klax_generate_quiz

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

With no annotations provided, the description fully carries the burden of behavioral disclosure. It discloses important behaviors: answers/explanations are withheld until submission, quizzes are persisted to study.db, and auto-enrollment in the forgetting curve queue. It also notes the deterministic rule-based nature. It could mention side effects of storage and queue enrollment more explicitly, but it does mention them. The only minor gap is not describing the output schema in detail, but that is not required since output schema exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the core action. It uses a clear structure: first sentence states the action and method, second sentence adds key behavioral details (withholding answers, submission flow), third sentence adds persistence and queue behavior. No filler or redundant content. It could be slightly more structured with bullet points, but it's effective as is.

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

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

Given the tool's moderate complexity (2 params, no annotations, output schema exists), the description covers the essential aspects: what it does, how it works (deterministic rules), key behavioral side effects (persistence, queue enrollment), and the workflow with klax_submit_quiz_answer. It doesn't describe the output schema shape, but that's provided by the output schema already. It could mention prerequisites like needing to index materials first, but that's a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains that chunk_id refers to a locally indexed lecture chunk (adding meaning to the parameter), and course_id is implied as the course context. It does not detail the format or constraints, but given the simplicity of the parameters, the description provides enough context to understand their purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (generate), the resource (a quiz), and the specific basis (deterministic rules from indexed lecture chunks). It also differentiates from siblings like klax_submit_quiz_answer and klax_get_review_queue by specifying that quiz generation is one quiz per chunk and not for other purposes.

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

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

The description implies when to use this tool: when a quiz needs to be generated from a specific chunk, and it explicitly mentions the workflow with klax_submit_quiz_answer. However, it does not explicitly state when not to use it or mention alternatives for summarization (e.g., klax_generate_study_guide_html), so it misses explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.