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list_interviews

Retrieve and filter job interviews tracked from email confirmations, including upcoming, scheduled, or status-based results.

Instructions

List job interviews that are being actively tracked by JobGPT (detected from email confirmations). Use upcoming=true to get scheduled/rescheduled interviews. Can also filter by application ID or status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobApplicationIdNoFilter interviews for a specific job application
statusNoFilter by interview status
upcomingNoIf true, returns only upcoming interviews (SCHEDULED or RESCHEDULED)
pageNoPage number (default: 1)
limitNoNumber of results per page (default: 20, max: 50)

Implementation Reference

  • The implementation of the `list_interviews` tool handler, which uses the provided `client` to fetch interview data and formats the result.
    server.tool(
      'list_interviews',
      'List job interviews that are being actively tracked by JobGPT (detected from email confirmations). Use upcoming=true to get scheduled/rescheduled interviews. Can also filter by application ID or status.',
      {
        jobApplicationId: z.string().optional().describe('Filter interviews for a specific job application'),
        status: z.enum(['SCHEDULED', 'RESCHEDULED', 'CANCELLED', 'COMPLETED']).optional().describe('Filter by interview status'),
        upcoming: z.boolean().optional().describe('If true, returns only upcoming interviews (SCHEDULED or RESCHEDULED)'),
        page: z.number().optional().describe('Page number (default: 1)'),
        limit: z.number().optional().describe('Number of results per page (default: 20, max: 50)'),
      },
      async (args) => {
        const result = await client.listInterviews({
          jobApplicationId: args.jobApplicationId,
          status: args.status,
          upcoming: args.upcoming,
          page: args.page,
          limit: args.limit,
        });
        return { content: [{ type: 'text' as const, text: JSON.stringify({ count: result.count, interviews: result.interviews.map(formatInterview) }, 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 that interviews are 'detected from email confirmations,' which adds context about data sourcing, but it doesn't cover critical behavioral aspects such as pagination behavior (implied by 'page' and 'limit' parameters but not explained), rate limits, authentication requirements, or whether this is a read-only operation. For a list tool with 5 parameters, this leaves significant gaps in understanding how the tool behaves in practice.

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 concise and front-loaded: the first sentence states the core purpose, and the second sentence provides key usage tips. There's no wasted text, and it efficiently covers essential points in two sentences. However, it could be slightly more structured by explicitly separating purpose from filtering options for better 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 complexity (5 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits (e.g., pagination, rate limits), does not explain return values or error handling, and provides minimal guidance on tool selection versus siblings. For a list tool with filtering and pagination, this leaves the agent with insufficient context to use it effectively beyond basic parameter passing.

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 beyond the schema: it mentions 'upcoming=true' to get scheduled/rescheduled interviews and filtering by application ID or status, but these are already clear from the schema's enum and descriptions. No additional syntax, format, or usage details are provided, so it meets the baseline for high schema coverage without enhancing 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: 'List job interviews that are being actively tracked by JobGPT (detected from email confirmations).' It specifies the verb ('List') and resource ('job interviews'), and mentions the source of data ('detected from email confirmations'), which adds useful context. However, it doesn't explicitly differentiate from sibling tools like 'list_applications' or 'get_application', which might also relate to interviews indirectly.

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 implied usage guidance: it mentions using 'upcoming=true' to get scheduled/rescheduled interviews and filtering by application ID or status. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., 'list_applications' for broader application data) or any prerequisites. The guidance is functional but lacks comparative context with sibling tools.

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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