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grade_student_question

Score a student's answer to a specific question with an optional comment, supporting short answer and attachment question types.

Instructions

[批改 3/4] 给学生某道题打分。

何时调用:仅简答题(type=6)和附件题(type=7)需要;选择/填空/判断/编程系统自动评分,跳过。
score 上限 = query_preview_student_paper 返回的该题 score 字段;未 submit 前可重复打分覆盖。
四步流程:query_test_result → query_preview_student_paper → grade_student_question → submit_student_mark。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
group_idYes课程组id(通过 query_teacher_groups 获取)
publish_idYes发布id(来自 query_group_tasks 的 publish_id 字段)
mark_paper_record_idYes批阅记录id(来自 query_preview_student_paper 的 mark_paper_record_id 字段)
record_idYes答题记录id(来自 query_test_result 的 answer_records[].record_id 字段)
question_idYes题目id(通过 query_paper 获取)
answer_idYes学生单题答案id(来自 query_preview_student_paper 的 questions[].answer_id 字段)
scoreYes批改得分(0 ≤ score ≤ 该题满分,满分从 query_preview_student_paper 的 questions[].score 取)
commentNo批改评语(可为空)
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses the score constraint (0 to max from query_preview_student_paper), that scoring can be overwritten before submission, and the four-step process. However, it lacks details on auth requirements, error handling, or side effects beyond scoring. Still, it provides significant behavioral context.

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 concise (three lines) and well-structured with bullet points for when to call, score limit, and process flow. Every sentence adds value, and the most critical information is front-loaded. No wasted words.

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 8 parameters, no output schema, and no annotations, the description covers the tool's purpose, usage scope, parameter provenance, and integration into a larger workflow. It provides sufficient context for an AI agent to use the tool correctly within the grading pipeline.

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?

Schema coverage is 100%, but the description adds value by explaining the score limit in relation to another tool's output and the overall workflow. It clarifies where each parameter comes from (e.g., group_id from query_teacher_groups), which goes beyond the schema descriptions. This enhances understanding of parameter usage.

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 student's question for specific types (short answer and attachment). It specifies the score limit and that it can be repeated before submission, distinguishing it from automated grading for other question types. This provides a specific verb and resource with clear scope.

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 explicitly states when to call (only for short answer type=6 and attachment type=7) and when not to (other types are auto-graded). It also outlines a four-step workflow: query_test_result → query_preview_student_paper → grade_student_question → submit_student_mark, providing clear context and alternatives.

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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