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add_job_to_applications

Save and track jobs found during searches by adding them to your application tracking list for organized job hunting.

Instructions

Add a job from search results to your applications. Use this when a user wants to save/track a job they found.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesThe job ID to add to applications
jobHuntIdYesThe job hunt ID to add this job to

Implementation Reference

  • The 'add_job_to_applications' tool implementation and registration, which calls the JobGPTApiClient to add a job to a specific job hunt.
    server.tool(
      'add_job_to_applications',
      'Add a job from search results to your applications. Use this when a user wants to save/track a job they found.',
      {
        jobId: z.string().describe('The job ID to add to applications'),
        jobHuntId: z.string().describe('The job hunt ID to add this job to'),
      },
      async (args) => {
        const result = await client.addJobToApplications(args.jobId, args.jobHuntId);
        return { content: [{ type: 'text' as const, text: JSON.stringify(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 full burden. It mentions 'save/track a job,' which hints at a write operation, but lacks details on permissions, effects (e.g., duplicates, limits), or response behavior. This is inadequate 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the main action and followed by usage context. It's efficient with minimal waste, though slightly repetitive in phrasing ('Add a job...' and 'save/track a job').

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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks behavioral details (e.g., success/failure outcomes, constraints) and doesn't compensate for the missing structured data, making it insufficient for reliable 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?

Schema description coverage is 100%, so the schema fully documents the two parameters (jobId and jobHuntId). The description adds no additional parameter details beyond what the schema provides, meeting the baseline of 3 for high coverage without extra value.

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 ('Add a job from search results to your applications') and resource ('job'), making the purpose understandable. However, it doesn't explicitly differentiate from siblings like 'apply_to_job' or 'import_job_by_url' beyond implying it's for saving/tracking rather than applying or importing.

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

Usage Guidelines3/5

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

The description provides some guidance with 'Use this when a user wants to save/track a job they found,' which implies a context of job search results. However, it doesn't specify when not to use it or mention alternatives like 'apply_to_job' for direct application, leaving usage somewhat implied rather than explicit.

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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