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lightcube-labs

Interview Prep MCP

Get interview questions

get_questions
Read-only

Retrieve role-specific interview questions filtered by topic and level, with optional model answers and paging to support focused practice.

Instructions

Return interview questions for a role, optionally filtered by topic and level, with model answers if include_answers is true. Supports paging with offset.

Input Schema

NameRequiredDescriptionDefault
nNoHow many questions (1-25)
roleYesai-engineer or investment-banking
levelNoEntry, Mid or Senior
topicNoTopic id from list_topics, e.g. rag-and-retrieval, valuation, lbo
offsetNoSkip this many matches (for paging)
include_answersNoInclude model answers, follow-ups and common mistakes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds little beyond that — the include_answers and offset behavior it mentions is already in the schema — and it doesn't state the result count cap, ordering, or what a question object contains.

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 sentences, zero filler, front-loaded with the primary outcome and followed by the optional modifiers. Every clause maps to a real capability of the tool.

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?

For a 6-parameter read tool with 100% schema coverage and clear read-only annotations, the description covers purpose, filters, and paging adequately. The remaining gap is the return shape (no output schema exists), which the agent must discover at call time.

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%, with enums, defaults, and min/max bounds all documented per parameter. The description only restates role/topic/level/offset/include_answers without adding format or selection semantics, so the schema is doing all the work.

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 ("Return interview questions for a role") and enumerates the available filters and the paging mechanism. It's clear what the tool does, but it never distinguishes itself from siblings like search_questions or random_drill, so an agent must infer which retrieval tool fits.

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?

Implies usage through the filter list (role required, topic/level optional) but gives no explicit when-to-use guidance and no exclusions versus search_questions or random_drill. The agent has to guess whether this is the canonical browse/enumerate path.

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