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search.unified

Read-onlyIdempotent

Perform cross-component semantic search across layouts, UI parts, motion patterns, background designs, and narratives by executing parallel searches and merging results by similarity score.

Instructions

Layout(セクション)・Part(UIコンポーネント)・Motion(アニメーション)・Background(背景デザイン)・Narrative(世界観)を横断的にセマンティック検索します。個別検索ツールを並列実行し、結果をsimilarityスコア順にマージして返却します。 / Cross-component semantic search across Layout sections, UI Parts, Motion patterns, Background designs, and Narratives. Executes individual search tools in parallel and merges results by similarity score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes検索クエリ(自然言語、1-500文字) / Search query (natural language, 1-500 chars)
typesNo検索対象タイプ(デフォルト: 全タイプ) / Target types (default: all types)
limitNo取得件数(1-50、デフォルト: 10) / Result limit (1-50, default: 10)
webPageIdNoWebページIDでフィルター / Filter by web page ID
industryNo業種フィルター / Industry filter (e.g., 'SaaS', 'E-commerce')
audienceNoターゲットオーディエンスフィルター / Target audience filter (e.g., 'Developer', 'Enterprise')
tagsNoタグフィルター / Tags filter
profile_idNo嗜好プロファイルID(検索結果のリランキングに使用) / Preference profile ID
enable_rerankingNoCross-Encoderリランキング有効化(デフォルト: true) / Enable Cross-Encoder reranking (default: true)
query_typeNoクエリタイプ(auto: 自動分類、visual: 見た目、structural: レイアウト構造、functional: 機能、stylistic: スタイル) / Query type (auto: auto-classify)auto
include_facetsNoファセットカウント付与(デフォルト: false)。trueにすると sectionType/industry/audience/tags のカウントを返却 / Include facet counts (default: false). Returns counts for sectionType/industry/audience/tags when true
facet_fieldsNoファセットフィールド指定(指定時はinclude_facetsが暗黙的にtrue)。未指定時は全4フィールド / Facet fields to compute (implicitly enables include_facets). Defaults to all 4 fields when omitted. sectionType: セクション/パーツタイプ, industry: 業種, audience: ターゲット, tags: タグ
Behavior4/5

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

The description adds value beyond annotations (readOnlyHint, idempotentHint) by detailing the parallel execution and merging by similarity score. It discloses key behavioral traits like semantic search and cross-component scope. However, it omits potential side effects or performance considerations, but for a read-only, idempotent operation this is acceptable.

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 extremely concise, with only two sentences (Japanese and English) that front-load the key purpose and mechanism. Every word is informative and no extraneous content exists.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the tool having 12 parameters with extensive filtering, faceting, reranking, and query type options, the description only covers cross-component search and merging. It fails to mention key capabilities like filtering by webPageId, industry, tags, or the inclusion of facets and reranking, leaving agents unaware of the full functionality without examining the schema.

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 100%, with each parameter having clear descriptions. The tool description does not add additional meaning beyond what the schema provides. Baseline score of 3 is appropriate as the schema already handles parameter documentation.

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 it performs cross-component semantic search across five domains (Layout, Parts, Motion, Background, Narrative) and explains it executes individual search tools in parallel and merges results by similarity score. This distinguishes it from sibling single-domain search tools like layout.search or motion.search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

While the description implies it is used for multi-domain searches, it does not explicitly state when to use this tool versus individual search tools. No direct guidance on exclusions or prerequisites is provided, relying on the agent to infer from sibling context.

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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