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klax_submit_quiz_answer

Submit a quiz answer for instant grading, receive correct answer and explanation, and automatically adjust review schedule using SM-2 spaced repetition.

Instructions

생성된 퀴즈(quiz_id)에 대한 답안(user_answer, options 배열의 인덱스)을 채점합니다.

채점 결과(정답 여부, 정답 인덱스, 해설)를 반환하고, 결과에 따라 에빙하우스 망각곡선 기반 복습 큐(review_queue)의 간격을 SM-2 변형 알고리즘으로 자동 갱신합니다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
quiz_idYes
user_answerYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the side effect of automatically updating the review queue interval using the SM-2 variant algorithm, and mentions it returns grading results. However, it does not address error handling, idempotency, or authentication requirements, which are relevant for a mutation tool.

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 two concise sentences. The first sentence states the core action, and the second explains the return and side effect. It is front-loaded with the primary purpose and contains no redundant text.

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

Completeness4/5

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

With an output schema present and only two parameters, the description covers the essential purpose, the side effect on the review queue, and a key parameter clarification. It lacks explicit error conditions or additional behavioral details, but these are not critical given the simplicity and the presence of an output schema.

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

Parameters4/5

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

With 0% schema description coverage, the description compensates by explaining that user_answer is the index in the options array, which the schema does not provide. quiz_id is self-explanatory as the identifier of the generated quiz. This adds meaningful context beyond the schema.

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 states the tool grades a user answer for a quiz, specifying the verb (grade), resource (quiz answer), and key input semantics (user_answer is an index into the options array). It also mentions the return of grading results and the side effect on the review queue, which distinguishes it from sibling tools like quiz generation or review queue retrieval.

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 when to use it (when you have a quiz_id and a user answer) and clarifies the input format. However, it does not explicitly state alternatives or exclusions, though the context makes it clear this is for submitting answers, not for generating quizzes or retrieving the queue.

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