop
resultYesthe full result
nullRateYesthese settings' own Type-I rate: how many of the universe's genes, each taken as the hypothesis, the same test would support
supportedYeswhether the stopped product of e-values exceeds 1/α

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already establish the safety profile (readOnlyHint=true, openWorldHint=false, destructiveHint=false), and the description goes well beyond them: it discloses the fixed test order, exact rank-based p-values under a null, the p-to-e calibration and sequential stopping rule (product > 1/α), Type-I control, and the power caveat versus custom tests. It stops short of operational details such as failure modes or cost, but the behavioral disclosure is rich.

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

Conciseness3/5

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

Purpose is front-loaded, but the body is a single dense block of parenthetical-heavy statistical prose — including TargetVal benchmarking and power comparison — that is hard to parse and partially redundant with the schema. The content is not wasted, but the structure is less scannable than it could 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 a statistically complex tool with an output schema already present and 100% parameter description coverage, the description supplies the surrounding method context (two tables, sequential falsification, what the result reports) that an agent needs to interpret the call. It is largely complete, with only sibling routing and any prerequisite data assumptions left unstated.

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 documents every parameter including alpha, kappa, expression_test, tissues, trait_genes and categories. The description reinforces the meaning of alpha (stop at 1/α) and kappa (the calibrator), but adds little syntax or format detail the schema does not already carry, 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?

The description gives a specific verb and resource — putting a gene-trait hypothesis through POPPER-style sequential falsification on GTEx v8 expression and the GWAS Catalog — so the tool's function is unambiguous. It does not, however, distinguish itself from conceptually adjacent siblings such as discover_target_validation or rank_next_tests, so an agent must infer positioning on its own.

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?

Usage is implied: this is for testing 'gene G is involved in trait T' when GTEx co-expression/tissue and GWAS category evidence are the relevant tables. The description does contrast it weakly with 'an agent writing its own tests' (fewer questions answered, lower power), but there is no explicit when-to-use / when-not-to-use or named alternative tool, leaving routing 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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