Skip to main content
Glama

search.unified

Read-onlyIdempotent

Search across layouts, UI components, motion patterns, backgrounds, and narratives using natural language. Merges parallel semantic queries and ranks results by similarity score with filtering and reranking.

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?

Annotations declare readOnlyHint and idempotentHint. The description adds valuable behavioral context not in annotations: it discloses the internal parallel execution model and similarity-score merging logic. It does not contradict annotations (searching/merging aligns with read-only). Could improve by mentioning latency implications of parallel execution or cache behavior.

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?

Despite being bilingual (Japanese/English), the description is extremely efficient: two sentences per language with zero waste. First sentence establishes scope (five domains), second explains mechanism (parallel execution + merge). Information is front-loaded and every clause earns its place.

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?

For a complex 12-parameter cross-domain tool, the description provides sufficient high-level context given the rich annotations and complete schema. It explains the merging behavior adequately. Minor gap: no output schema exists, and the description doesn't detail return structure (e.g., whether it returns unified objects or typed collections), though it implies similarity-scored results.

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?

With 100% schema description coverage, the schema fully documents all 12 parameters including the five type enums. The description mentions the five component types (layout, part, motion, background, narrative) which reinforces the 'types' parameter semantics, but does not need to duplicate the comprehensive schema documentation. Baseline 3 is appropriate given schema completeness.

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 precisely defines the tool's scope using specific verbs ('cross-component semantic search') and enumerates all five searchable domains (Layout, Part, Motion, Background, Narrative). It clearly distinguishes this as an aggregator that 'executes individual search tools in parallel,' differentiating it from sibling single-domain tools like layout.search or part.search.

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 implies usage context by stating it executes individual search tools in parallel and merges results, suggesting this is for cross-cutting queries rather than single-domain searches. However, it lacks explicit 'when to use unified vs. specific tools' guidance (e.g., 'use this when searching across multiple component types, use layout.search for layout-only queries').

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/TKMD/reftrix-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server