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AlphaGenome MCP Server

by taehojo
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TypeScript
Remote

batch_tissue_comparison

Rank multiple genomic variants by predicted effect in each specified tissue. Use it to compare tissue-specific variant impact for research prioritization, not clinical classification.

Instructions

Rank several variants by predicted effect within each of several tissues: one ranking per tissue.

Runs live inference (score_variant with the SDK's recommended variant scorers); works for single-nucleotide variants, indels and multi-nucleotide variants. The result states source: live.

Results are AlphaGenome model predictions for research prioritization, not clinical classifications: scores and calibrated quantiles are reported as returned, and no pathogenic/benign call is made.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tissuesYesTissue names or ontology CURIEs (1-10)
variantsYesVariants to score (1-100)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed13 schema fields changedv0.3.0
    • addedInput schema / properties / tissues / description
      Added value: +"Tissue names or ontology CURIEs (1-10)"
    • removedInput schema / properties / tissues / minItems
      Removed value: -1
    • addedInput schema / properties / variants / description
      Added value: +"Variants to score (1-100)"
    • addedInput schema / properties / variants / items / properties / alt / description
      Added value: +"Alternate allele (A, C, G, T; more than one base for an indel)"
    • addedInput schema / properties / variants / items / properties / alt / pattern
      Added value: +"^[ATGCatgc]+$"
    • addedInput schema / properties / variants / items / properties / chromosome / description
      Added value: +"Chromosome (chr1-chr22, chrX, chrY)"
    • addedInput schema / properties / variants / items / properties / chromosome / pattern
      Added value: +"^chr([1-9]|1[0-9]|2[0-2]|X|Y)$"
    • addedInput schema / properties / variants / items / properties / position / description
      Added value: +"Genomic position (1-based, hg38)"
    • addedInput schema / properties / variants / items / properties / position / minimum
      Added value: +1
    • addedInput schema / properties / variants / items / properties / ref / description
      Added value: +"Reference allele (A, C, G, T; more than one base for an indel)"
    • addedInput schema / properties / variants / items / properties / ref / pattern
      Added value: +"^[ATGCatgc]+$"
    • addedInput schema / properties / variants / items / properties / variant_id
      Added value: +{
+  "description": "Optional: variant identifier (e.g., rs number)",
+  "type": "string"
+}
    • addedInput schema / properties / variants / maxItems
      Added value: +100
  2. First observed

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does substantial work: it discloses that inference is live, that the result carries 'source: live', and that outputs are model predictions for research prioritization rather than pathogenic/benign calls. It omits auth requirements, cost/latency expectations for live scoring, and rate limits, which keeps it from a 5.

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?

Front-loaded with the core operation in the first sentence, followed by scope, execution mode, and a necessary caveat. Every sentence earns its place, though the caveat paragraph is slightly verbose for the amount of information conveyed.

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?

No output schema exists, so the description must convey the return shape, and it does: one ranking per tissue, live source marker, and calibrated quantiles as returned. The only meaningful gap is the absence of any note on response size or runtime for up to 100 variants across 10 tissues in a live-inference call.

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?

Schema description coverage is 100% with only two top-level parameters, so the schema already documents tissues, variants, and every nested allele field. The description adds the implicit pairing semantics (each variant ranked in each tissue) but no formats or constraints beyond the schema. Baseline 3 applies.

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?

States a specific verb and output shape: 'Rank several variants by predicted effect within each of several tissues: one ranking per tissue.' The variants-by-tissues matrix framing clearly distinguishes it from flat siblings like batch_score_variants or compare_variants. It does not name a sibling explicitly, so it stops short of a 5.

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?

It gives input-eligibility guidance ('works for single-nucleotide variants, indels and multi-nucleotide variants'), which is useful, but never says when to choose this over batch_score_variants, predict_tissue_specific, or compare_variants. Usage is implied by the matrix output rather than stated.

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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