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Rorogogogo

jobjourney-claude-plugin

by Rorogogogo

generate_interview_questions

Generate practice interview questions for a specific job using AI. Choose technical or behavioral questions to prepare for interviews.

Instructions

Use AI to generate practice interview questions for a specific job. Choose between technical or behavioral questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyNoCompany name
job_titleYesJob title
descriptionNoJob description
interview_typeNoType of interview questions (default: Technical)
required_skillsNoRequired skills

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.2.5

TDQS

B3.3/5.0
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 behavioral burden. It says the tool uses AI, but it does not disclose whether questions are stored, whether the call is read-only, latency or cost implications, or what the output looks like.

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?

Two short sentences with no repetition or filler. The core action and the main option are front-loaded.

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?

For a generation tool with no output schema and no annotations, the description is minimally adequate: it explains what is generated and the type option, but it does not describe the return shape, quantity of questions, or any behavioral caveats.

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 five parameters and the interview_type enum. The description reinforces the choice between technical and behavioral but adds no syntax, defaults, or constraints beyond the schema.

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 states a clear verb and resource: generate practice interview questions for a specific job, with an option for technical or behavioral. It does not distinguish the tool from the sibling conduct_mock_interview, which is a related interview-preparation tool.

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?

It implies usage for interview preparation and tells the agent to choose technical or behavioral, but it gives no explicit when-to-use guidance, prerequisites, or alternatives such as conduct_mock_interview.

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

Deploy Server

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