QuizBase

List region codes (cultural affinity) with counts

quizbase_regions

Read-onlyIdempotent

Discover region codes used by the catalog. Cultural affinity — a question is tagged with a region if residents of that country, or members of that cultural/religious group, are statistically more likely to know the answer (NOT geography of the subject). USE WHEN: planning a quiz targeting users from a specific country or cultural background, exploring "what regions are represented". OUTPUT: array of {code, kind, label, count} sorted by count DESC. INPUTS: lang (en|pl), q (substring on code/label), kind (country|cultural), cursor, limit (max 500). Pair with quizbase_random or quizbase_list using regions:[...] to fetch matching questions.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`q`	No	Substring search on region code + label (case-insensitive). E.g. `q=jew` returns `jewish` cultural code.
`kind`	No	Filter by region type. `country` returns ISO 3166-1 alpha-2 codes (`us`, `pl`, `gb`...). `cultural` returns cultural/religious affinity codes (`jewish`, `christian-catholic`, `islam`...). Omit for both.
`lang`	No	Language for region `label` (ISO 639-1). Default "en". Supported: en, pl. Codes themselves language-agnostic.	en
`limit`	No	Page size (1-500, default 100). Catalog has ~150 distinct codes per language.
`cursor`	No	Opaque pagination cursor from previous `meta.nextCursor`. Omit for page 1.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`meta`	Yes	Pagination metadata: count returned, total available, cursor for next page.
`regions`	Yes	Region codes (cultural affinity) with localized labels, kind (country\|cultural), and per-region counts.

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds behavioral context beyond annotations: the cultural affinity definition, output format `array of {code, kind, label, count}`, sort order (count DESC), and input semantics. No contradiction with annotations.

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 yet rich, using bold labels (cultural affinity, USE WHEN, OUTPUT, INPUTS) to structure information. Every sentence adds value — from purpose to usage to pairing — with no filler or redundant restatement of the title.

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 the presence of a full output schema, the description is complete enough. It covers the tool's purpose, key semantic disambiguation, usage scenarios, output shape, and how to combine with sibling tools. No significant gaps remain for an agent to invoke it 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?

Schema description coverage is 100%, and each parameter (q, kind, lang, limit, cursor) already has clear descriptions. The tool description merely summarizes the parameters without adding new meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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 'Discover region codes used by the catalog' — a specific verb and resource. It then clarifies the crucial 'cultural affinity' meaning, explicitly distinguishing it from geography, and defines the output array shape. This clearly differentiates it from sibling list tools like categories, tags, and languages.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides an explicit 'USE WHEN' section (planning quizzes for country/cultural backgrounds) and a negative constraint ('NOT geography of the subject'). It also instructs users to pair with quizbase_random or quizbase_list using the `regions:[...]` parameter, which is concrete cross-tool 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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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: categories, languages, list, question_by_id, random, regions, report, stats, subcategories, tags, topic_by_slug, and topics. Descriptions explicitly guide when to use which, avoiding ambiguity even between similar tools like quizbase_list and quizbase_random.

Naming Consistency5/5

All tools follow a consistent 'quizbase_' prefix followed by a noun or noun phrase, with underscores for compound names like question_by_id and topic_by_slug. This pattern makes it easy to predict tool names based on function.

Tool Count5/5

With 12 tools, the set is well-scoped for a trivia question database. It covers metadata discovery, question browsing, random sampling, reporting, and statistics without unnecessary bloat or missing essentials.

Completeness5/5

The tool set provides comprehensive read access and reporting for the catalog: metadata (categories, languages, regions, stats, taxonomy), question retrieval (list, random, by ID), and issue submission. No obvious gaps for typical consumption workflows.

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