Skip to main content
Glama
Augmented-Nature

ProteinAtlas MCP Server

get_subcellular_location

Retrieve subcellular localization data for proteins to understand their cellular distribution and function using Human Protein Atlas information.

Instructions

Get subcellular localization data for a protein

Input Schema

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

Implementation Reference

  • The main handler function for the 'get_subcellular_location' tool. Validates input using isValidGeneArgs, fetches data via fetchSubcellularLocalization, and returns JSON-formatted subcellular location data or an error.
    private async handleGetSubcellularLocation(args: any) {
      if (!isValidGeneArgs(args)) {
        throw new McpError(ErrorCode.InvalidParams, 'Invalid gene arguments');
      }
    
      try {
        const result = await this.fetchSubcellularLocalization(args.gene);
        return {
          content: [
            {
              type: 'text',
              text: JSON.stringify(result, null, 2),
            },
          ],
        };
      } catch (error) {
        return {
          content: [
            {
              type: 'text',
              text: `Error fetching subcellular location: ${error instanceof Error ? error.message : 'Unknown error'}`,
            },
          ],
          isError: true,
        };
      }
    }
  • Input schema definition for the tool, specifying 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:535-546 (registration)
    Tool registration in the ListToolsRequestSchema handler, defining name, description, and input schema.
    {
      name: 'get_subcellular_location',
      description: 'Get subcellular localization 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:685-686 (registration)
    Dispatch case in the CallToolRequestSchema switch statement that routes to the handler.
    case 'get_subcellular_location':
      return this.handleGetSubcellularLocation(args);
  • Helper method that fetches subcellular location data by calling searchProteins with specific columns: scl (subcell location), scml (main location), scal (additional locations), relce (reliability).
    private async fetchSubcellularLocalization(gene: string): Promise<any> {
      const columns = ['g', 'eg', 'scl', 'scml', 'scal', 'relce'];
      return this.searchProteins(gene, 'json', columns, 1);
    }
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. It states what the tool does but lacks details on permissions, rate limits, data sources, or response structure. For a tool with no annotation coverage, this minimal description is insufficient to inform safe and effective use.

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 with no unnecessary words, making it highly concise and front-loaded. It efficiently communicates the core purpose without any fluff or redundancy, earning a top score for brevity and clarity.

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 doesn't explain what the returned data includes (e.g., localization details, confidence scores) or any behavioral aspects like error handling. For a tool with 2 parameters and no structured output, more context is needed to ensure proper usage.

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 description adds no parameter-specific information beyond what the input schema provides. Since schema description coverage is 100%, the baseline score is 3. The schema already documents the 'gene' parameter as a gene symbol and 'format' as an enum for output format, so the description doesn't enhance parameter understanding.

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 ('subcellular localization data for a protein'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_by_subcellular_location' or 'get_protein_info', which might offer overlapping functionality, so it falls short of 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. For example, it doesn't specify if this is for single-protein lookups compared to batch operations or how it differs from 'search_by_subcellular_location'. Without such context, users must infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Augmented-Nature/ProteinAtlas-MCP-Server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server