Ripostiq

List interview-prep topics

list_interviews

Read-only

List the curated Interview-Prep topics (e.g. 'Agentic AI in Production'). Each is a realistic, adaptive mock interview on a fixed topic — concept→scenario question pairs across several areas — that ends in a candid scorecard. No login or course needed. Call this when someone wants to prep for an interview on a topic the catalog covers, then start_interview.

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_interviews"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 31, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful behavioral context: 'No login or course needed', the adaptive mock-interview structure, and the candid scorecard. This is more than enough for a read-only list tool.

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?

The description is front-loaded with the action and includes a compact example. The later sentences explain content and usage without padding, though the dash-heavy clause about question pairs is slightly dense.

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

Completeness5/5

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

For a no-parameter, read-only list tool with an output schema and annotations, the description covers purpose, content type, prerequisites, and the recommended next step. Nothing essential is missing.

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?

There are zero parameters and the schema coverage is 100%, so there is nothing to document. The description adds conceptual meaning about what the listed items are, which is sufficient for parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('List') and resource ('curated Interview-Prep topics'), with an example topic, making the action unambiguous. It also distinguishes itself from sibling tools by clarifying that this is the listing step before start_interview, rather than actually conducting an interview.

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

Usage Guidelines4/5

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

The description clearly states when to use the tool ('Call this when someone wants to prep for an interview on a topic the catalog covers') and points to the follow-up tool (start_interview). It does not explicitly discuss when not to use it relative to other siblings, so it misses the top score.

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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