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generate_video_quiz

Create interactive quizzes from YouTube video content to test comprehension and reinforce learning through structured questions.

Instructions

Generate a quiz based on the video content.

Args: video_id: YouTube video ID

Returns: A formatted quiz with 10 questions of various types

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
video_idYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates a quiz with 10 questions of various types, which adds some context beyond the basic purpose. However, it lacks details on permissions, rate limits, error handling, or whether it's a read-only or mutation operation (though 'generate' suggests creation, this isn't clarified). For a tool with zero annotation coverage, this is a significant gap.

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 appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections add structure, though they could be more integrated. There's no wasted text, but the formatting is slightly verbose for such a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (simple generation with one parameter), no annotations, no output schema, and low schema coverage, the description is somewhat complete but has gaps. It covers the purpose and return format, but lacks behavioral details and parameter nuances. It's adequate as a minimum viable description but not fully comprehensive.

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?

The description adds minimal meaning beyond the input schema. It specifies that 'video_id' is a 'YouTube video ID,' which provides context not in the schema (which has 0% description coverage and only lists 'Video Id' as the title). However, with only one parameter, the baseline is 4, but the description doesn't fully compensate for the low schema coverage by explaining format or constraints (e.g., length, validation). Thus, it's scored slightly below baseline.

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?

The description clearly states the tool's purpose: 'Generate a quiz based on the video content.' It specifies the verb ('generate') and resource ('quiz'), and distinguishes it from sibling tools like 'generate_video_flashcards' or 'summarize_video' by focusing on quiz creation. However, it doesn't explicitly differentiate from all siblings (e.g., 'get_video_info' might also process video content), so it's not a perfect 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose it over 'generate_video_flashcards' or other video-related tools, nor does it specify prerequisites or exclusions. The context is implied (video-based quiz generation), but explicit usage instructions are absent.

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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