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get_variant_disruptions

Identifies the top biological annotation disruptions for a genetic variant, ranking molecular features by magnitude of change to explain pathogenic or benign effects.

Instructions

Get the top biological annotation disruptions for a variant.

Shows which molecular features are most affected by the variant, ranked by magnitude of change. Each disruption shows what the Evo 2 model predicts for the reference vs. alternate allele across 325 biological annotations spanning protein structure, chromatin state, regulatory elements, splice sites, and more.

This is the key tool for understanding WHY a variant is predicted pathogenic or benign — e.g., a splice-site variant might show large disruptions in splice donor/acceptor annotations, while a missense variant might show disruptions in protein domain and secondary structure annotations.

Categories: amino_acid, atacseq, ccre, chipseq, chromhmm, elm, fstack, protein_feature, interpro, genomic_feature, ptm, region, secondary_structure.

Args: variant_id: Variant identifier in chr:pos:ref:alt format. top_n: Number of top disruptions to return (default 15, max 100). category: Optional category filter — restrict ranking to one category (e.g. to see only splice-related disruptions: category='genomic_feature').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
variant_idYes
top_nNo
categoryNo
Behavior4/5

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

With no annotations, the description carries full burden and does well: discloses it returns ranked disruptions from Evo 2 model across 325 annotations, mentions categories. However, it does not address error behavior (e.g., invalid variant_id) or provide details about output structure, but overall it is transparent about what the tool does.

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?

Well-structured with clear sections: purpose, explanation, categories list, and parameters. Slightly verbose with the categories list and example, but all information is useful. The front-loaded sentence effectively states the action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While param descriptions are solid and the output concept is explained, the absence of an output schema means the description should ideally detail the returned fields or format. It only mentions 'shows which molecular features are most affected' which is vague. Could be more complete for a tool that returns structured data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description fully compensates: specifies variant_id format, top_n default and max, and category filter with an example. This adds essential meaning beyond the bare schema.

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?

Clearly states it retrieves top biological annotation disruptions for a variant. The explanation of ranking and categories adds clarity. However, it does not explicitly differentiate from sibling tools like get_variant_annotations, which might also provide disruption info, so purpose is clear but not fully distinguished.

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?

Provides context that this is the key tool for understanding pathogenicity, and includes example usage with category filter. However, it does not specify when not to use it or compare it directly to sibling tools, leaving some ambiguity about choosing among similar tools.

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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