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AI Interview Questions MCP

AI Interview Questions MCP

by Johnnnmai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

List topics

list_topics
Read-onlyIdempotent

Retrieve available interview topics for AI Engineer and Investment Banking roles, with question counts by Entry, Mid, and Senior level, to plan targeted practice.

Instructions

List the interview topics (AI Engineer and Investment Banking) with question counts per level (Entry, Mid, Senior).

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": "list_topics"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and a closed world, so the safety profile is covered. The description adds value by disclosing the exact scope of the data returned (two fixed topics, counts split across Entry/Mid/Senior), which is not in any structured field since there is no output schema.

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?

A single sentence that front-loads the action and the return content with no filler or redundancy. Every clause (topics, counts, levels) earns its place.

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 zero-parameter, non-destructive list tool with no output schema, the description successfully carries the return-shape burden by naming the topics and the per-level counts. Only minor gaps remain, such as ordering or exact count semantics, which an agent does not strictly need to call it.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline is 4; there is nothing parameter-wise to clarify. The description correctly implies a no-argument call with no filters.

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 (List) and resource (interview topics) and even enumerates the fixed set of topics and the per-level breakdown returned. It is clearly distinguishable in kind from the question-level siblings, though it never names them explicitly.

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?

There is no explicit when-to-use or when-not guidance and no reference to search_questions, get_question, or random_question. The usage is only implied by the zero-parameter, enumeration framing (a starting point for browsing).

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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