{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-unstructured","slug":"unstructured","name":"Unstructured","type":"mcp","url":"https://github.com/Unstructured-IO/UNS-MCP","page_url":"https://unfragile.ai/unstructured","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-unstructured__cap_0","uri":"capability://tool.use.integration.mcp.based.document.ingestion.pipeline.orchestration","name":"mcp-based document ingestion pipeline orchestration","description":"Exposes Unstructured Platform's document processing workflows through the Model Context Protocol (MCP), allowing Claude and other MCP-compatible clients to trigger, configure, and monitor multi-stage data pipelines. Uses MCP's resource and tool abstractions to map Unstructured's processing stages (partitioning, chunking, embedding, extraction) into callable operations with schema-based parameter passing and streaming result delivery.","intents":["I want to connect Claude to my document processing pipeline without building custom API integrations","I need to orchestrate complex multi-stage document workflows from an AI agent context","I want to expose Unstructured's processing capabilities as tools available to language models"],"best_for":["AI agent developers building document-centric workflows","Teams integrating Unstructured Platform with Claude or other MCP clients","Builders prototyping RAG systems that need dynamic document processing"],"limitations":["Requires active Unstructured Platform account and API credentials — cannot run purely locally without platform backend","MCP protocol overhead adds latency for high-frequency small document operations","Limited to Unstructured Platform's supported document types and processing models"],"requires":["Unstructured Platform account with API key","MCP-compatible client (Claude Desktop, or custom MCP host)","Network connectivity to Unstructured Platform endpoints","Python 3.8+ or Node.js 16+ depending on MCP server implementation"],"input_types":["PDF documents","Word documents (DOCX)","PowerPoint presentations","Images (PNG, JPG)","HTML/XML","Plain text","Email (EML)","Markdown"],"output_types":["Structured JSON with extracted elements","Chunked text segments with metadata","Vector embeddings","Element-level annotations (tables, headers, footers)","Processing status and error logs"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unstructured__cap_1","uri":"capability://data.processing.analysis.intelligent.document.partitioning.with.element.classification","name":"intelligent document partitioning with element classification","description":"Decomposes unstructured documents into semantically meaningful elements (text blocks, tables, headers, footers, images) using Unstructured's partitioning models, which employ layout analysis and OCR-aware heuristics to identify document structure. Exposes this capability through MCP tools that accept raw documents and return hierarchically-organized elements with bounding boxes, confidence scores, and element type classifications.","intents":["I need to extract structured elements from PDFs while preserving document layout and semantic meaning","I want to identify and separate tables, headers, and body text automatically without manual annotation","I need to handle mixed-format documents (scanned PDFs, digital documents) with a single pipeline"],"best_for":["Document processing teams building RAG systems that need semantic chunking","Developers extracting structured data from unstructured documents at scale","Organizations processing heterogeneous document types (contracts, reports, forms)"],"limitations":["Partitioning accuracy varies by document type — scanned PDFs with poor OCR may produce fragmented elements","Complex multi-column layouts may be misclassified as separate elements rather than continuous text","Element bounding box coordinates are relative to original document — require coordinate transformation for downstream use"],"requires":["Unstructured Platform API access","Document file in supported format (PDF, DOCX, PPTX, HTML, etc.)","MCP client with tool-calling capability"],"input_types":["PDF (digital and scanned)","DOCX","PPTX","HTML","XML","Plain text","Images (PNG, JPG, TIFF)"],"output_types":["JSON array of element objects with type, text, bounding box, confidence score","Hierarchical element tree with parent-child relationships","Element metadata (page number, element index, extracted coordinates)"],"categories":["data-processing-analysis","document-understanding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unstructured__cap_2","uri":"capability://data.processing.analysis.semantic.chunking.with.configurable.chunk.boundaries","name":"semantic chunking with configurable chunk boundaries","description":"Segments partitioned document elements into chunks optimized for embedding and retrieval, using Unstructured's chunking strategies that respect semantic boundaries (sentence breaks, paragraph boundaries, table cells) rather than fixed token counts. Exposes configuration options through MCP parameters to control chunk size, overlap, and boundary-respecting behavior, with output including chunk text, source element references, and metadata for traceability.","intents":["I want to chunk documents for RAG without breaking sentences or tables across chunk boundaries","I need to control chunk size while maintaining semantic coherence for better embedding quality","I want to track which original document elements contributed to each chunk for citation and traceability"],"best_for":["RAG system builders optimizing retrieval quality through semantic chunking","Teams building citation-aware QA systems that need element-to-chunk traceability","Developers tuning chunk parameters for specific embedding models or vector databases"],"limitations":["Semantic chunking is slower than fixed-size splitting — adds ~50-200ms per document depending on size","Chunk size guarantees are soft (may exceed max_chunk_size to avoid breaking semantic units)","Overlap configuration can significantly increase total chunk count and storage requirements"],"requires":["Pre-partitioned document elements from partitioning capability","Unstructured Platform API access","MCP client with tool-calling capability"],"input_types":["Partitioned element arrays (output from partitioning capability)","Configuration parameters: max_chunk_size, chunk_overlap, boundary_strategy"],"output_types":["JSON array of chunk objects with text, metadata, source element IDs","Chunk-to-element mapping for traceability","Chunk statistics (size, element count, overlap regions)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unstructured__cap_3","uri":"capability://data.processing.analysis.multi.modal.element.extraction.and.classification","name":"multi-modal element extraction and classification","description":"Extracts and classifies diverse element types from documents including text, tables, images, and metadata, using Unstructured's element-specific extractors. Tables are parsed into structured formats (JSON, CSV), images are extracted with OCR fallback, and metadata (titles, authors, dates) is identified through heuristic and model-based approaches. Exposes extraction through MCP tools with configurable output formats and element filtering options.","intents":["I need to extract tables from documents as structured data (JSON or CSV) for downstream processing","I want to identify and extract images from documents while preserving their context","I need to extract document metadata (title, author, creation date) automatically"],"best_for":["Data extraction teams processing documents with mixed content types","Organizations building document intelligence systems that need structured table extraction","Developers building document search systems that index both text and visual content"],"limitations":["Table extraction accuracy degrades for complex nested tables or tables with merged cells","Image extraction preserves images but does not perform image understanding — requires separate vision model for interpretation","Metadata extraction relies on document structure — may fail for non-standard document formats or corrupted metadata"],"requires":["Partitioned document elements","Unstructured Platform API access","MCP client with tool-calling capability"],"input_types":["Partitioned elements containing tables, images, text blocks","Configuration: output_format (json/csv for tables), include_images (boolean), metadata_extraction_mode"],"output_types":["Structured table data (JSON, CSV, Markdown)","Image metadata and extraction status","Document metadata object (title, author, creation_date, etc.)","Element-level extraction results with confidence scores"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unstructured__cap_4","uri":"capability://data.processing.analysis.document.embedding.generation.with.provider.flexibility","name":"document embedding generation with provider flexibility","description":"Generates vector embeddings for document chunks using configurable embedding providers (OpenAI, Hugging Face, local models), with Unstructured Platform handling provider abstraction and batch processing. Exposes embedding configuration through MCP parameters allowing selection of embedding model, dimensionality, and batch size. Returns embeddings alongside chunk metadata for direct integration with vector databases.","intents":["I want to generate embeddings for document chunks without managing multiple embedding provider SDKs","I need to switch embedding models (OpenAI to open-source) without changing my pipeline code","I want to batch embed large document collections efficiently with automatic rate limiting"],"best_for":["RAG system builders who want provider-agnostic embedding generation","Teams evaluating different embedding models without pipeline refactoring","Developers building document search systems at scale with cost optimization needs"],"limitations":["Embedding generation is synchronous in MCP context — large batches may timeout depending on MCP client timeout settings","Provider costs vary significantly (OpenAI embeddings ~$0.02/1M tokens vs. local models free) — no built-in cost optimization","Embedding dimensionality and model selection are fixed per request — cannot mix models in single batch"],"requires":["Unstructured Platform API access","API credentials for selected embedding provider (OpenAI key, Hugging Face token, etc.)","MCP client with tool-calling capability","Document chunks from chunking capability"],"input_types":["Text chunks (from chunking capability)","Configuration: embedding_provider (openai/huggingface/local), model_name, dimensions"],"output_types":["Vector embeddings (float arrays of specified dimensionality)","Embedding metadata (model used, dimensions, generation timestamp)","Chunk-embedding pairs ready for vector database ingestion"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unstructured__cap_5","uri":"capability://automation.workflow.workflow.state.persistence.and.resumption","name":"workflow state persistence and resumption","description":"Manages document processing workflow state across MCP invocations, allowing pipelines to resume from intermediate stages without reprocessing. Unstructured Platform maintains state for partitioned elements, chunks, and embeddings, with MCP tools exposing state retrieval and resumption capabilities. Enables efficient re-processing of documents with modified parameters (e.g., different chunking strategy) by reusing earlier pipeline stages.","intents":["I want to re-chunk documents with different parameters without re-partitioning them","I need to resume document processing after a failure without losing intermediate results","I want to experiment with different embedding models on the same chunks without re-chunking"],"best_for":["Teams processing large document collections where re-processing is expensive","Developers iterating on pipeline parameters and tuning chunking/embedding strategies","Organizations building fault-tolerant document processing workflows"],"limitations":["State persistence is tied to Unstructured Platform — no local state export for offline processing","State retention duration depends on platform plan — may be limited to 30-90 days for free tier","State resumption requires matching document version — modifications to source documents invalidate cached state"],"requires":["Unstructured Platform account with state persistence enabled","Document processing workflow initiated through MCP","MCP client with tool-calling capability"],"input_types":["Workflow ID or document reference","Stage identifier (partitioning/chunking/embedding)","Optional: modified parameters for downstream stages"],"output_types":["Cached state from specified pipeline stage","Workflow status and metadata","Resumption instructions for downstream stages"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unstructured__cap_6","uri":"capability://automation.workflow.batch.document.processing.with.progress.tracking","name":"batch document processing with progress tracking","description":"Processes multiple documents in batch mode through the full pipeline (partitioning → chunking → embedding) with asynchronous execution and progress tracking. MCP tools expose batch submission, status polling, and result retrieval, with Unstructured Platform managing job queuing and parallelization. Returns per-document processing status, error details, and results aggregation for large-scale document ingestion workflows.","intents":["I want to process hundreds of documents efficiently without blocking on individual document completion","I need to monitor processing progress and handle failures gracefully in large batch jobs","I want to ingest a document corpus into a vector database with automatic error recovery"],"best_for":["Teams building document ingestion pipelines for RAG systems at scale","Organizations migrating large document repositories to searchable formats","Developers building background job systems for document processing"],"limitations":["Batch processing is asynchronous — requires polling for completion status, no native webhook support through MCP","Per-document error handling is basic — failures are logged but don't automatically trigger retries","Batch size limits depend on platform plan — may be capped at 100-1000 documents per batch"],"requires":["Unstructured Platform account with batch processing enabled","Multiple documents in supported formats","MCP client with polling capability for status checks","Sufficient API quota for batch size"],"input_types":["Document list (file paths, URLs, or document IDs)","Batch configuration: pipeline_stages, chunking_params, embedding_model","Optional: error_handling_strategy, retry_policy"],"output_types":["Batch job ID for tracking","Per-document processing status (pending/processing/completed/failed)","Aggregated results (total documents processed, success rate, error summary)","Per-document results (partitioned elements, chunks, embeddings)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-unstructured__cap_7","uri":"capability://data.processing.analysis.custom.extraction.rules.and.field.mapping","name":"custom extraction rules and field mapping","description":"Allows definition of custom extraction rules to identify and extract specific fields or patterns from documents (e.g., invoice numbers, dates, customer names) using Unstructured's rule engine. Rules can be defined as regex patterns, semantic patterns (e.g., 'find all monetary amounts'), or element-type-based filters. Exposes rule definition and application through MCP tools, returning extracted field values with confidence scores and source element references.","intents":["I want to extract specific fields (invoice number, date, total amount) from documents automatically","I need to identify and extract entities matching custom patterns without manual annotation","I want to map extracted fields to a structured schema for downstream processing"],"best_for":["Organizations processing domain-specific documents (invoices, contracts, forms) with consistent structure","Teams building document intelligence systems that need field-level extraction","Developers automating data entry from unstructured documents"],"limitations":["Rule definition requires understanding of document structure — may need manual tuning per document type","Regex-based rules are brittle — changes in document format may break extraction","Semantic pattern matching is less accurate than fine-tuned ML models — confidence scores may be low for ambiguous patterns"],"requires":["Partitioned document elements","Unstructured Platform API access","MCP client with tool-calling capability","Rule definitions (regex patterns or semantic descriptions)"],"input_types":["Partitioned elements","Rule definitions: pattern_type (regex/semantic/element_type), pattern, field_name","Optional: confidence_threshold, extraction_scope (document/page/element)"],"output_types":["Extracted field values with confidence scores","Source element references (element ID, page number, bounding box)","Extraction metadata (rule matched, extraction timestamp)","Structured output (JSON object with extracted fields)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Unstructured Platform account with API key","MCP-compatible client (Claude Desktop, or custom MCP host)","Network connectivity to Unstructured Platform endpoints","Python 3.8+ or Node.js 16+ depending on MCP server implementation","Unstructured Platform API access","Document file in supported format (PDF, DOCX, PPTX, HTML, etc.)","MCP client with tool-calling capability","Pre-partitioned document elements from partitioning capability","Partitioned document elements","API credentials for selected embedding provider (OpenAI key, Hugging Face token, etc.)"],"failure_modes":["Requires active Unstructured Platform account and API credentials — cannot run purely locally without platform backend","MCP protocol overhead adds latency for high-frequency small document operations","Limited to Unstructured Platform's supported document types and processing models","Partitioning accuracy varies by document type — scanned PDFs with poor OCR may produce fragmented elements","Complex multi-column layouts may be misclassified as separate elements rather than continuous text","Element bounding box coordinates are relative to original document — require coordinate transformation for downstream use","Semantic chunking is slower than fixed-size splitting — adds ~50-200ms per document depending on size","Chunk size guarantees are soft (may exceed max_chunk_size to avoid breaking semantic units)","Overlap configuration can significantly increase total chunk count and storage requirements","Table extraction accuracy degrades for complex nested tables or tables with merged cells","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.050Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=unstructured","compare_url":"https://unfragile.ai/compare?artifact=unstructured"}},"signature":"/EY+/ibJzRJ2lgVH3Lnhxa0AVYY7yZ2lv6TnR5QoVjPzHkXbD20fgjK1jrYh+QRmz1IliLGMUN5n389EsRCcDQ==","signedAt":"2026-06-21T23:35:25.942Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/unstructured","artifact":"https://unfragile.ai/unstructured","verify":"https://unfragile.ai/api/v1/verify?slug=unstructured","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"}}