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Augmented-Nature

ProteinAtlas MCP Server

get_pathology_data

Retrieve cancer and pathology data for specific proteins from the Human Protein Atlas database to support biomedical research and analysis.

Instructions

Get cancer and pathology data for a protein

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geneYesGene symbol
formatNoOutput format (default: json)

Implementation Reference

  • The main handler function for the 'get_pathology_data' tool. Validates input using isValidGeneArgs, fetches pathology data via fetchPathologyData, and returns the JSON-formatted result or an error response.
    private async handleGetPathologyData(args: any) {
      if (!isValidGeneArgs(args)) {
        throw new McpError(ErrorCode.InvalidParams, 'Invalid gene arguments');
      }
    
      try {
        const result = await this.fetchPathologyData(args.gene);
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(result, null, 2),
            },
          ],
        };
      } catch (error) {
        return {
          content: [
            {
              type: 'text',
              text: `Error fetching pathology data: ${error instanceof Error ? error.message : 'Unknown error'}`,
            },
          ],
          isError: true,
        };
      }
    }
  • Input schema for the 'get_pathology_data' tool, defining required 'gene' parameter and optional 'format'.
    inputSchema: {
      type: 'object',
      properties: {
        gene: { type: 'string', description: 'Gene symbol' },
        format: { type: 'string', enum: ['json', 'tsv'], description: 'Output format (default: json)' },
      },
      required: ['gene'],
    },
  • src/index.ts:562-573 (registration)
    Tool registration in the tools list, specifying name, description, and input schema for MCP server.setTools.
    {
      name: 'get_pathology_data',
      description: 'Get cancer and pathology data for a protein',
      inputSchema: {
        type: 'object',
        properties: {
          gene: { type: 'string', description: 'Gene symbol' },
          format: { type: 'string', enum: ['json', 'tsv'], description: 'Output format (default: json)' },
        },
        required: ['gene'],
      },
    },
  • src/index.ts:690-691 (registration)
    Dispatcher switch case in the CallToolRequestHandler that routes 'get_pathology_data' calls to the handler method.
    case 'get_pathology_data':
      return this.handleGetPathologyData(args);
  • Helper method that performs the core data fetch for pathology data by calling searchProteins with specific prognostic columns.
    private async fetchPathologyData(gene: string): Promise<any> {
      const columns = ['g', 'eg', 'prognostic_Breast_Invasive_Carcinoma_(TCGA)', 'prognostic_Colon_Adenocarcinoma_(TCGA)', 'prognostic_Lung_Adenocarcinoma_(TCGA)', 'prognostic_Prostate_Adenocarcinoma_(TCGA)'];
      return this.searchProteins(gene, 'json', columns, 1);
    }
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 data but does not cover aspects like authentication needs, rate limits, data freshness, or error handling. This leaves significant gaps in understanding how the tool behaves in practice.

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, direct sentence that efficiently conveys the core purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick understanding.

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 (2 parameters, no output schema, no annotations), the description is minimally adequate. It states what data is retrieved but lacks details on output structure, data scope, or integration with sibling tools. Without annotations or output schema, more context would improve completeness for effective agent use.

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 100% description coverage, clearly documenting the 'gene' and 'format' parameters. The description adds no additional meaning beyond the schema, such as explaining what 'cancer and pathology data' entails or providing examples. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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 action ('Get') and resource ('cancer and pathology data for a protein'), making the purpose understandable. However, it does not distinguish this tool from siblings like 'search_cancer_markers' or 'get_protein_info', which might overlap in scope, so it lacks explicit differentiation.

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 such as 'search_cancer_markers' or 'get_protein_info'. It implies usage for retrieving pathology data but offers no context on prerequisites, exclusions, or comparisons to sibling tools.

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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