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gnomad-genetics-mcp-server

by io.github.cyanheads

gnomad-genetics-mcp-server: get coverage

gnomad_get_coverage
Read-onlyIdempotent

Fetch gnomAD sequencing-coverage summary across a gene, transcript, or region — mean and median read depth, plus the mean fraction of samples covered at each depth threshold (1× through 100×), separated by exome and genome track. Use this to disambiguate a true absent variant from an uncallable position: a variant missing from a well-covered region is informative, while one missing from a poorly-covered region is not. Supply exactly one of gene, transcript_id, or region. The optional coverage_source narrows to one track; by default both available tracks are returned. Echoes the effective dataset and build. Data source: gnomAD (Broad Institute) — https://gnomad.broadinstitute.org/

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geneNoGene — HGNC symbol (e.g. PCSK9) or Ensembl gene ID (e.g. ENSG00000169174). Obtain a stable ID from ensembl_lookup_gene.
regionNoGenomic region chrom-start-stop (1-based inclusive, e.g. 1-55039447-55064852). Mutually exclusive with gene and transcript_id.
datasetNognomAD dataset: gnomad_r4 (GRCh38, default), gnomad_r3 (GRCh38), gnomad_r2_1 (GRCh37), exac (GRCh37). Echoed in output.
transcript_idNoEnsembl transcript ID (e.g. ENST00000302118). Mutually exclusive with gene and region.
coverage_sourceNoRestrict to one coverage track. Omit to return every available track.
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.
noticeNoGuidance when no coverage data is available for the target.
targetNoThe resolved target (gene symbol/ID, transcript ID, or region) the coverage describes.
datasetNoEffective gnomAD dataset.
summariesNoPer-track coverage summaries (exome and/or genome).
target_kindNoWhich target type was queried.
reference_genomeNoEffective reference build.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent behavior; the description adds materially useful behavioral detail beyond that: the output shape (depth stats, threshold fractions, exome/genome separation), the default to return all available tracks, and the behavior of echoing the effective dataset and build. It does not cover failure modes or edge-case behavior, but it does not contradict the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but efficient, front-loading the output content and purpose in the first sentence, then giving the use case, cardinality rule, and default behavior. The brief data-source attribution is earned for provenance. Every sentence contributes to correct selection or invocation.

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 a rich 100%-covered input schema, an output schema, and read-only/idempotent annotations, the description covers the essential non-schema guidance: what the output means, why an agent would call this tool, the selector cardinality, and the default track behavior. Nothing critical is missing for correct invocation.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds real value by clarifying the exact-one-of constraint across gene/transcript_id/region, which the schema only expresses through mutual-exclusion pair notes. It also reinforces the coverage_source default behavior. It does not add deep syntax guidance for each parameter, but the schema already handles that.

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 anchors on a specific, unambiguous operation: fetching gnomAD sequencing-coverage summaries. It enumerates the resource types (gene, transcript, region) and specifies exactly what the result contains (mean/median depth, fraction at 1×–100× thresholds, exome/genome tracks). This clearly separates it from sibling variant and dataframe tools even without naming them.

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?

It gives an explicit, actionable use case: disambiguating true absent variants from uncallable positions, with a clear explanation of why well-covered versus poorly-covered regions matter. It also instructs the caller to supply exactly one of gene, transcript_id, or region. It stops short of explicitly naming sibling alternatives or stating when not to use this tool, so it misses a 5.

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.

Resources