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gnomAD MCP Server

gnomAD MCP Server

by koido
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_clinvar_variant_info

Retrieve ClinVar variant information from gnomAD datasets to access clinical significance data for genetic variants.

Instructions

[gnomAD API] Retrieve ClinVar variant info (v2/v3/v4) Args: dataset (str): gnomAD dataset ID (gnomad_r4/gnomad_r3/gnomad_r2_1) reference_genome (str): Reference genome (GRCh37 or GRCh38) variant_id (str): Variant ID Returns: dict: ClinVar info

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset": "<dataset>",
      "reference_genome": "<reference_genome>",
      "variant_id": "<variant_id>"
    },
    "name": "get_clinvar_variant_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
datasetYes
reference_genomeYes
variant_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.py:124-144 (handler)
    The `get_clinvar_variant_info` tool handler, implemented in `server.py` using the FastMCP decorator. It queries the `clinvar_variant` metadata via `run_query_with_metadata`.
    def get_clinvar_variant_info(
    dataset: str,
    reference_genome: str,
    variant_id: str
) -> dict:
    """
    [gnomAD API] Retrieve ClinVar variant info (v2/v3/v4)
    Args:
        dataset (str): gnomAD dataset ID (gnomad_r4/gnomad_r3/gnomad_r2_1)
        reference_genome (str): Reference genome (GRCh37 or GRCh38)
        variant_id (str): Variant ID
    Returns:
        dict: ClinVar info
    """

    variables = {
        'dataset': dataset,
        'reference_genome': reference_genome,
        'variant_id': variant_id,
    }
    return run_query_with_metadata('clinvar_variant', variables)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information (implying read-only), but doesn't cover error handling, rate limits, authentication needs, or what happens with invalid inputs. The return type is mentioned but without detail on structure or potential null responses.

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 efficiently structured with a clear purpose statement followed by parameter and return sections. Every sentence adds value: the first establishes context and scope, while the subsequent lines document inputs and outputs without redundancy.

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

Completeness3/5

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

Given the tool's moderate complexity (3 required parameters, no annotations, but with an output schema), the description is partially complete. It covers parameters and return type at a high level, but lacks behavioral details like error cases or usage examples. The output schema existence reduces the need to fully describe returns, but more operational context would help.

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?

The description explicitly lists all three parameters with brief explanations, compensating for the 0% schema description coverage. It clarifies that 'dataset' corresponds to gnomAD versions, 'reference_genome' specifies GRCh37/GRCh38, and 'variant_id' identifies the variant, adding meaningful context beyond the bare schema.

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?

The description clearly states the tool's purpose: 'Retrieve ClinVar variant info' with specific API context (gnomAD API) and version coverage (v2/v3/v4). It distinguishes itself from siblings like 'get_variant_info' by focusing specifically on ClinVar data, though it doesn't explicitly contrast with all similar tools.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. While the description implies it's for ClinVar-specific variant data, it doesn't explain when to choose this over 'get_variant_info' or other variant-related tools, nor does it mention prerequisites or constraints.

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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