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import_job_by_url

Import job postings from URLs like LinkedIn or Greenhouse to track applications and optionally trigger auto-apply functionality.

Instructions

Import a job from a URL (e.g., LinkedIn, Greenhouse, Lever, Workday) and add it to your applications. Optionally trigger auto-apply immediately. Use this when a user has a direct link to a job posting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe job posting URL (supports LinkedIn, Greenhouse, Lever, Workday, and most ATS platforms)
jobHuntIdYesThe job hunt ID to add this job to
autoApplyNoWhether to automatically apply to this job (default: false)

Implementation Reference

  • The tool handler for 'import_job_by_url'. It validates the input using zod and invokes the client's importJobByUrl method.
      'import_job_by_url',
      'Import a job from a URL (e.g., LinkedIn, Greenhouse, Lever, Workday) and add it to your applications. Optionally trigger auto-apply immediately. Use this when a user has a direct link to a job posting.',
      {
        url: z.string().describe('The job posting URL (supports LinkedIn, Greenhouse, Lever, Workday, and most ATS platforms)'),
        jobHuntId: z.string().describe('The job hunt ID to add this job to'),
        autoApply: z.boolean().optional().describe('Whether to automatically apply to this job (default: false)'),
      },
      async (args) => {
        const result = await client.importJobByUrl(args.url, args.jobHuntId, args.autoApply || false);
        return {
          content: [{
            type: 'text' as const,
            text: JSON.stringify({
              message: args.autoApply
                ? 'Job imported and auto-apply triggered. The application will be submitted shortly.'
                : 'Job imported successfully and added to your applications.',
              application: formatApplication(result),
            }, null, 2),
          }],
        };
      }
    );
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 mentions the tool imports a job and optionally triggers auto-apply, but lacks details on permissions needed, rate limits, error handling, or what happens during import (e.g., data parsing, validation). This is insufficient for a mutation tool with zero 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 front-loaded and efficiently structured in two sentences with zero waste. The first sentence states the core action and optional feature, and the second provides clear usage context, making it easy to understand quickly.

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 complexity (mutation with 3 parameters) and lack of annotations and output schema, the description is moderately complete. It covers the purpose and basic usage but lacks behavioral details like side effects, return values, or error conditions, which are important for a tool that modifies data and interacts with external URLs.

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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by implying the URL supports specific platforms and mentioning the auto-apply option, but does not provide additional syntax or format details beyond what the schema provides, meeting the baseline for high schema coverage.

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 specific verbs ('import', 'add') and resources ('job from a URL', 'to your applications'), and distinguishes it from siblings by specifying it handles direct job posting URLs from platforms like LinkedIn, Greenhouse, etc., unlike generic job-related tools such as 'add_job_to_applications' or 'search_jobs'.

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

Usage Guidelines4/5

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

The description provides explicit context for when to use this tool ('when a user has a direct link to a job posting'), which helps differentiate it from alternatives. However, it does not explicitly state when not to use it or name specific sibling tools as alternatives, such as 'add_job_to_applications' for manual job entry.

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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