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Quiz: suggest daily challenge questions

queue_daily_challenge

Suggest questions for the daily challenge (one question a day, answered any time that day, no clock) of a group, household chat or channel the person hosts — only when they ask. The questions go to the host's review list in the app ("waiting for your yes"); none is published until the person approves it there. multiple_choice or closest_wins, 1–14 at a time. Consent: refused if the person's AI setting for groups is 'off'; otherwise always for their review.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoOnly needed with an id that list_quiz_shows doesn't list, e.g. "group".
whereYesThe group, household chat or channel — its name or id from list_quiz_shows (canHostIn), e.g. "Familjen" or "g_8f3k2l1m".
questionsYes1–14 questions.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
addedYes
whereYes
itemIdYes
queuedYes
statusYes
dailyOnYes
reviewUrlYes
waitingForReviewYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior5/5

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

Adds substantial behavior beyond annotations: submitted questions land in the host's review list ("waiting for your yes") and nothing is published until approved there, plus the consent/gating rule. This explains the draft-and-approve lifecycle that readOnlyHint=false alone does not convey.

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?

One dense paragraph, front-loaded with purpose then scope, volume limits, and consent. Every clause carries information, though the parenthetical challenge definition and quoted UI string add a little length without much agent utility.

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?

With annotations and an output schema present, the description supplies the remaining agent-critical context — hosting scope, 1–14 question limit, question kinds, review-before-publish, and consent gating. Nothing needed to call it correctly is missing.

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%, so the schema already documents where, kind, and the nested question shape. The description only echoes what the schema states (multiple_choice or closest_wins, 1–14 at a time), adding no new parameter meaning.

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 — suggesting questions for the daily challenge of a group/household chat/channel the person hosts — and defines the challenge concept (one question a day, answered any time, no clock). It does not explicitly contrast itself with the close sibling suggest_question, so the agent must infer the boundary.

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?

Gives a clear activation condition ("only when they ask") and a hard gating rule (refused if the person's AI setting for groups is 'off'), which is genuine when/when-not guidance. It never names an alternative tool, so the routing vs. suggest_question remains unstated.

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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