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Generate Precision Medicine Variant Report

generate_variant_clinical_report
Read-onlyIdempotent

Cross-reference seven biomedical databases to generate a structured variant interpretation report for any HGVS variant, including ACMG/AMP criteria and an actionability statement for research.

Instructions

Generate a multi-source variant interpretation report.

Cross-references evidence from up to seven upstream databases for a single HGVS variant into one structured report. The report is a research aid: it surfaces the upstream evidence and the ACMG/AMP criteria that the available evidence supports, but it is not a clinical interpretation and must not be used as a diagnostic without independent review by a qualified clinical laboratory.

  1. Ensembl VEP — functional consequence, SIFT/PolyPhen/CADD predictions

  2. ClinVar — clinical pathogenicity classifications and review status

  3. gnomAD v4 — population allele frequencies (gnomAD v4, ~807k individuals)

  4. AlphaMissense — deep-learning missense pathogenicity (Cheng et al. 2023)

  5. Open Targets — disease-gene evidence scores

  6. DisGeNET — curated gene-disease association scores

  7. ChEMBL — approved drugs acting on the gene product

The report includes a draft ACMG/AMP criteria checklist with evidence mapping, a structural impact summary, and an actionability statement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already indicate read-only and idempotent behavior, and the description adds valuable detail on report content, data sources, and the non-clinical nature. No contradictions.

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 a clear opening sentence, important disclaimer, and organized numbered list. Slightly lengthy but every sentence adds value.

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 the tool's complexity and the presence of an output schema, the description explains the report contents, data sources, and warnings adequately. Could mention output format briefly but acceptable.

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 does not elaborate on parameters beyond the input schema, which already provides clear descriptions for all three parameters. Baseline of 3 is appropriate as schema coverage is thorough.

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?

Clearly states the tool generates a multi-source variant interpretation report, lists the seven databases cross-referenced, and distinguishes from siblings like classify_variant_acmg which focuses on ACMG classification alone.

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?

Provides context that the report is a research aid and not for clinical use without review, and lists the databases included. Does not explicitly state when to use alternatives, but the detailed database list implies when this comprehensive tool is appropriate.

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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