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get_industries

Retrieve valid company industry values to filter job searches, create job hunts, or update job hunts in the JobGPT MCP Server.

Instructions

Get the list of valid company industries. Use these values for the "industries" filter in search_jobs, create_job_hunt, or update_job_hunt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The 'get_industries' tool handler defined in src/tools/jobs.ts using the McpServer. It calls client.getIndustries() and returns the industry list as a JSON string.
    server.tool(
      'get_industries',
      'Get the list of valid company industries. Use these values for the "industries" filter in search_jobs, create_job_hunt, or update_job_hunt.',
      {},
      async () => {
        const industries = await client.getIndustries();
        return { content: [{ type: 'text' as const, text: JSON.stringify({ count: industries.length, industries }, null, 2) }] };
      }
    );
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It implies this is a read-only operation ('Get the list') and specifies the data's purpose as reference values for filters. However, it doesn't mention potential limitations like rate limits, authentication needs, or data freshness.

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 two concise sentences with zero waste. The first sentence states the purpose, and the second provides explicit usage guidance. Every word earns its place.

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

Completeness4/5

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

For a simple read-only tool with no parameters and no output schema, the description is mostly complete. It explains what the tool does and how to use the output. However, without annotations or output schema, it could benefit from mentioning the return format (e.g., list of strings) or any constraints.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%. The description doesn't need to explain parameters, but it adds value by clarifying the output's semantic purpose as filter values for other tools, which goes beyond what the empty schema provides.

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

Purpose5/5

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 ('list of valid company industries'). It distinguishes itself from siblings by focusing on retrieving reference data rather than job applications, resumes, or job hunts.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool: 'Use these values for the "industries" filter in search_jobs, create_job_hunt, or update_job_hunt.' It provides clear alternatives by naming three specific sibling tools where the output should be applied.

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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