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Trivia

by io.github.pipeworx-io
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Get Category Stats

get_category_stats
Read-onlyIdempotent

Question inventory for one Open Trivia Database (opentdb.com) category, given the numeric category id from list_categories. Returns the total number of quiz questions in that category plus the easy, medium, and hard counts. Answers how many trivia questions the Open Trivia DB holds on a topic before requesting a quiz round of a given difficulty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesCategory ID. Use list_categories to get available IDs.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
easyYesEasy difficulty question count
hardYesHard difficulty question count
totalYesTotal question count for category
mediumYesMedium difficulty question count
category_idYesThe category ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "category_id": {
+      "description": "The category ID",
+      "type": "number"
+    },
+    "easy": {
+      "description": "Easy difficulty question count",
+      "type": "number"
+    },
+    "hard": {
+      "description": "Hard difficulty question count",
+      "type": "number"
+    },
+    "medium": {
+      "description": "Medium difficulty question count",
+      "type": "number"
+    },
+    "total": {
+      "description": "Total question count for category",
+      "type": "number"
+    }
+  },
+  "required": [
+    "category_id",
+    "total",
+    "easy",
+    "medium",
+    "hard"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "category": 9
+  }
+]
  3. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, lowering the burden on the description. The description adds transparency by specifying the output granularity (total plus easy/medium/hard counts) and the dependency on a category ID from list_categories, which is useful beyond the structured annotations.

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 compact, with the core purpose in the first sentence and a clarifying follow-up sentence. The opening noun phrase 'Question inventory' is slightly awkward, but the text is efficient and front-loaded with the essential 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?

With only one parameter, a fully descriptive schema, and an output schema present, the description provides sufficient context for tool selection and invocation. It integrates the dependency on list_categories and the pre-quiz use case, making it complete for the tool's simplicity.

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 coverage is 100%, and the parameter description already explains 'category' as a numeric ID sourced from list_categories. The tool description repeats this guidance without adding new parameter-level detail, so it meets the baseline but does not exceed it.

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 clearly identifies the tool as providing a question count inventory for a single Open Trivia Database category, explicitly stating it returns total and difficulty-specific counts. It distinguishes itself from sibling tools like list_categories and get_questions by specifying the input is a numeric category ID from list_categories and that it answers pre-quiz count questions.

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 implies the intended use case—checking question counts 'before requesting a quiz round'—and directs users to list_categories for valid IDs. It does not explicitly contrast with alternatives like get_questions or suggest_questions, but the context and references provide sufficient guidance for appropriate use.

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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