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quolu
by quolu

claude_approval

Inspect Claude's permission approval UI during active turns, then respond with approve_once or deny only when the same operation and digest are still shown.

Instructions

aiterm相関付きClaudeのactive turn中に表示された権限確認UIを、turn相関を保ったまま検査・応答する専用面。inspectで画面digestと安全な単発Yes/Noだけを取得し、respondは同じoperation・同じdigestが現在も表示中の場合だけ送信する。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
session_idYes
operation_idNodurable operationのID。通常pty_send由来の匿名turnでは省略する
approval_choiceNorespondだけに指定する
observed_prompt_digestNo直前のinspectが返したdigest。respondだけに指定する

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
atYes
actionYes
schemaYes
statusYes
choicesYes
session_idYes
operation_idYes
prompt_digestYes
selected_choiceYes
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that inspect returns only the screen digest and safe single Yes/No, and that respond is conditional on matching operation and current digest. This provides meaningful behavioral context, though it does not cover all error cases or side effects of responding.

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 a single dense sentence that efficiently conveys the main purpose, actions, and safety condition without wasted words. It is compact and front-loaded, though the multiple clauses make it slightly complex to parse.

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?

An output schema exists, so return values need not be described. The description covers the two actions, what inspect returns, and the precondition for respond, which is sufficient for a specialized approval tool. It lacks details on mismatch handling or how to obtain session_id, but these are partially addressed by the schema hints.

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?

Schema description coverage is 60%, and the schema already documents the optional parameters (operation_id, approval_choice, observed_prompt_digest) with useful descriptions. The tool description adds context for the action enum (inspect vs respond) but does not elaborate on individual parameters beyond what the schema provides. It adds some value but not extensive.

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's function as a dedicated surface for inspecting and responding to permission confirmation UI during an active Claude turn, preserving turn correlation. It distinguishes itself from sibling PTY tools by focusing on the approval context and the specific inspect/respond actions.

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 specifies the intended scenario (permission UI during active Claude turn) and the safe usage pattern: inspect only retrieves a digest and simple Yes/No, while respond is only sent when the same operation and digest are still displayed. It implies this is the correct tool for approvals but does not explicitly name alternatives like pty_send or state exclusions.

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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