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review_practice_attempt

Save agent feedback and next steps for a historical practice attempt, tracking user verdicts and ensuring version control. Does not replace automatic grading.

Instructions

保存对某次历史作答的Agent反馈及后续建议,不能替代自动判分或掌握判定。先read_practice_history(include_details=true)核对当时题面和作答,再read_practice_feedback取得current_feedback_revision(首次0)。feedback.agent_verdict是Agent分析,user_verdict默认unreported,只有用户明确表达时填写并忠实记录user_note。本次无新增用户意见时,unreported和空user_note保留此前意见;仅用户明确撤回时用withdraw_user_opinion=true及非空撤回说明。user_opinion_source_revision追溯用户意见来源,沿用不表示再次发言。版本冲突先读取,修订保留旧反馈;不会改写作答、题目或原卷批改。旧作答无快照时拒绝自动补判。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feedbackYes
attempt_idYes
expected_feedback_revisionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only declare readOnly=false, idempotent=false, destructive=false, openWorld=false, so the description carries real additional weight: it will not rewrite the answer, question, or original grading, refuses to auto re-grade attempts lacking a snapshot, and preserves the old feedback on a revision. The withdraw mechanism and its constraints are also disclosed. This is substantial context beyond the annotations and is fully consistent with them.

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?

Purpose is front-loaded, followed by prerequisites, then per-field semantics and edge cases, so ordering is logical. The prose is dense and run-on in places, and mentions user_opinion_source_revision, which does not appear in the input schema. Every sentence still carries distinct information, so waste is low.

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, return values needn't be described, and the description instead covers the workflow, defaults, conflict handling, and mutation limits an agent needs. The only gap is the reference to user_opinion_source_revision, which is absent from the schema, creating a small traceability mismatch.

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 description coverage is 0% (only withdraw_user_opinion carries an inline note), so the description must compensate and largely does: it defines agent_verdict as the Agent's analysis, sets user_verdict's default of unreported and the condition for filling it, explains user_note fidelity rules, and clarifies withdraw_user_opinion's required companion fields. The attempt_id and expected_feedback_revision semantics are covered only indirectly via 'current_feedback_revision'.

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 opening states a specific verb and resource: saving Agent feedback plus follow-up suggestions onto a historical practice attempt, and immediately bounds it ('不能替代自动判分或掌握判定'). It effectively separates itself from siblings by naming read_practice_history and read_practice_feedback as adjacent-but-different tools. It stops short of a 5 only because the tool name ('review_') slightly undersells that the action is a save/write.

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?

Explicit when-to-use and prerequisites: call read_practice_history(include_details=true) to verify the original prompt/answer, then read_practice_feedback to obtain current_feedback_revision (0 on first call). It also gives conditional rules for edge cases — fill user_verdict only on explicit user statements, use withdraw_user_opinion=true only on explicit withdrawal, and read first on version conflict.

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