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Open Trivia DB — Get Questions

opentdb.trivia.questions
Read-onlyIdempotent

Fetch random trivia questions from the Open Trivia Database. Returns up to 50 questions per call with category, difficulty, correct answer, incorrect answers, and a shuffled all_answers list for quiz presentation. HTML entities are decoded automatically. Filter by category (use opentdb.trivia.categories to get IDs), difficulty (easy/medium/hard), and type (multiple-choice or True/False). Examples: 10 general knowledge questions (no params), 5 hard science questions (category=17, amount=5, difficulty=hard), True/False history questions (category=23, type=boolean). Source: opentdb.com — CC BY-SA 4.0, user-contributed, 5000+ verified questions, 24 categories, free unlimited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoQuestion type: multiple (4 choices) or boolean (True/False). Omit for both types.
amountNoNumber of trivia questions to return (default 10, max 50).
categoryNoCategory ID to filter by (e.g. 9=General Knowledge, 17=Science & Nature, 21=Sports, 23=History). Use opentdb.trivia.categories to list all 24 IDs.
difficultyNoDifficulty filter: easy, medium, or hard. Omit for mixed difficulty.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

The annotations already establish a safe read-only/idempotent profile. The description adds genuinely useful behavioral context beyond those annotations: results are random, capped at 50, include a shuffled all_answers list, and HTML entities are decoded automatically. No contradiction with the annotations exists.

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 front-loaded with the core behavior, then covers result format, filters, examples, and provenance in a logical order. Every sentence contributes to correct invocation or expectation-setting, with no redundant restatement of the schema.

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?

Given that the tool has no required parameters, a complete output schema, and rich safe-read annotations, nothing needed for correct invocation is missing. The description covers defaults, limits, filtering options, output fields, and where to obtain category IDs, making it self-sufficient for an agent.

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 schema covers all four parameters, so the baseline is 3. The description earns extra credit by giving realistic example combinations and clarifying that category IDs come from a sibling tool, which makes the parameter values more actionable without simply repeating 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 opens with a specific verb and resource ('Fetch random trivia questions from the Open Trivia Database') and lists the returned fields. It clearly distinguishes this from the nearby opentdb metadata tools by framing it as the question-fetching endpoint.

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?

It gives concrete invocation scenarios (10 general knowledge questions with no params, 5 hard science questions, boolean history questions) and explicitly routes category-ID lookups to opentdb.trivia.categories. It does not provide an explicit 'when not to use' statement or mention the count-related siblings, so it stops just short of fully complete routing guidance.

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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