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careerproof-mcp

careerproof-mcp

by detonnate
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Generate interview questions

careerproof_generate_interview_questions

Generate interview questions from job requirements, prioritizing those with the weakest evidence to focus your preparation effectively.

Instructions

Generates likely interview questions from a job's requirements, prioritising requirements with the weakest evidence so preparation time is spent where it matters most.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "jobId": "<jobId>"
    },
    "name": "careerproof_generate_interview_questions"
  }
}

Input Schema

NameRequiredDescriptionDefault
countNo
jobIdYesJob ID returned by careerproof_analyse_job_description

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose a real behavioral trait: questions are biased toward requirements with the weakest evidence. It says nothing about determinism, output format, limits on generation, or whether count is capped at the schema maximum of 30.

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?

A single front-loaded sentence with no filler, leading with the action and following with the prioritisation rationale. Slightly dense and lacks any structural separation of purpose vs. behaviour, but every clause earns its place.

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 simple two-parameter generation tool with no output schema, the description covers what it produces but omits the relationship to prerequisite tools, the meaning of `count`, and any indication of return shape or determinism. Adequate for selection, thin for confident invocation.

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

Parameters2/5

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

Schema coverage is 50%: jobId has a description pointing at careerproof_analyse_job_description, while `count` (1-30) is undocumented in both schema and description. The description never references either parameter, so it does not compensate for the gap.

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?

States a specific verb (generates) and resource (interview questions) and adds scope ('from a job's requirements'). It is clearly distinguishable in substance from siblings like careerproof_generate_star_answer or careerproof_score_interview_answer, though it never names or contrasts them.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance and no named alternative. The phrase 'prioritising requirements with the weakest evidence' hints at intent but does not state prerequisites (presumably a prior careerproof_analyse_job_description) or when another sibling should be chosen instead.

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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