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VCF meta-info

vcf_metainfo
Read-onlyIdempotent

Determine a VCF file's genome build, variant input type (WGS/array/gVCF), and inferred genetic ancestry to guide PRS scoring. Use this to validate inputs before computing polygenic risk scores.

Instructions

Detect a genome's build, input type (WGS/array/gVCF), and genetic ancestry.

One call that pairs WGS-vs-array/gVCF detection with population (super-population) inference, so before scoring you already know: the genome build (read from the VCF header), whether the input is whole-genome sequencing, a consumer genotyping array (and which chip), or a gVCF / all-sites callset, and the sample's inferred super-population.

Pass genotypes_path (a normalized Parquet from normalize_vcf / download_sample_genome) for the fast path, or a raw vcf_path. Genome-build detection reads the VCF header, so it is only available on the vcf_path branch.

The result carries ready-to-use recommendations: recommended_reference_restoration (feed to compute_prs / compute_prs_batch / compute_prs_by_trait as reference_restoration — though those tools default to "auto" and derive the same thing) and recommended_superpopulation (feed as superpopulation). Set infer_population=False to skip ancestry inference (it pulls a small model from HuggingFace on first use).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
panelNo
vcf_pathNo
resolutionNosuperpop
genome_buildNo
genotypes_pathNo
infer_populationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesGenotype source inspected (VCF or normalized Parquet).
chip_idNoDetected genotyping-array chip id (array input only).
is_gvcfNoTrue when reference-block / all-sites (gVCF) records were detected.
summaryYesHuman-readable summary of the detected properties.
warningsNoNon-fatal caveats (e.g. ancestry inference skipped).
input_typeYesDetected input kind: 'wgs', 'genotyping_array', or 'unknown'.
n_variantsYesNumber of variant rows in the genotype source.
marker_countYesVariant/marker count used for the input-type call.
chip_platformNoDetected chip platform label (array input only).
ancestry_errorNoWhy ancestry inference was skipped/failed, if it was.
ancestry_panelNoReference panel used for ancestry inference (e.g. '1000g').
fine_populationNoFiner population call when resolution='population'.
superpopulationNoInferred super-population (AFR/AMR/EAS/EUR/SAS), or null if not inferred.
ancestry_coverageNoFraction of the ancestry model's sites covered by the sample.
ancestry_confidenceNoClassifier confidence for the super-population call (0-1).
genotype_input_modeYesResolved genotype interpretation: 'variant_only', 'all_sites', or 'plink_present_only'.
chip_generation_labelNoDetected chip generation label (array input only).
detected_genome_buildNoGenome build read from the VCF header ('GRCh37'/'GRCh38'), or null.
ancestry_probabilitiesNoPer-super-population classifier posteriors.
n_variants_ancestry_usedNoNumber of sample variants used by the ancestry model.
recommended_superpopulationNoSuper-population to pass to percentile / compute_prs_by_trait, when inferred.
recommended_reference_restorationYesScope to pass as compute_prs* reference_restoration: 'wgs', a chip id, or 'off'.
Install Server

TDQS

A4.1/5.0
Behavior4/5

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

The description adds meaningful behavior beyond the read-only/idempotent annotations: it reveals dependency on a HuggingFace model on first ancestry inference, explains that build detection only works on the vcf_path branch, and clarifies that the returned reference_restoration recommendation is already derived by downstream tools in their default mode. No contradiction with annotations exists.

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 structured and front-loaded with a clear, high-level purpose. It is somewhat long and repeats the output/consumption idea several times, but the detail is largely relevant, especially for distinguishing fast-path vs vcf-only behavior and for explaining external model usage.

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 reasonably complex metadata tool with an output schema and no required parameters, the description covers the key invocation options, important branch constraints, side effects, and downstream consuming tools. Missing parameter semantics for three fields and some resource/cost caveats prevent a perfect score.

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?

The description compensates for a 0% schema-description coverage by explaining genotypes_path, vcf_path, infer_population, and the recommended output fields. However, panel, resolution, and genome_build remain unclear as parameters, and the reader must infer their role from general context. This is useful but incomplete parameter-level guidance.

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 opens with a precise verb and resource: 'Detect a genome's build, input type (WGS/array/gVCF), and genetic ancestry.' It clearly differentiates this tool as a metadata-inference step rather than a file-normalization or PRS-scoring tool, making its purpose unambiguous against the sibling tools.

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?

The description gives concrete usage guidance: pass a normalized Parquet from normalize_vcf/download_sample_genome for the fast path, or a raw vcf_path when VCF-header build detection is needed. It also explains when to set infer_population=False and how the recommendations feed downstream compute_prs tools. It does not explicitly name alternative tools or state when not to use it, but the context is strong.

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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