Hopi

Would you rather and icebreaker generator

hopi_would_you_rather_generator

Read-onlyIdempotent

Draw original, family-friendly would-you-rather pairs, open icebreaker questions or rapid-fire this-or-that pairs, optionally filtered by category. Would-you-rather categories: silly, food, travel, work, deep, kids. Icebreaker categories: work, party, deep, kids. This-or-that has no categories. Returns distinct items drawn at random. Source: https://hopi.co.uk/would-you-rather-generator/

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	Which list to draw from
`count`	No	How many to draw, 1 to 20 (default 1)
`category`	No	Optional category filter (would-you-rather: silly, food, travel, work, deep, kids; icebreaker: work, party, deep, kids). Omit for all.

Output Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes
`count`	Yes
`items`	Yes
`summary`	Yes
`category`	Yes
`poolSize`	Yes
`source_url`	Yes	The tool's page on hopi.co.uk (cite or open to check the figure)

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 20, 2026

addedOutput schema / required

Added value: +[
+  "category",
+  "count",
+  "items",
+  "mode",
+  "poolSize",
+  "source_url",
+  "summary"
+]

First observedSep 20, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond that: results are drawn at random, items are distinct, content is family-friendly, and the source is cited. This gives the agent a good sense of expected output behavior.

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?

The description is compact and front-loaded, with the core purpose in the first sentence. The category breakdown is structured and easy to parse, and the source line provides provenance without bloating the text. Every sentence contributes useful information.

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 read-only generator with a full input schema and an output schema, the description covers all essential choices: mode, category applicability, randomness, distinctness, and content tone. There is no missing information that would prevent an agent from selecting or invoking the tool correctly.

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?

The schema already documents all three parameters completely, including enums, defaults, ranges, and category lists, so the baseline is 3. The description adds marginal value by repeating category options and noting that this-or-that has no categories, but it does not add significant semantic detail beyond the schema.

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 states a clear, specific purpose: drawing would-you-rather pairs, icebreaker questions, or this-or-that pairs, with optional category filtering. It distinguishes this tool from the many other hopi generators by naming its exact content types and scope.

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 establishes when to use the tool—when the user wants original, family-friendly conversation prompts or icebreakers—and even explains which categories apply to which mode. It does not explicitly name alternative tools or exclusion cases, but the use context is strong and unambiguous.

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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