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AI Interview Questions MCP

AI Interview Questions MCP

by Johnnnmai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

Random question

random_question
Read-only

Generate random interview questions for practice with hidden answers. Filter by track, topic, and level, or pass a seed to replay the same question set.

Instructions

Pick random questions for practice (answers hidden). Optional track, topic and level filters; pass a seed to replay the same set.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "random_question"
  }
}

Input Schema

NameRequiredDescriptionDefault
seedNoSame seed and filters return the same questions
countNo
levelNo
topicNo
trackNoai-engineer or investment-banking; omit for both

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover read-only, non-destructive and closed-world, so the bar is lower, and the description still adds real behavior: answers are withheld from the payload and a seed reproduces the same set. It does not mention the per-call cap of 10 or what happens when filters match too few questions; the idempotentHint=false does not conflict with seed reproducibility since no state is mutated.

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 compact sentences, front-loaded with the core action and the answers-hidden behavior, then the optional filters and seed. No filler wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description usefully signals that returned questions omit answers, and it explains filter and seed behavior. The missing piece is the result-set size limit and default count, which matters for an agent assembling a practice set.

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 coverage is only 40%, so the description has to compensate, and it does partially by naming the three filter dimensions and the seed's replay semantics. The count parameter (default 1, maximum 10) is documented in neither schema nor description, and the enum values for topic/level are left entirely to the schema, so a gap remains.

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 and resource ('Pick random questions') plus a meaningful qualifier ('answers hidden') that distinguishes it from get_question and search_questions. It never names a sibling explicitly, but the random-sampling purpose is clear enough to separate it from deterministic lookup tools.

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?

'for practice' and 'pass a seed to replay the same set' imply when to reach for it, and the filter list signals it can be scoped. However, there is no explicit when-not guidance or pointer to search_questions/get_question for deterministic lookup, so the agent must infer the routing.

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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