{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_struct","slug":"struct","name":"Struct","type":"product","url":"https://www.struct.ai","page_url":"https://unfragile.ai/struct","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_struct__cap_0","uri":"capability://search.retrieval.semantic.vector.search.with.embedding.indexing","name":"semantic-vector-search-with-embedding-indexing","description":"Converts unstructured text documents into dense vector embeddings and indexes them in a vector database, enabling semantic similarity search that retrieves results based on meaning rather than keyword matching. Uses embedding models (likely OpenAI or similar) to transform documents and queries into comparable vector space, then performs approximate nearest-neighbor search to return contextually relevant results ranked by cosine similarity or similar distance metrics.","intents":["I want users to find relevant documentation answers even when they don't use exact keywords","I need to build a semantic search layer on top of my existing knowledge base without managing a separate vector database","I want to reduce false negatives in FAQ/documentation search by understanding intent rather than matching terms"],"best_for":["Product teams building searchable documentation for SaaS products","Support teams wanting to surface relevant help articles based on customer intent","Small-to-medium companies without dedicated ML/data infrastructure"],"limitations":["No published performance benchmarks for knowledge bases >100k documents; unclear scaling characteristics","Embedding model choice and dimensionality not customizable — locked to platform defaults, limiting fine-tuning for domain-specific vocabularies","Latency for vector search operations not disclosed; typical semantic search adds 50-500ms per query depending on index size","No hybrid search (combining keyword + semantic) explicitly mentioned, forcing choice between retrieval strategies"],"requires":["Structured text documents or markdown content","Internet connectivity for embedding API calls (if using cloud embeddings)","Knowledge base content in supported formats (likely markdown, HTML, or plain text)"],"input_types":["text","markdown","HTML"],"output_types":["ranked search results with relevance scores","structured metadata (document title, URL, snippet)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_struct__cap_1","uri":"capability://text.generation.language.seo.optimized.knowledge.page.generation","name":"seo-optimized-knowledge-page-generation","description":"Automatically generates or transforms indexed knowledge base content into SEO-optimized HTML pages with structured metadata (meta tags, Open Graph, schema markup), heading hierarchy, and internal linking suggestions. Likely uses templates and heuristics to inject keywords, optimize title/description length, and structure content for search engine crawlability while maintaining readability. Pages are generated from indexed vector content, creating a feedback loop where search-relevant documents become discoverable pages.","intents":["I want my knowledge base to rank in Google search results without hiring an SEO specialist","I need to automatically generate meta descriptions and title tags for hundreds of documentation pages","I want internal linking suggestions to improve site structure and SEO authority distribution"],"best_for":["SaaS companies wanting organic search traffic to their documentation","Product teams without dedicated SEO or content operations resources","Companies with large documentation sets needing bulk SEO optimization"],"limitations":["No control over generated meta tags or schema markup — optimization rules are opaque and not customizable per page type","Internal linking suggestions may not respect content hierarchy or user journey; purely algorithmic without editorial review workflow","No A/B testing framework for SEO variations; single optimization path per page","Unclear how it handles multi-language content or regional SEO variations (hreflang tags, geo-targeting)"],"requires":["Indexed knowledge base content (requires semantic-vector-search capability first)","Public-facing documentation domain or subdomain","Content in supported formats with extractable titles and structure"],"input_types":["indexed knowledge base documents","content metadata (title, URL, category)"],"output_types":["HTML pages with SEO metadata","structured data (JSON-LD schema)","sitemap.xml entries","internal linking recommendations"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_struct__cap_2","uri":"capability://data.processing.analysis.knowledge.base.content.ingestion.and.indexing","name":"knowledge-base-content-ingestion-and-indexing","description":"Accepts unstructured knowledge base content (documentation, FAQs, help articles) in multiple formats and automatically parses, chunks, and indexes it into the vector search system. Likely uses document parsing libraries to extract text from markdown/HTML, applies chunking strategies (sliding windows, semantic boundaries) to create indexable units, and batches embedding generation. Metadata extraction (title, URL, category) is preserved for ranking and filtering.","intents":["I want to upload my existing Notion/Confluence documentation and immediately make it searchable","I need to ingest markdown files from my GitHub docs repository without manual preprocessing","I want to continuously sync my knowledge base as content updates without re-indexing everything"],"best_for":["Teams migrating from unstructured documentation to searchable knowledge bases","Companies with existing markdown or HTML documentation needing indexing","Support teams managing FAQs across multiple formats"],"limitations":["Chunking strategy not configurable — fixed chunk size/overlap may not suit domain-specific content (code examples, tables, long-form prose need different strategies)","No explicit support for structured data extraction (tables, code blocks, lists) — likely treats all content as flat text, losing semantic structure","Batch ingestion latency not published; unclear if real-time indexing is supported or if updates require full re-indexing","No deduplication or conflict resolution for duplicate content across sources"],"requires":["Knowledge base content in supported formats (markdown, HTML, plain text, possibly PDF)","Accessible source (file upload, API endpoint, or direct paste)","Reasonable content size (no published limits on total knowledge base size)"],"input_types":["markdown","HTML","plain text","possibly PDF"],"output_types":["indexed vector embeddings","chunked documents with metadata","searchable knowledge base"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_struct__cap_3","uri":"capability://search.retrieval.metadata.filtering.and.faceted.search","name":"metadata-filtering-and-faceted-search","description":"Enables filtering search results by document metadata (category, tags, author, date, URL path) and supports faceted navigation to narrow results without re-querying. Likely stores metadata alongside embeddings and applies post-retrieval filtering or pre-filters the vector search space. Facets are dynamically generated from indexed content, allowing users to explore knowledge base structure without keyword queries.","intents":["I want users to filter search results by documentation category (API docs, tutorials, troubleshooting)","I need to show facets (tags, authors, date ranges) to help users narrow results","I want to restrict search to specific sections of my knowledge base (e.g., product-specific docs)"],"best_for":["Large knowledge bases with rich metadata and hierarchical structure","Support teams needing to surface category-specific help articles","Multi-product companies wanting to isolate search by product line"],"limitations":["Metadata schema not customizable — limited to predefined fields; no way to add domain-specific metadata (severity, status, audience level)","Facet generation strategy not documented; unclear if facets are pre-computed or generated on-demand, affecting query latency","No support for complex filters (AND/OR logic, range queries on numeric metadata) — likely simple equality matching only","Filtering happens post-retrieval, potentially wasting vector search compute on irrelevant results"],"requires":["Indexed knowledge base with extractable metadata","Metadata fields populated during ingestion (title, category, tags, etc.)"],"input_types":["search query","filter parameters (category, tags, date range)"],"output_types":["filtered search results","facet counts and options"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_struct__cap_4","uri":"capability://search.retrieval.search.result.ranking.and.relevance.tuning","name":"search-result-ranking-and-relevance-tuning","description":"Ranks search results by relevance using vector similarity scores and optional secondary signals (metadata recency, document popularity, click-through data). Likely uses cosine similarity or dot-product scoring on embeddings, with optional boosting for high-quality or frequently-accessed documents. Relevance tuning may expose simple controls (boost by category, date decay) without requiring model retraining.","intents":["I want the most relevant documentation to appear first, not just the most recent","I need to boost certain high-quality articles (official guides) over user-generated content","I want to deprioritize outdated or deprecated documentation"],"best_for":["Teams wanting to improve search relevance without ML expertise","Support teams needing to surface official documentation first","Companies with mixed-quality content needing curation through ranking"],"limitations":["Ranking signals and weights not exposed — no visibility into how results are scored or ability to customize weights","No learning-to-rank capability; ranking rules are static and don't adapt based on user feedback or clicks","Secondary signals (recency, popularity) likely require manual configuration; no automatic signal detection","No A/B testing framework for ranking variations"],"requires":["Indexed knowledge base with embeddings","Optional: metadata for secondary ranking signals (date, popularity metrics)"],"input_types":["search query","indexed documents with embeddings"],"output_types":["ranked search results with relevance scores"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_struct__cap_5","uri":"capability://data.processing.analysis.multi.source.knowledge.base.consolidation","name":"multi-source-knowledge-base-consolidation","description":"Ingests and indexes knowledge content from multiple sources (uploaded files, API endpoints, web URLs, connected platforms) into a unified searchable index. Likely maintains source attribution and deduplication logic to prevent indexing the same content twice. Supports incremental updates from sources without full re-indexing, enabling continuous synchronization with external knowledge bases.","intents":["I want to search across documentation from multiple platforms (Notion, GitHub, Zendesk) in one place","I need to keep my knowledge base in sync as content updates in the source systems","I want to consolidate FAQs from multiple teams without manually copying content"],"best_for":["Organizations with fragmented knowledge across multiple platforms","Teams using multiple documentation tools (Notion for internal, GitHub for code docs, Zendesk for support)","Companies needing to consolidate knowledge during platform migrations"],"limitations":["Supported source integrations not clearly documented — unclear which platforms (Notion, Confluence, Zendesk, etc.) have native connectors vs. requiring manual upload","Deduplication strategy not specified; unclear how conflicts are resolved when the same content exists in multiple sources","Sync frequency and latency not published; unclear if real-time sync is supported or if updates batch on a schedule","No conflict resolution or versioning — if content diverges across sources, unclear which version is indexed"],"requires":["Access credentials for source systems (API keys, OAuth tokens)","Source systems in supported integrations","Reasonable content volume across sources (no published limits)"],"input_types":["multiple content sources (files, APIs, web URLs, platform connectors)"],"output_types":["unified indexed knowledge base","source attribution metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_struct__cap_6","uri":"capability://automation.workflow.knowledge.page.public.hosting.and.distribution","name":"knowledge-page-public-hosting-and-distribution","description":"Hosts generated knowledge pages on a public-facing domain with automatic URL routing, custom branding, and optional white-label options. Pages are served with SEO metadata, structured data, and analytics tracking. Likely uses a CDN for fast global delivery and supports custom domain configuration. Pages are dynamically generated from indexed content or pre-rendered for performance.","intents":["I want to publish my knowledge base as a public-facing website without managing hosting infrastructure","I need to white-label the knowledge base with my company branding and custom domain","I want to track which documentation pages users visit and how they search"],"best_for":["SaaS companies wanting a public knowledge base without custom hosting","Teams needing white-label documentation for customers or partners","Companies wanting to reduce infrastructure overhead for documentation hosting"],"limitations":["Custom domain setup requires DNS configuration; no guidance on SSL certificate management or CDN configuration","White-label options not detailed — unclear if branding is limited to logo/colors or if full design customization is possible","Analytics tracking limited to page views and search queries; no user behavior analytics (scroll depth, time-on-page, click patterns)","No version control or staging environment — unclear if changes are published immediately or if there's a review workflow"],"requires":["Generated knowledge pages from SEO optimization capability","Custom domain (optional; default subdomain provided)","DNS access for custom domain configuration"],"input_types":["generated knowledge pages with metadata"],"output_types":["public-facing HTML pages","analytics data (page views, search queries)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_struct__cap_7","uri":"capability://data.processing.analysis.search.analytics.and.query.insights","name":"search-analytics-and-query-insights","description":"Tracks search queries, click-through rates, and user engagement with search results to identify gaps in knowledge base coverage and popular search intents. Likely logs queries, result selections, and page dwell time, then surfaces aggregated insights (top queries, zero-result queries, trending topics). May use these signals to recommend new content or identify documentation gaps.","intents":["I want to see what users are searching for to identify missing documentation","I need to find queries that return no results so I can add relevant content","I want to understand which documentation pages are most helpful based on search behavior"],"best_for":["Product and support teams wanting data-driven documentation improvements","Content teams needing to prioritize which topics to document","Companies wanting to measure documentation effectiveness"],"limitations":["Analytics granularity not specified — unclear if data is available at query, user, or session level","Privacy considerations not addressed — no mention of PII handling, GDPR compliance, or data retention policies","No real-time alerting for zero-result queries or search anomalies; insights likely require manual dashboard review","No integration with content management systems to automatically create tasks for missing documentation"],"requires":["Active search usage (requires search-retrieval capability)","User consent for analytics tracking (privacy/compliance)"],"input_types":["search queries","result clicks","page engagement metrics"],"output_types":["analytics dashboard","query insights (top queries, zero-result queries, trends)","content gap recommendations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_struct__cap_8","uri":"capability://tool.use.integration.api.based.search.integration","name":"api-based-search-integration","description":"Exposes search functionality via REST or GraphQL API, enabling embedding semantic search into custom applications, chatbots, or internal tools without using the hosted UI. API returns ranked results with metadata and relevance scores in structured JSON format. Supports pagination, filtering, and optional result customization (snippet length, fields returned). Authentication uses API keys or OAuth.","intents":["I want to embed semantic search into my product's help widget or chatbot","I need to query the knowledge base from a custom application or internal tool","I want to build a custom search UI with different UX than the default Struct interface"],"best_for":["Developers building custom search experiences or integrations","Teams wanting to embed search in chatbots or AI agents","Companies needing programmatic access to knowledge base content"],"limitations":["API documentation and rate limits not published — unclear if there are throttling constraints or pricing tiers based on API usage","No webhook support for real-time search result notifications or event streaming","Response format and available fields not documented — unclear what metadata is returned or if it's customizable","No batch search API — each query requires a separate API call, potentially inefficient for bulk operations"],"requires":["API key or authentication credentials","Indexed knowledge base (requires ingestion capability)","HTTP client library or SDK (if provided)"],"input_types":["search query (text)","filter parameters (optional)","pagination parameters (optional)"],"output_types":["JSON-formatted search results","ranked documents with relevance scores","metadata (title, URL, category, snippet)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Structured text documents or markdown content","Internet connectivity for embedding API calls (if using cloud embeddings)","Knowledge base content in supported formats (likely markdown, HTML, or plain text)","Indexed knowledge base content (requires semantic-vector-search capability first)","Public-facing documentation domain or subdomain","Content in supported formats with extractable titles and structure","Knowledge base content in supported formats (markdown, HTML, plain text, possibly PDF)","Accessible source (file upload, API endpoint, or direct paste)","Reasonable content size (no published limits on total knowledge base size)","Indexed knowledge base with extractable metadata"],"failure_modes":["No published performance benchmarks for knowledge bases >100k documents; unclear scaling characteristics","Embedding model choice and dimensionality not customizable — locked to platform defaults, limiting fine-tuning for domain-specific vocabularies","Latency for vector search operations not disclosed; typical semantic search adds 50-500ms per query depending on index size","No hybrid search (combining keyword + semantic) explicitly mentioned, forcing choice between retrieval strategies","No control over generated meta tags or schema markup — optimization rules are opaque and not customizable per page type","Internal linking suggestions may not respect content hierarchy or user journey; purely algorithmic without editorial review workflow","No A/B testing framework for SEO variations; single optimization path per page","Unclear how it handles multi-language content or regional SEO variations (hreflang tags, geo-targeting)","Chunking strategy not configurable — fixed chunk size/overlap may not suit domain-specific content (code examples, tables, long-form prose need different strategies)","No explicit support for structured data extraction (tables, code blocks, lists) — likely treats all content as flat text, losing semantic structure","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.559Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=struct","compare_url":"https://unfragile.ai/compare?artifact=struct"}},"signature":"hW2IqI4Ax6lujC/EcIYZrwQhqYwmmwrnb2ucHhSEHPr0CZnSqzsFqJ3XiQI03gNFHjLAvZy0axDjeGUpQ4KYCw==","signedAt":"2026-06-20T17:16:28.225Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/struct","artifact":"https://unfragile.ai/struct","verify":"https://unfragile.ai/api/v1/verify?slug=struct","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}