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AI Interview Questions MCP

AI Interview Questions MCP

by Johnnnmai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

Get question

get_question
Read-onlyIdempotent

Fetch one AI engineering or investment banking interview question by ID, number, or slug with model answer, framework, follow-ups, and common mistakes; hide the answer for practice.

Instructions

Get one question by id (e.g. 'aie-026' or 'ib-012'), number (26, AI Engineer) or slug ('what-is-rag'), with the model answer, answer framework, likely follow-ups and common mistakes.

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": {
      "id": "<id>"
    },
    "name": "get_question"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesQuestion id like 'aie-026' or 'ib-012', a number like '26', or a slug
include_answerNoSet false to show only the question (for practice)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, closed-world, non-destructive, so the safety profile is covered. The description adds real value beyond them by disclosing what the call returns (model answer, answer framework, likely follow-ups, common mistakes) and that answers can be suppressed for practice mode.

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 the verb and resource first and no filler. The only slight waste is repeating the id examples already present in the schema.

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 carries the return-content burden and does so briefly and adequately for a simple single-record read. Minor gaps remain: no mention of not-found behavior, and the 'number, AI Engineer' combined topic+number format is ambiguous.

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 100% and both parameters are documented in the schema, including the practice-mode semantics of include_answer. The description restates the id formats and the practice option but adds no syntax or format detail beyond what the schema already provides, so the baseline 3 applies.

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 ('Get one question by id') with accepted identifier formats and enumerates the payload (model answer, framework, follow-ups, mistakes). 'One question' implicitly contrasts with the list/search/random siblings, but none is named explicitly, so differentiation is left partly to inference.

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?

Usage is implied: fetch a single known question, with the 'for practice' hint on include_answer=false showing one workflow. There is no explicit when-to-use-this-vs-search_questions/random_question routing or statement about what happens when the id is unknown.

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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