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get_dataset_info

Retrieve details about available GTEx datasets, including dataset IDs and descriptions, to identify relevant genomics data for analysis.

Instructions

Get information about available GTEx datasets

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetIdNoSpecific dataset ID (optional, returns all if not provided)

Implementation Reference

  • Main handler function for 'get_dataset_info' tool. Calls API client, handles errors, and formats dataset information into a detailed markdown response.
    async getDatasetInfo(args: any) {
      const result = await this.apiClient.getDatasetInfo(args.datasetId);
    
      if (result.error) {
        return {
          content: [{
            type: "text",
            text: `Error retrieving dataset information: ${result.error}`
          }],
          isError: true
        };
      }
    
      const datasets = result.data || [];
      if (datasets.length === 0) {
        return {
          content: [{
            type: "text",
            text: "No dataset information available."
          }]
        };
      }
    
      let output = `**GTEx Dataset Information**\n\n`;
    
      datasets.forEach((dataset, index) => {
        if (datasets.length > 1) {
          output += `### Dataset ${index + 1}: ${dataset.datasetId}\n`;
        }
        
        output += `**Basic Information:**\n`;
        output += `  • ID: ${dataset.datasetId}\n`;
        output += `  • Display Name: ${dataset.displayName}\n`;
        output += `  • Organization: ${dataset.organization}\n`;
        if (dataset.description) {
          output += `  • Description: ${dataset.description}\n`;
        }
        if (dataset.dbgapId) {
          output += `  • dbGaP ID: ${dataset.dbgapId}\n`;
        }
    
        output += `\n**Genomic References:**\n`;
        output += `  • Genome Build: ${dataset.genomeBuild}\n`;
        output += `  • GENCODE Version: ${dataset.gencodeVersion}\n`;
        if (dataset.dbSnpBuild) {
          output += `  • dbSNP Build: ${dataset.dbSnpBuild}\n`;
        }
    
        output += `\n**Sample Statistics:**\n`;
        output += `  • Total subjects: ${dataset.subjectCount.toLocaleString()}\n`;
        output += `  • Total tissues: ${dataset.tissueCount}\n`;
        output += `  • RNA-seq samples: ${dataset.rnaSeqSampleCount.toLocaleString()}\n`;
        output += `  • RNA-seq + genotype samples: ${dataset.rnaSeqAndGenotypeSampleCount.toLocaleString()}\n`;
    
        output += `\n**QTL Analysis:**\n`;
        output += `  • eQTL subjects: ${dataset.eqtlSubjectCount.toLocaleString()}\n`;
        output += `  • eQTL tissues: ${dataset.eqtlTissuesCount}\n`;
    
        if (datasets.length > 1 && index < datasets.length - 1) {
          output += '\n';
        }
      });
    
      return {
        content: [{
          type: "text",
          text: output
        }]
      };
    }
  • src/index.ts:763-767 (registration)
    Registration and dispatching logic in the main CallToolRequestHandler that routes 'get_dataset_info' calls to the referenceHandlers.getDatasetInfo method.
    if (name === "get_dataset_info") {
      return await referenceHandlers.getDatasetInfo({
        datasetId: args?.datasetId
      });
    }
  • Tool schema definition including name, description, and input schema for 'get_dataset_info' in the ListTools response.
    name: "get_dataset_info",
    description: "Get information about available GTEx datasets",
    inputSchema: {
      type: "object",
      properties: {
        datasetId: {
          type: "string",
          description: "Specific dataset ID (optional, returns all if not provided)"
        }
      }
    }
  • API client helper method that makes the HTTP request to GTEx Portal API /metadata/dataset endpoint to fetch dataset information.
    async getDatasetInfo(datasetId?: string): Promise<GTExApiResponse<DatasetInfo[]>> {
      try {
        const params = datasetId ? { datasetId } : {};
        const queryParams = this.buildQueryParams(params);
        const response = await this.axiosInstance.get(`/metadata/dataset?${queryParams}`);
        return { data: response.data };
      } catch (error) {
        return error as GTExApiResponse<DatasetInfo[]>;
      }
    }
  • TypeScript interface defining the structure of DatasetInfo objects returned by the API, used for type safety in handlers and client.
    export interface DatasetInfo {
      datasetId: string;
      dbSnpBuild: number;
      dbgapId: string;
      description: string;
      displayName: string;
      eqtlSubjectCount: number;
      eqtlTissuesCount: number;
      gencodeVersion: string;
      genomeBuild: string;
      organization: string;
      rnaSeqAndGenotypeSampleCount: number;
      rnaSeqSampleCount: number;
      subjectCount: number;
      tissueCount: number;
    }
Behavior2/5

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. While 'Get information' implies a read-only operation, it doesn't specify whether this requires authentication, what format the information returns in, whether there are rate limits, or if it's a lightweight metadata query versus a computationally intensive operation. The description is too minimal for a tool with no annotation coverage.

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 a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized for a simple lookup tool and front-loads the essential information.

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 lack of annotations and output schema, the description is incomplete. For a tool in a complex GTEx analysis environment with 24 sibling tools, it should provide more context about what 'dataset information' includes, how it relates to other tools, and what format the output takes. The current description leaves too many questions unanswered for effective tool selection.

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 schema description coverage is 100%, with the single parameter clearly documented as optional and returning all datasets if not provided. The description adds no additional parameter information beyond what's in the schema, but since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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 with a specific verb ('Get') and resource ('information about available GTEx datasets'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'get_tissue_info' or 'get_sample_info', which also retrieve information about specific GTEx resources, leaving room for potential confusion.

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?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools that retrieve various types of GTEx information (e.g., get_tissue_info, get_sample_info, get_gene_info), there's no indication of what distinguishes this dataset-focused tool from others, leaving the agent to guess based on tool names alone.

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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