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gnomad-genetics-mcp-server

gnomad-genetics-mcp-server: get variant

gnomad_get_variant
Read-onlyIdempotent

Fetch the full gnomAD population record for one or more variants — allele count/number/frequency overall and broken down per genetic-ancestry group, homozygote and hemizygote counts, quality flags, transcript consequence, in-silico predictor scores, and joined ClinVar clinical significance. The "how common, is it benign" answer in one call. Accepts a batch of up to 25 IDs (chrom-pos-ref-alt or rsID) with per-item partial success: a malformed or absent ID lands in failed[] without failing the others. An empty found[] for a well-formed ID means the variant is not in the chosen dataset — pair with gnomad_get_coverage to confirm the position is callable before concluding true absence. Data source: gnomAD (Broad Institute) — https://gnomad.broadinstitute.org/

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetNognomAD dataset: gnomad_r4 (GRCh38, default), gnomad_r3 (GRCh38), gnomad_r2_1 (GRCh37), exac (GRCh37). Echoed in output.
variantsYes1–25 variant IDs (chrom-pos-ref-alt or rsID) to look up in one batched call.
reference_genomeNoReference build. Derived from dataset when omitted (v4/v3=GRCh38, v2.1/ExAC=GRCh37). If supplied it must match the dataset, or the call is rejected. Keep aligned with ensembl coordinates.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
foundNoVariants resolved to a population record.
failedNoPer-item failures: malformed IDs, variants absent from the dataset, or upstream errors.
noticeNoNon-fatal notice when optional ClinVar annotation was unavailable.
datasetNoEffective gnomAD dataset used for the batch.
reference_genomeNoEffective reference build used for the batch.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context beyond these: per-item partial success (malformed/absent IDs land in failed[] without failing others), empty found[] semantics indicating absence in the dataset, and the recommendation to pair with coverage tool. It also mentions the data source. No contradiction with annotations; in fact, it reinforces the read-only nature by explaining non-destructive error handling.

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 comprehensive but not overly verbose. The first sentence front-loads the core purpose. It then covers batch behavior, absence semantics, and pairing guidance in clear, structured sentences. It includes a data source line at the end, which is useful but could be considered optional. Slightly longer than strictly necessary, but every sentence adds value; a strong structure with no filler.

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 the tool's complexity (batch, multiple datasets, reference genomes, partial success), the description covers all necessary operational details: accepted ID formats, dataset-to-genome mapping, error handling, and absence interpretation. Since there is an output schema (presumably documenting return fields), the description doesn't need to explain return values. The inclusion of the data source and the pairing suggestion for coverage completes the picture for correct invocation.

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 100%, with each parameter described. The description adds substantial enrichment: it clarifies the meaning of dataset values (e.g., gnomad_r4 is GRCh38 default), provides concrete variant ID examples (1-55051215-G-GA, rs11591147), explains how reference_genome is derived from dataset and the requirement of coordinate alignment. These details go beyond the schema and help the agent construct correct calls.

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 verb 'Fetch' and the resource 'full gnomAD population record for one or more variants', enumerating the specific data fields (allele counts, homozygote/hemizygote counts, quality flags, transcript consequence, in-silico scores, ClinVar significance). It distinguishes itself from siblings by framing the answer as 'how common, is it benign' and explicitly referencing gnomad_get_coverage for absence confirmation, which sets it apart clearly.

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?

The description provides explicit usage context: 'The ''how common, is it benign'' answer in one call.' It also gives a clear pairing instruction with gnomad_get_coverage for confirming absence, and explains the batch behavior with per-item partial success and failure handling. This effectively tells the agent when to use this tool versus alternatives, including a direct reference to a sibling tool.

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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TDQS

A4.2/5.0
Disambiguation5/5

Every tool has a clearly distinct role: specific variant lookup, gene constraint, coverage, ClinVar search, and canvas SQL describe/query. The closest pair, gnomad_get_variant and gnomad_list_gene_variants, is separated by input type and the depth of data returned.

Naming Consistency4/5

Most tools follow a gnomad_<verb>_<object> pattern like get_coverage, get_variant, list_gene_variants, and search_clinvar. The two canvas helpers break this with gnomad_dataframe_describe and gnomad_dataframe_query, but they form a readable and internally consistent sub-pattern.

Tool Count5/5

Seven tools is well-scoped for a gnomAD/ClinVar genetics server. Each tool addresses a distinct query need, and the two dataframe utilities add real analytic value without bloating the API.

Completeness5/5

The surface covers the core read-only workflows: variant lookup, gene variant listing with filters, constraint, coverage, and ClinVar curation. The dataframe query path prevents dead ends when working with large staged result sets.