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Glama

trivia

by io.github.pipeworx-io

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. Dates show when Glama detected each change.

  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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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have notably unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and polymarket_edges, polymarket_arbitrage, and bet_research heavily overlap in surfacing betting opportunities. Entity_profile, recent_changes, and compare_entities also share overlapping research scope, making misselection likely.

Naming Consistency3/5

Most names use snake_case and a roughly readable verb_noun style (resolve_entity, compare_entities, list_categories), but conventions vary: some are bare nouns (entity_profile), some are plain verbs (remember, forget), and ask_pipeworx/pipeworx_* break the pattern. It is readable overall, but not a consistent scheme.

Tool Count2/5

34 tools is far more than the 'trivia' name implies, and most of them (Pipeworx research, Polymarket analysis, memory, subscriptions) are unrelated to trivia. The set reads as an entire platform bundled together rather than a purpose-scoped server.

Completeness2/5

For the stated trivia purpose, the surface is missing core lifecycle features like quiz sessions, answer validation, or scoring; the few trivia tools are just category/reference lookups. As a general data/research server it is broad, but there are significant gaps and no coherent domain model tying the tools together.

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