Jean by Melad Labs

genotype_pca

Read-only

Principal components of a call set's genotypes, the population-structure picture: biallelic sites above a minor-allele count, LD-pruned by sliding windows, scaled per site (Patterson, standard or centring) and decomposed; every sample's coordinates with its population, and the variance each component explains (scikit-allel pca). Validated on 1000 Genomes phase 3 APOL1, all 2,504 samples.

Input Schema

Name	Required	Description	Default
`vcf`	Yes	the call set as VCF text (a .vcf, or a .vcf.gz already decompressed); every record's GT is read, ploidy 2.
`prune`	No	drop sites in LD before the decomposition (locate_unlinked), as scikit-allel's PCA assumes.
`_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.
`minMac`	No	keep biallelic sites whose REF and first ALT are each seen at least this many times.
`scaler`	No	per-site scaling before the decomposition: Patterson divides the centred dosage by √(p(1−p)); standard by its standard deviation; center only centres. Values: patterson (Patterson (√(p(1−p)))), standard (Standard deviation), center (Centre only).	patterson
`groupBy`	No	which column of the sample table groups the samples; empty is its first grouping column.
`_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.
`precision`	No	float64, or float16: scikit-allel's own path when handed integer genotypes, which rounds the scaled matrix to half precision. Values: float64 (Double precision), float16 (Half precision (as scikit-allel on integer genotypes)).	float64
`pruneSize`	No	pruning window, in sites. Only applies when prune is true.
`pruneStep`	No	sites the pruning window advances. Only applies when prune is true.
`nComponents`	No	components to report.
`populations`	No	a sample table: a header row with a `sample` column and one or more grouping columns (tab-separated, as 1000 Genomes' .panel). Samples it does not list belong to no group.
`pruneThreshold`	No	prune the later site of a pair whose Rogers–Huff r² exceeds this. Only applies when prune is true.

Output Schema

Name	Required	Description
`sites`	Yes	how many sites qualified, how many pruning kept, and their positions
`coords`	Yes	one row per sample: sample, its group, and its coordinate on each component
`explainedVariance`	Yes	the variance each reported component explains, in the data's own units (the eigenvalues)
`explainedVarianceRatio`	Yes	the share of variance each reported component explains

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 21, 2026

addedOutput schema / properties / explainedVariance

Added value: +{
+  "description": "the variance each reported component explains, in the data's own units (the eigenvalues)"
+}

changedOutput schema / required

Previous value: -[
-  "coords",
-  "explainedVarianceRatio",
-  "sites"
-]New value: +[
+  "coords",
+  "explainedVarianceRatio",
+  "explainedVariance",
+  "sites"
+]

First observedOct 20, 2026

TDQS

A3.5/5.0

Behavior4/5

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

Annotations only cover the safety profile (readOnly, non-destructive, closed-world), leaving the description to add substance – and it does: it discloses the full processing chain, the default LD-pruning behaviour and its assumptions, the scaling variants, and even validation against 1000 Genomes phase 3 (2,504 samples). It also previews the returned content (per-sample coordinates with population and variance explained), which the annotations do not.

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?

Two sentences, method first and validation second, with the core operation front-loaded. The long first sentence is dense but earns its place by summarizing the whole pipeline; there is mild redundancy with the schema's parameter text.

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 13-parameter compute tool with an output schema and full annotation coverage, the description is nearly complete – it conveys method, defaults, assumptions and output shape. The only real gap is sibling routing, which is the missing piece an agent needs to pick this over related population-genetics tools.

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 all 13 parameters in depth (defaults, enums, min/max, scikit-allel rationale). The description restates the same concepts (minor-allele count, LD pruning, scaling) at a high level without adding format, syntax or interaction detail beyond the schema. Baseline 3 is appropriate.

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 states a specific verb and resource – principal components of a call set's genotypes, decomposed via scikit-allel PCA – and gives the pipeline (biallelic filtering, LD pruning, per-site scaling). It is unmistakably a dimensionality-reduction / population-structure tool. It does not, however, name or contrast itself with close siblings such as population_ancestry, population_fst or population_site_stats.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named despite several overlapping population-genetics siblings. The methodological detail implies the context (PCA for population structure) but the agent must infer when this beats population_ancestry or population_fst.

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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