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Uses a two-stage pipeline: document chunking with configurable overlap, followed by batch embedding generation via sentence-transformers models, enabling privacy-preserving knowledge base construction entirely offline.","intents":["I want to index my codebase or documentation locally without sending data to external APIs","I need to build a searchable knowledge base from PDFs and markdown files with semantic understanding","I want to set up RAG infrastructure that works completely offline and respects data privacy"],"best_for":["enterprises with sensitive documents requiring on-premise processing","developers building privacy-first AI applications","teams working in air-gapped or low-bandwidth environments"],"limitations":["Embedding generation is CPU-bound; large document collections (>100k documents) require significant compute time or GPU acceleration","LanceDB is embedded and single-process; no built-in distributed indexing for multi-node deployments","Chunking strategy is fixed (token-based); no adaptive chunking based on document structure or semantic boundaries","No incremental indexing — re-indexing requires full reprocessing of all documents"],"requires":["Node.js 16+","Python 3.8+ (for transformers library via node-gyp bindings)","2GB+ RAM minimum (4GB+ recommended for models like all-MiniLM-L6-v2)","Disk space for LanceDB index (typically 10-20% of source document size)"],"input_types":["PDF files","plain text (.txt)","markdown (.md)","structured JSON documents"],"output_types":["LanceDB vector index (binary format)","embedding vectors (float32 arrays)","metadata with document source and chunk boundaries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-local-rag__cap_1","uri":"capability://search.retrieval.semantic.document.search.with.vector.similarity","name":"semantic-document-search-with-vector-similarity","description":"Executes semantic search queries against the indexed document collection by converting user queries to embeddings and computing vector similarity (cosine distance) against stored document chunks in LanceDB. Returns ranked results with relevance scores and source document metadata, enabling natural language search without keyword matching. Implements configurable top-k retrieval with optional similarity threshold filtering.","intents":["I want to search my documentation using natural language questions instead of keywords","I need to find semantically similar code snippets or documentation sections across a large codebase","I want to retrieve the most relevant context for an LLM to answer user questions about my documents"],"best_for":["developers building AI chatbots over internal documentation","teams implementing RAG systems for code search and documentation Q&A","researchers needing semantic search over academic or technical papers"],"limitations":["Search quality depends entirely on embedding model quality; domain-specific terminology may not be well-represented in general-purpose models","No support for hybrid search (combining semantic + keyword matching); pure vector similarity can miss exact phrase matches","Similarity threshold tuning requires manual experimentation; no automatic relevance calibration","Query embedding generation adds 100-500ms latency depending on query length and hardware"],"requires":["Pre-indexed document collection in LanceDB","Same embedding model used for both indexing and query (model consistency is critical)","MCP client capable of calling tool functions"],"input_types":["natural language query strings","optional: similarity threshold (0.0-1.0)","optional: top-k parameter (default 5-10)"],"output_types":["ranked list of document chunks with relevance scores","source document metadata (filename, page number if applicable)","raw embedding vectors for advanced filtering"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-local-rag__cap_2","uri":"capability://tool.use.integration.mcp.tool.interface.for.rag.operations","name":"mcp-tool-interface-for-rag-operations","description":"Exposes RAG operations (indexing, search, metadata retrieval) as standardized MCP tools that Claude, Cursor, and other MCP-compatible clients can discover and invoke. Implements the Model Context Protocol specification with proper tool schemas, parameter validation, and error handling, allowing seamless integration into multi-tool agent workflows without custom client code.","intents":["I want Claude to be able to search my documents directly within a conversation","I need to build an agent that can index new documents and search them in a single workflow","I want to expose my local RAG system to multiple MCP clients without writing integration code"],"best_for":["developers using Claude with MCP or Cursor IDE with MCP support","teams building multi-tool AI agents that need document search capabilities","organizations standardizing on MCP for AI tool integration"],"limitations":["MCP protocol overhead adds ~50-100ms per tool call for serialization/deserialization","Tool discovery and schema validation happens at client startup; schema changes require client reconnection","No built-in authentication or authorization; assumes trusted local network or single-user environment","Error messages are limited by MCP protocol; detailed debugging requires server-side logs"],"requires":["MCP-compatible client (Claude via MCP, Cursor IDE, or custom MCP client)","mcp-local-rag server running and accessible (localhost:3000 by default or configured port)","MCP client configuration pointing to the server"],"input_types":["MCP tool call requests with JSON parameters","tool schemas defining expected inputs"],"output_types":["MCP tool result objects with structured data","error responses with diagnostic information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-local-rag__cap_3","uri":"capability://data.processing.analysis.multi.format.document.ingestion.with.parsing","name":"multi-format-document-ingestion-with-parsing","description":"Automatically detects and parses multiple document formats (PDF via pdfjs, plain text, markdown) into normalized text chunks suitable for embedding. Handles PDF metadata extraction, text encoding detection, and format-specific preprocessing (markdown frontmatter stripping, code block preservation) before chunking, enabling heterogeneous document collections without manual conversion.","intents":["I want to index a mix of PDFs, markdown docs, and text files without converting them first","I need to preserve code blocks and structured content when indexing markdown documentation","I want to extract and index metadata from PDF documents alongside their content"],"best_for":["teams with diverse documentation formats (API docs, guides, research papers)","developers indexing code repositories with mixed documentation types","organizations migrating legacy documentation to searchable knowledge bases"],"limitations":["PDF parsing may fail on scanned documents or complex layouts; no OCR support for image-based PDFs","Markdown parsing is basic; doesn't preserve semantic structure (headings, lists) in chunk boundaries","No support for binary formats (Word, Excel, PowerPoint); requires pre-conversion to text/PDF","Large PDFs (>100MB) may cause memory spikes during parsing"],"requires":["pdfjs-dist library (included in package)","File system access to document directory","Sufficient memory for parsing large documents (2GB+ for multi-gigabyte collections)"],"input_types":["PDF files (.pdf)","plain text files (.txt)","markdown files (.md)","UTF-8 encoded text"],"output_types":["normalized text chunks","document metadata (filename, format, page numbers for PDFs)","chunk boundaries and source references"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-local-rag__cap_4","uri":"capability://data.processing.analysis.configurable.document.chunking.with.overlap","name":"configurable-document-chunking-with-overlap","description":"Splits documents into semantically-relevant chunks using token-based boundaries with configurable chunk size and overlap parameters. Preserves document structure by respecting paragraph and sentence boundaries when possible, and maintains chunk metadata (source document, chunk index, character offsets) for precise source attribution. Overlap between chunks enables better context preservation for queries that span chunk boundaries.","intents":["I want to control how documents are split to balance context window usage with retrieval precision","I need to ensure search results include sufficient context around the matched content","I want to track exactly where in the source document each search result came from"],"best_for":["developers tuning RAG systems for specific LLM context window sizes","teams needing precise source attribution for compliance or citation purposes","researchers optimizing retrieval quality through chunk size experimentation"],"limitations":["Token counting is approximate (uses tiktoken or similar); actual token counts may vary by tokenizer","No semantic-aware chunking; splits are purely token-based and may break logical units","Overlap increases index size and embedding costs proportionally; no automatic optimization","Chunk boundaries don't respect code block or table structures; may split code snippets mid-statement"],"requires":["Tokenizer library (tiktoken or equivalent) for accurate token counting","Configuration parameters: chunk_size (tokens), overlap (tokens or percentage)"],"input_types":["parsed document text","chunk size parameter (default 512-1024 tokens)","overlap parameter (default 20-50 tokens or 10-20%)"],"output_types":["list of text chunks with metadata","chunk boundaries (character offsets)","source document references"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-local-rag__cap_5","uri":"capability://memory.knowledge.local.embedding.model.management","name":"local-embedding-model-management","description":"Manages lifecycle of Hugging Face transformer models for embedding generation, including automatic model downloading, caching, and device selection (CPU/GPU). Supports multiple embedding models (all-MiniLM-L6-v2, all-mpnet-base-v2, etc.) with configurable model selection and lazy loading to minimize startup time. Handles model versioning and ensures consistency between indexing and query embedding models.","intents":["I want to use different embedding models without managing downloads and caching manually","I need to switch between lightweight and high-quality embedding models based on available hardware","I want to ensure my indexing and search use the same embedding model to avoid mismatches"],"best_for":["developers experimenting with different embedding models for quality/speed tradeoffs","teams deploying RAG on heterogeneous hardware (laptops, servers, edge devices)","organizations requiring model version pinning for reproducibility"],"limitations":["Model downloads are large (100MB-500MB+); first-run setup requires significant bandwidth and disk space","GPU support requires CUDA/cuDNN setup; falls back to CPU silently, causing 5-10x slowdown","No model quantization or distillation; cannot reduce model size for memory-constrained environments","Model caching is global; no per-project isolation or version management"],"requires":["Hugging Face transformers library (installed via npm dependencies)","Internet connection for initial model download (subsequent runs use cache)","Disk space: 500MB-2GB depending on selected models","For GPU: CUDA 11.8+ and cuDNN 8.6+ (optional but recommended)"],"input_types":["model identifier string (e.g., 'sentence-transformers/all-MiniLM-L6-v2')","device preference (auto/cpu/cuda)"],"output_types":["loaded embedding model instance","model metadata (dimensions, max sequence length)","embedding function accepting text input"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-local-rag__cap_6","uri":"capability://memory.knowledge.lancedb.vector.index.persistence","name":"lancedb-vector-index-persistence","description":"Persists vector indexes to disk using LanceDB's columnar format, enabling fast index loading on subsequent runs without re-embedding documents. Implements index versioning and metadata tracking to detect schema changes or model mismatches. Supports index export/import for backup and distribution, and provides index statistics (document count, index size, last updated) for monitoring.","intents":["I want to build an index once and reuse it across multiple search sessions","I need to backup and restore my document indexes reliably","I want to monitor index health and detect when re-indexing is needed"],"best_for":["production deployments requiring persistent knowledge bases","teams with large document collections where re-indexing is expensive","organizations needing index versioning and audit trails"],"limitations":["LanceDB indexes are not portable across different embedding models; model changes require full re-indexing","No built-in compression; index size is typically 10-20% of source document size","Single-process access only; concurrent writes from multiple processes will corrupt the index","No incremental updates; adding new documents requires re-indexing or manual append operations"],"requires":["LanceDB library (included in package)","Writable file system with sufficient disk space","Consistent embedding model across sessions (model changes invalidate index)"],"input_types":["document chunks with embeddings","metadata dictionary per chunk"],"output_types":["LanceDB index files (binary format)","index metadata (document count, embedding dimensions, creation timestamp)","index statistics for monitoring"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-mcp-local-rag__cap_7","uri":"capability://automation.workflow.mcp.server.lifecycle.management","name":"mcp-server-lifecycle-management","description":"Implements MCP server initialization, request handling, and graceful shutdown with proper resource cleanup. Manages stdio-based communication with MCP clients, tool registration and discovery, and error handling with detailed diagnostic logging. Supports configuration via environment variables or config files, enabling deployment flexibility without code changes.","intents":["I want to run mcp-local-rag as a service that Claude or Cursor can connect to","I need to configure the server for different environments (development, production, air-gapped)","I want to debug server issues with detailed logs and error messages"],"best_for":["developers deploying RAG as a service for multiple clients","teams running mcp-local-rag in containerized or serverless environments","organizations needing production-grade logging and monitoring"],"limitations":["Stdio-based communication limits throughput; not suitable for high-frequency tool calls (>100 calls/sec)","No built-in authentication; assumes trusted network or single-user environment","Configuration via environment variables is limited; complex setups require code modification","Graceful shutdown may take up to 30 seconds if embeddings are in progress"],"requires":["Node.js 16+ runtime","MCP client with stdio transport support","Environment variables for configuration (optional)"],"input_types":["MCP protocol messages via stdin","environment variables for configuration"],"output_types":["MCP protocol responses via stdout","diagnostic logs to stderr"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","Python 3.8+ (for transformers library via node-gyp bindings)","2GB+ RAM minimum (4GB+ recommended for models like all-MiniLM-L6-v2)","Disk space for LanceDB index (typically 10-20% of source document size)","Pre-indexed document collection in LanceDB","Same embedding model used for both indexing and query (model consistency is critical)","MCP client capable of calling tool functions","MCP-compatible client (Claude via MCP, Cursor IDE, or custom MCP client)","mcp-local-rag server running and accessible (localhost:3000 by default or configured port)","MCP client configuration pointing to the server"],"failure_modes":["Embedding generation is CPU-bound; large document collections (>100k documents) require significant compute time or GPU acceleration","LanceDB is embedded and single-process; no built-in distributed indexing for multi-node deployments","Chunking strategy is fixed (token-based); no adaptive chunking based on document structure or semantic boundaries","No incremental indexing — re-indexing requires full reprocessing of all documents","Search quality depends entirely on embedding model quality; domain-specific terminology may not be well-represented in general-purpose models","No support for hybrid search (combining semantic + keyword matching); pure vector similarity can miss exact phrase matches","Similarity threshold tuning requires manual experimentation; no automatic relevance calibration","Query embedding generation adds 100-500ms latency depending on query length and hardware","MCP protocol overhead adds ~50-100ms per tool call for serialization/deserialization","Tool discovery and schema validation happens at client startup; schema changes require client reconnection","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2851720290155681,"quality":0.26,"ecosystem":0.8,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.653Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1333,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-local-rag","compare_url":"https://unfragile.ai/compare?artifact=mcp-local-rag"}},"signature":"QjWCOkVd9C0Y0zMOxaI1hgKkLrASfjaX7QUPCj0T1rpWegdQSsDBjayLz1n59FoTtGckEQ87EIR2dsHDC4vbDg==","signedAt":"2026-06-20T08:23:53.340Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-local-rag","artifact":"https://unfragile.ai/mcp-local-rag","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-local-rag","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"}}