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Augmented-Nature

ProteinAtlas MCP Server

get_blood_expression

Retrieve blood cell expression data for proteins to analyze gene activity in immune cells and blood components.

Instructions

Get blood cell expression data for a protein

Input Schema

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

Implementation Reference

  • Primary handler function for the 'get_blood_expression' tool. It validates the input arguments using isValidGeneArgs, fetches the blood expression data via fetchBloodExpression, and returns the JSON-formatted result or an error response.
    private async handleGetBloodExpression(args: any) {
      if (!isValidGeneArgs(args)) {
        throw new McpError(ErrorCode.InvalidParams, 'Invalid gene arguments');
      }
    
      try {
        const result = await this.fetchBloodExpression(args.gene);
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(result, null, 2),
            },
          ],
        };
      } catch (error) {
        return {
          content: [
            {
              type: 'text',
              text: `Error fetching blood expression: ${error instanceof Error ? error.message : 'Unknown error'}`,
            },
          ],
          isError: true,
        };
      }
  • Input schema defining the parameters for the get_blood_expression tool: requires a 'gene' string and optional 'format' enum.
    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:510-521 (registration)
    Registration of the get_blood_expression tool in the ListToolsRequestSchema response, including name, description, and input schema.
    {
      name: 'get_blood_expression',
      description: 'Get blood cell expression 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:680-681 (registration)
    Dispatch registration in the CallToolRequestSchema switch statement that routes calls to the handleGetBloodExpression method.
    case 'get_blood_expression':
      return this.handleGetBloodExpression(args);
  • Helper function that queries the Protein Atlas API for blood expression data using specific blood-related columns and delegates to searchProteins.
    private async fetchBloodExpression(gene: string): Promise<any> {
      const columns = ['g', 'eg', 'rnabcs', 'rnabcd', 'rnabcss', 'blood_RNA_basophil', 'blood_RNA_classical_monocyte', 'blood_RNA_eosinophil', 'blood_RNA_neutrophil', 'blood_RNA_NK-cell'];
      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 what the tool does but fails to describe key traits such as whether it's a read-only operation, if it requires authentication, rate limits, error handling, or the structure of returned data. This is a significant gap 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 unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly, with no wasted 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. It does not address behavioral aspects like safety, permissions, or data format details, nor does it explain return values or potential errors. For a tool with no structured support, 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 100% description coverage, with clear documentation for both parameters ('gene' and 'format'), including an enum for 'format'. The description does not add any meaning beyond what the schema provides, such as explaining gene symbol conventions or format implications, so it meets the baseline for high schema coverage.

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 verb 'Get' and the resource 'blood cell expression data for a protein', making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'get_brain_expression' or 'get_tissue_expression', which target different biological contexts, so it lacks sibling distinction for a perfect score.

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. It does not mention when to choose it over sibling tools such as 'get_brain_expression' or 'get_tissue_expression', nor does it specify prerequisites or exclusions, leaving usage context implied at best.

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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