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get_variant

Retrieve detailed information on a genetic variant including clinical significance, predictor scores, gene constraint, disease associations, and AI-generated mechanistic interpretation.

Instructions

Get comprehensive information about a specific genetic variant.

Returns clinical significance, model-derived scores from EVEE's heads (aligned to AlphaMissense, CADD, REVEL, SIFT, etc.), reference predictor scores from external databases when present, gene constraint (LOEUF), HGVS notation, disease associations, protein domains, and the AI-generated mechanistic interpretation.

If the stored interpretation isn't ready, this tool hits EVEE's on-demand /analysis endpoint once: if generation has already completed, the fresh interpretation is returned inline; otherwise the response carries an interpretation = {status: queued/processing, detail: ...} entry and you should call wait_for_variant_analysis to poll until it finishes.

Args: variant_id: Variant identifier in chr:pos:ref:alt format (e.g. "chr17:43092918:G:A" for BRCA1 ClinVar ID 41812). NOTE: EVEE uses 0-based positions; ClinVar/VCF/HGVS are 1-based. Subtract 1 from ClinVar pos for SNVs; for indels the offset varies — prefer search_variants.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
variant_idYes
Behavior5/5

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

With no annotations, the description fully discloses behavior: hitting an on-demand endpoint, returning status if analysis not ready, and suggesting polling. Also explains coordinate system differences, which is critical for correct usage.

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 well-structured and front-loaded, but slightly verbose with some long sentences. However, all content is necessary given the tool's complexity, earning its space.

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

Completeness5/5

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

Given no output schema, the description covers all essential aspects: return values, conditional behavior, input format, and edge cases. It is complete and preempts common questions.

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 provides extensive details: format (chr:pos:ref:alt), example, and a crucial note about 0-based vs 1-based positions. This adds significant meaning beyond the simple string type.

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 clearly states the tool gets comprehensive information about a genetic variant and lists specific attributes. It distinguishes from siblings by mentioning wait_for_variant_analysis and search_variants for alternative use cases.

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

Usage Guidelines5/5

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

Explicitly says when to use this tool and when to use alternatives: if interpretation is queued, call wait_for_variant_analysis; for indels, prefer search_variants. Provides clear context for decision-making.

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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