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jp_lit_refine_results

Refine saved Japanese literature search results locally: sort, filter by source/date/title/author, or combine multiple cache keys using set operations. Optionally retrieve duplicate clusters for deduplication analysis.

Instructions

read-only。保存済み jp_lit_search 結果を upstream 再検索せずローカルでソート・フィルタ・集合演算し、必要時だけ重複候補クラスタも返す。cache_key が分かっている結果を再評価するときに使い、cache_key を探す段階では jp_lit_search_cache_index または jp_lit_list_cache を使う。cache や session は変更しない

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cache_keyNo再抽出する単一の jp_lit_search cache_key。cache_keys と同時指定しない。
cache_keysNo集合演算する複数の jp_lit_search cache_key。
session_idNoセッションに紐づく検索 cache を対象にする場合のセッションID。
combineNo複数 cache の集合演算。union は和集合、intersection は積集合、minus は先頭から後続を除外する。union
key_byNo集合演算時に item を同一視するキー。source_record は source+source_id、duplicate_key は正規化重複キー、title_author_year はタイトル著者年。source_record
sort_byNo再抽出結果の並び替え項目。未指定なら元の順序を保つ。
sort_orderNosort_by 指定時の並び順。asc
limitNo返す item の最大件数。最大 200。
offsetNo返す item の offset。0 始まり。
include_duplicate_clustersNotrue の場合は重複候補クラスタを追加で返す。
cluster_limitNo返す重複クラスタの最大件数。
cluster_offsetNo重複クラスタ一覧の offset。0 始まり。
cluster_member_limitNo各重複クラスタで preview する member の最大件数。
filtersNo保存済み結果に対するローカル filter。upstream 再検索は行わない。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
base_cache_keyYes
base_cache_keysYes
combineYes
key_byYes
totals_by_baseYes
total_beforeYes
total_afterYes
limitYes
offsetYes
itemsYes
cluster_summaryNo
clustersNo
Behavior4/5

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

No annotations provided, so the description carries full burden. It discloses that the tool is read-only, does not modify cache or session, and operates locally without upstream re-search. It also mentions returning duplicate candidate clusters when needed. However, it lacks details on potential side effects, error conditions, or permissions, but the key behavioral traits are covered.

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 composed of three clear sentences, front-loaded with the key concept 'read-only' and main functionality. No redundant phrases, and every sentence adds value, making it highly concise and well-structured.

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?

The tool has 14 parameters, a nested filters object, and an output schema (not shown). The description gives a high-level overview but does not explain the combine operations or cluster details; however, the input schema descriptions are comprehensive. Given the complexity and completeness of the schema, the description is sufficient for an AI agent to understand the core purpose and usage.

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 coverage is 100% with detailed parameter descriptions in the input schema. The description adds high-level context (local sort/filter/set operations) but does not significantly enhance understanding beyond what the schema already provides. Baseline 3 is appropriate.

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 'read-only' and specifies that the tool performs local sort, filter, and set operations on saved jp_lit_search results without upstream re-search. It distinguishes itself from sibling tools like jp_lit_search_cache_index and jp_lit_list_cache by stating when to use each, fulfilling the purpose clarity dimension.

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 use this tool ('cache_key が分かっている結果を再評価するときに使い') and when not to ('cache_key を探す段階では jp_lit_search_cache_index または jp_lit_list_cache を使う'). This provides clear guidance on 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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