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jp_lit_prune_cache

Destructive

List and delete old cache entries for Japanese literature research tools, with a dry-run mode to preview candidates before actual cleanup.

Instructions

delete when dry_run=false。古いローカル cache 候補を列挙し、既定の dry_run=true では削除せず候補だけ返す。dry_run=false のときだけ older_than_days と limit に一致する cache を削除する。個別 cache_key を削除する場合は jp_lit_delete_cache、一覧確認だけなら jp_lit_list_cache を使う

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolNo対象 tool cache 名。未指定なら全 tool cache を対象にする。
limitNo列挙または削除する候補の最大件数。最大 1000。
dry_runNotrue なら削除せず候補だけ返す。false のときだけ実際に古い cache を削除する。
older_than_daysNoこの日数より古い cache を prune 候補にする。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYes
limitYes
dry_runYes
messageYes
skippedYes
candidatesYes
total_bytesYes
pruned_countYes
matched_countYes
skipped_countYes
cutoff_saved_atYes
older_than_daysYes
Behavior4/5

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

Annotations include destructiveHint=true, which aligns with the description's mention of deletion when dry_run=false. The description adds context about the default non-destructive behavior, but lacks details on error handling or side effects, which is acceptable given the annotation coverage.

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?

Two concise sentences that are front-loaded with the key behavior. Every sentence adds value, with no redundant information. Efficiently communicates purpose, usage, and alternatives.

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 the presence of an output schema and annotations, the description covers the essential behavior, parameter interactions, and alternative tools. It is complete for an agent to use this tool appropriately.

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% with all parameters described. The description adds meaning beyond schema by explaining the workflow: enumeration vs deletion based on dry_run, and the role of older_than_days and limit. This enhances parameter understanding.

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?

Description clearly states the tool enumerates old cache candidates and deletes them when dry_run=false. It distinguishes from sibling tools jp_lit_delete_cache (individual deletion) and jp_lit_list_cache (listing), making the purpose unambiguous.

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?

Explicitly provides guidance on when to use this tool (pruning old caches) and when to use alternatives. It explains the dry_run parameter behavior, ensuring the agent knows when deletion actually occurs.

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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