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count_variants

Count variants matching filters by genomic region, minor-allele frequency, and missing data rate to size a query before downloading data.

Instructions

Count variants matching filters, computed server-side (nothing is downloaded).

Fast way to size a query before pulling data. Filter by genomic region (reference_name + optional start/end, from list_sequences), minor- allele frequency (min_maf/max_maf) and/or max_missing_data (0–1 fraction). With no filters this returns the total variant count of the set. variant_set_db_id is a BrAPI variantSetDbId (from list_variant_sets / list_content).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoRegion end position, 1-based inclusive.
startNoRegion start position, 1-based inclusive.
max_mafNoMaximum minor-allele frequency (0-1).
min_mafNoMinimum minor-allele frequency (0-1).
reference_nameNoChromosome/contig name to restrict the search to (see list_sequences).
max_missing_dataNoMaximum per-variant missing-data fraction (0-1).
variant_set_db_idYesBrAPI variantSetDbId identifying the run (MODULE§project§run); from list_variant_sets / list_content.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses server-side computation and no download. Also notes default behavior (total count without filters). Lacks explicit read-only safety statement but adequate.

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, no redundancy. Front-loaded with purpose. Could be slightly shorter but no waste.

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?

Given output schema exists, description doesn't need to cover returns. Input schema well-covered. Lacks performance notes but tool straightforward.

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%, baseline 3. Description adds meaning by explaining filters (genomic region, MAF, max_missing_data) and variant_set_db_id. Also clarifies behavior with no filters.

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?

Description clearly states 'Count variants matching filters' and emphasizes 'computed server-side (nothing is downloaded).' This distinguishes from siblings like search_variants, which likely return actual variant data.

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?

Explicitly says 'Fast way to size a query before pulling data,' providing clear guidance on when to use. Does not mention when not to use, but context is clear.

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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