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shukwong

gnomAD MCP Server

by shukwong

get_mitochondrial_variants

Retrieve mitochondrial DNA variants from gnomAD to analyze genetic variations in mitochondrial genomes for research and clinical applications.

Instructions

Get mitochondrial variants

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetNoDataset IDgnomad_r3

Implementation Reference

  • Handler logic for the get_mitochondrial_variants tool. It parses the dataset ID, makes a GraphQL request using the predefined mitochondrial variants query, and formats the result.
    case "get_mitochondrial_variants":
      result = await makeGraphQLRequest(QUERIES.getMitochondrialVariants, {
        datasetId: parseDatasetId((args.dataset as string) || "gnomad_r3"),
      });
      formattedResult = result.data?.mitochondrial_variants || [];
      break;
  • src/index.ts:617-630 (registration)
    Tool registration in the ListTools response, defining the name, description, and input schema for get_mitochondrial_variants.
    {
      name: "get_mitochondrial_variants",
      description: "Get mitochondrial variants",
      inputSchema: {
        type: "object",
        properties: {
          dataset: {
            type: "string",
            description: "Dataset ID",
            default: "gnomad_r3",
          },
        },
      },
    },
  • GraphQL query schema definition for retrieving mitochondrial variants, referenced as QUERIES.getMitochondrialVariants in the handler.
    getMitochondrialVariants: `
      query GetMitochondrialVariants($datasetId: DatasetId!) {
        mitochondrial_variants(dataset: $datasetId) {
          variant_id
          pos
          ref
          alt
          rsids
          ac_het
          ac_hom
          an
          af_het
          af_hom
          max_heteroplasmy
          filters
        }
      }
    `,
  • Helper function to parse and validate the dataset ID, used in the handler for get_mitochondrial_variants.
    function parseDatasetId(dataset: string): string {
      const validDatasets = [
        "gnomad_r2_1",
        "gnomad_r3",
        "gnomad_r4",
        "gnomad_sv_r2_1",
        "gnomad_sv_r4",
        "gnomad_cnv_r4",
        "exac",
      ];
      
      const datasetLower = dataset.toLowerCase();
      if (!validDatasets.includes(datasetLower)) {
        return "gnomad_r4"; // Default to latest version
      }
      return datasetLower;
    }
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it only states 'Get mitochondrial variants', which doesn't reveal any behavioral traits such as whether this is a read-only operation, potential side effects, authentication needs, rate limits, or what the return format might be. This leaves the agent with insufficient information to understand how the tool behaves.

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 extremely concise with just three words, 'Get mitochondrial variants', which is front-loaded and wastes no space. Every word earns its place by conveying the core action and target, though it lacks depth. This minimalism is efficient but comes at the cost of completeness.

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

Completeness2/5

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

Given the complexity of retrieving genetic variants, the absence of annotations and output schema, and the presence of sibling tools, the description is incomplete. It doesn't explain what 'mitochondrial variants' entails, how results are returned, or differentiate from other variant tools. For a tool with no structured support and potential nuances in genomics, more context is needed to be fully helpful.

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 input schema has 1 parameter with 100% description coverage ('Dataset ID'), so the schema already documents it adequately. The description doesn't add any meaning beyond what the schema provides, as it mentions no parameters. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't need to compensate but also doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get mitochondrial variants' is essentially a tautology that restates the tool name with minimal elaboration. While it indicates the tool retrieves mitochondrial variants, it lacks specificity about what kind of variants or what 'get' entails (e.g., list, fetch, query). It doesn't distinguish this tool from sibling tools like 'get_variant' or 'get_structural_variants' in terms of scope or target.

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

Usage Guidelines1/5

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

The description provides no guidance on when to use this tool versus alternatives. There are multiple sibling tools for retrieving variants (e.g., 'get_variant', 'get_variants_in_gene', 'get_structural_variants'), but the description doesn't clarify that this tool is specifically for mitochondrial variants or suggest any context or prerequisites for its use.

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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