{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-rag-memory-epf-mcp","slug":"rag-memory-epf-mcp","name":"rag-memory-epf-mcp","type":"mcp","url":"https://github.com/bripin123/rag-memory-epf-mcp","page_url":"https://unfragile.ai/rag-memory-epf-mcp","categories":["mcp-servers","rag-knowledge"],"tags":["mcp","model-context-protocol","rag","knowledge-graph","vector-search","fts5","sqlite","embeddings","bge-m3","multilingual","korean","claude-code","gemini-cli","codex-cli","memory","agent-memory"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-rag-memory-epf-mcp__cap_0","uri":"capability://memory.knowledge.project.local.rag.memory.with.vector.embeddings","name":"project-local rag memory with vector embeddings","description":"Implements a retrieval-augmented generation system that stores and indexes project-specific documents locally using vector embeddings, enabling semantic search across a knowledge base without external cloud dependencies. The system maintains embeddings in a local vector store and performs similarity-based retrieval to augment LLM context with relevant project information, supporting multilingual content through language-agnostic embedding models.","intents":["Store project documentation and codebase context locally for offline RAG","Retrieve semantically relevant information from project knowledge base to augment LLM prompts","Build context-aware agents that understand project-specific patterns and conventions","Enable multilingual search across documentation in different languages"],"best_for":["Teams building LLM agents with project-specific context requirements","Developers needing offline RAG without cloud API dependencies","Organizations with multilingual codebases or documentation"],"limitations":["Vector store is local-only — no built-in distributed persistence or replication across team members","Embedding quality depends on chosen model; no fine-tuning support for domain-specific vocabularies","Memory footprint scales linearly with document count; no automatic pruning or archival strategies","No versioning of embeddings — updates to source documents require manual re-indexing"],"requires":["Node.js 16+","MCP client compatible with server protocol","Local storage for vector database (SQLite or similar)","Embedding model (local or API-based)"],"input_types":["text documents","markdown files","code snippets","structured metadata"],"output_types":["retrieved document chunks","similarity scores","ranked search results","augmented context for LLM"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-memory-epf-mcp__cap_1","uri":"capability://memory.knowledge.knowledge.graph.construction.and.traversal","name":"knowledge graph construction and traversal","description":"Builds a graph-based representation of relationships between documents, entities, and concepts extracted from project knowledge, enabling structured reasoning and multi-hop retrieval across connected information. The system likely uses entity extraction and relationship inference to construct nodes and edges, allowing agents to traverse semantic connections rather than relying solely on vector similarity.","intents":["Understand relationships between different parts of project documentation","Perform multi-hop reasoning across connected concepts and entities","Identify dependencies and relationships in codebase architecture","Enable graph-based queries like 'find all modules that depend on this service'"],"best_for":["Teams with complex, interconnected knowledge bases","Projects requiring structural understanding of dependencies and relationships","Agents performing multi-step reasoning across project domains"],"limitations":["Graph construction requires entity extraction — accuracy depends on NLP model quality","No automatic relationship inference — may require manual annotation for complex domain semantics","Graph traversal adds latency compared to direct vector search; no query optimization for deep paths","Scalability limited by in-memory graph representation; no sharding or distributed graph support"],"requires":["Node.js 16+","Entity extraction model or service","Graph database or in-memory graph library (likely Neo4j.js or similar)","Relationship definition schema"],"input_types":["text documents","structured metadata","entity annotations"],"output_types":["graph nodes and edges","traversal paths","connected entity clusters","relationship metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-memory-epf-mcp__cap_2","uri":"capability://search.retrieval.multilingual.vector.search.with.language.agnostic.embeddings","name":"multilingual vector search with language-agnostic embeddings","description":"Implements semantic search across documents in multiple languages using embeddings that map different languages to a shared vector space, enabling cross-lingual retrieval without language-specific models or translation preprocessing. The system likely uses multilingual embedding models (e.g., multilingual-e5, LaBSE) that natively support 50+ languages, allowing a query in one language to retrieve relevant documents in any language.","intents":["Search project documentation written in multiple languages with a single query","Build multilingual knowledge bases without maintaining separate indexes per language","Enable international teams to search in their native language","Retrieve relevant content regardless of language mismatch between query and documents"],"best_for":["International teams with multilingual codebases and documentation","Projects supporting multiple languages without separate search implementations","Organizations needing cross-lingual semantic understanding"],"limitations":["Multilingual embeddings have lower dimensionality/quality than monolingual models — may reduce precision for language-specific nuances","Query and document languages must both be supported by the embedding model; unsupported languages fall back to English","No language detection — ambiguous queries may retrieve results in unexpected languages","Embedding model size is larger than monolingual alternatives, increasing memory footprint"],"requires":["Multilingual embedding model (e.g., multilingual-e5, LaBSE)","Support for 2+ target languages in embedding model","Vector store supporting dense vector similarity search"],"input_types":["text in any supported language","mixed-language documents"],"output_types":["ranked results across languages","similarity scores","language metadata per result"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-memory-epf-mcp__cap_3","uri":"capability://tool.use.integration.mcp.server.protocol.integration.for.llm.agent.context","name":"mcp server protocol integration for llm agent context","description":"Exposes RAG and knowledge graph capabilities through the Model Context Protocol (MCP), allowing Claude and other LLM clients to invoke memory operations as tools within agent workflows. The server implements MCP's resource and tool interfaces, enabling agents to call memory retrieval, graph traversal, and search operations as first-class capabilities without custom integration code.","intents":["Integrate project knowledge directly into Claude agent workflows","Allow agents to retrieve context on-demand during reasoning","Enable tool-use patterns where agents decide when to query memory","Build multi-turn conversations with persistent project context"],"best_for":["Teams using Claude or other MCP-compatible LLM clients","Developers building LLM agents that need project-specific context","Organizations standardizing on MCP for tool integration"],"limitations":["MCP protocol overhead adds ~50-200ms per tool invocation compared to direct library calls","Server must be running as separate process — no in-process embedding for lower latency","Tool schema complexity may limit how agents discover and use memory capabilities","No built-in caching of tool responses — repeated queries hit the vector store each time"],"requires":["MCP client (Claude desktop, or MCP-compatible LLM interface)","MCP server running and accessible (local or remote)","Tool schema definitions for memory operations"],"input_types":["tool invocation requests from LLM","query parameters","context specifications"],"output_types":["tool results in JSON format","structured search results","graph traversal responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-memory-epf-mcp__cap_4","uri":"capability://data.processing.analysis.document.ingestion.and.indexing.pipeline","name":"document ingestion and indexing pipeline","description":"Processes raw documents (markdown, code, text) into indexed vectors and knowledge graph nodes through a pipeline that handles chunking, embedding generation, and metadata extraction. The system likely implements configurable chunking strategies (sliding window, semantic boundaries) and batch embedding to efficiently process large document collections while maintaining chunk-to-source traceability.","intents":["Add new project documentation to the knowledge base","Index code files and extract relevant context for RAG","Update existing documents and re-index changed content","Batch import large document collections efficiently"],"best_for":["Teams with frequently updated documentation","Projects needing to index large codebases","Workflows requiring automated document ingestion"],"limitations":["Chunking strategy is fixed or limited — no adaptive chunking based on document structure","Embedding generation is synchronous — large batch imports may block the server","No incremental indexing — updating a single document may require re-embedding the entire chunk","Metadata extraction is basic — no semantic tagging or automatic categorization"],"requires":["Document files in supported formats (markdown, text, code)","Embedding model for vector generation","Storage for indexed vectors and metadata"],"input_types":["markdown files","code files","plain text documents","file paths or URLs"],"output_types":["indexed vectors","chunk metadata","source references","ingestion status/logs"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-memory-epf-mcp__cap_5","uri":"capability://data.processing.analysis.semantic.chunking.with.context.preservation","name":"semantic chunking with context preservation","description":"Splits documents into chunks optimized for semantic coherence rather than fixed-size windows, preserving context boundaries to ensure each chunk contains complete concepts. The system likely uses sentence/paragraph boundaries, code block detection, or semantic similarity thresholds to determine chunk boundaries, maintaining references to parent documents and surrounding context.","intents":["Ensure retrieved chunks contain complete, coherent information","Preserve code block and function boundaries in code indexing","Maintain context across chunk boundaries for better retrieval","Reduce noise from arbitrary chunk splits in vector search results"],"best_for":["Projects with structured documents (code, markdown with clear sections)","Teams prioritizing retrieval quality over indexing speed","Knowledge bases with mixed content types (code, prose, structured data)"],"limitations":["Semantic chunking is slower than fixed-size splitting — adds latency to indexing","Chunk size becomes variable — may exceed token limits if semantic boundaries are large","Requires language/format-specific parsing — no universal chunking strategy for all document types","Context preservation adds complexity; no automatic context window optimization"],"requires":["Document parser for target formats (markdown, code, etc.)","Semantic boundary detection logic (sentence splitter, AST parser, or similarity threshold)","Metadata storage for chunk relationships"],"input_types":["structured documents","code files","markdown with sections"],"output_types":["semantically coherent chunks","chunk boundaries and metadata","parent document references"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-memory-epf-mcp__cap_6","uri":"capability://search.retrieval.query.expansion.and.refinement.for.improved.retrieval","name":"query expansion and refinement for improved retrieval","description":"Enhances search queries by generating related terms, reformulations, or sub-queries to improve retrieval coverage, using techniques like synonym expansion, query decomposition, or multi-query generation. The system may use LLM-based query expansion to generate semantically similar queries that retrieve documents missed by the original query, or decompose complex queries into simpler sub-queries for targeted retrieval.","intents":["Improve recall for ambiguous or underspecified queries","Retrieve documents using alternative terminology or phrasings","Decompose complex questions into multiple focused searches","Handle domain-specific synonyms and abbreviations"],"best_for":["Projects with domain-specific terminology and synonyms","Teams needing high recall for complex information needs","Agents performing multi-step reasoning requiring comprehensive context"],"limitations":["Query expansion adds latency — multiple expanded queries must be executed and merged","Expansion quality depends on LLM or synonym database — may generate irrelevant queries","No feedback mechanism to learn which expansions are effective for this knowledge base","Merging results from multiple queries requires deduplication and ranking logic"],"requires":["LLM for query generation or synonym database","Vector search supporting multiple queries","Result merging and deduplication logic"],"input_types":["natural language queries","domain-specific terms"],"output_types":["expanded query set","merged and ranked results","relevance scores"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-memory-epf-mcp__cap_7","uri":"capability://search.retrieval.metadata.driven.filtering.and.faceted.search","name":"metadata-driven filtering and faceted search","description":"Enables filtering search results by document metadata (type, source, date, tags, language) and supports faceted navigation to narrow results by multiple dimensions simultaneously. The system maintains metadata indexes alongside vector indexes, allowing hybrid queries that combine semantic similarity with structured filtering, enabling agents to constrain searches to specific document types or sources.","intents":["Filter search results to specific document types (code, docs, issues)","Narrow results by source or project component","Search within specific date ranges or versions","Enable faceted navigation for exploratory search"],"best_for":["Large knowledge bases with diverse content types","Teams needing to distinguish between documentation, code, and other sources","Projects with versioned or time-sensitive information"],"limitations":["Metadata must be extracted or provided during indexing — no automatic metadata generation","Filtering adds complexity to query execution; no query optimization for common filter patterns","Facet counts require scanning all matching documents — expensive for large result sets","No dynamic facet suggestion based on query results"],"requires":["Metadata schema definition","Metadata extraction during indexing","Indexed metadata fields in vector store"],"input_types":["semantic queries","filter specifications","facet selections"],"output_types":["filtered search results","facet counts","result metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rag-memory-epf-mcp__cap_8","uri":"capability://memory.knowledge.context.window.optimization.for.llm.integration","name":"context window optimization for llm integration","description":"Intelligently selects and ranks retrieved chunks to maximize relevance within LLM token limits, using techniques like diversity-aware ranking, importance scoring, and redundancy elimination. The system may re-rank results by relevance, remove duplicate information, and prioritize high-impact chunks to fit within the LLM's context window while preserving the most important information.","intents":["Fit the most relevant information into limited LLM context windows","Eliminate redundant information from multiple retrieved chunks","Prioritize high-impact context for better reasoning","Adapt context selection based on query complexity"],"best_for":["Agents with strict token budgets or smaller context windows","Teams needing to maximize information density in prompts","Projects with large knowledge bases where selective retrieval is critical"],"limitations":["Ranking and selection add latency to retrieval — no caching of optimized context sets","Token counting is approximate — actual token usage may exceed estimates","Importance scoring is heuristic-based — no learning from which context actually helps reasoning","Diversity-aware ranking may exclude highly relevant but similar chunks"],"requires":["Token counter for target LLM","Relevance and importance scoring models","Diversity-aware ranking algorithm"],"input_types":["retrieved chunks with scores","token budget specification","query context"],"output_types":["optimized chunk selection","token count estimates","ranked context for LLM"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","MCP client compatible with server protocol","Local storage for vector database (SQLite or similar)","Embedding model (local or API-based)","Entity extraction model or service","Graph database or in-memory graph library (likely Neo4j.js or similar)","Relationship definition schema","Multilingual embedding model (e.g., multilingual-e5, LaBSE)","Support for 2+ target languages in embedding model","Vector store supporting dense vector similarity search"],"failure_modes":["Vector store is local-only — no built-in distributed persistence or replication across team members","Embedding quality depends on chosen model; no fine-tuning support for domain-specific vocabularies","Memory footprint scales linearly with document count; no automatic pruning or archival strategies","No versioning of embeddings — updates to source documents require manual re-indexing","Graph construction requires entity extraction — accuracy depends on NLP model quality","No automatic relationship inference — may require manual annotation for complex domain semantics","Graph traversal adds latency compared to direct vector search; no query optimization for deep paths","Scalability limited by in-memory graph representation; no sharding or distributed graph support","Multilingual embeddings have lower dimensionality/quality than monolingual models — may reduce precision for language-specific nuances","Query and document languages must both be supported by the embedding model; unsupported languages fall back to English","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3349354935746398,"quality":0.43,"ecosystem":0.7000000000000001,"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":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":2364,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=rag-memory-epf-mcp","compare_url":"https://unfragile.ai/compare?artifact=rag-memory-epf-mcp"}},"signature":"1oNpYK2m9yOz5H6tUwoMrZnVvnxYvVlplLBDVQhxbW3u6s01v1QnLbWKe4+qHwJLhjjzbiByC9vSeDt/FchRDw==","signedAt":"2026-06-22T01:19:26.209Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rag-memory-epf-mcp","artifact":"https://unfragile.ai/rag-memory-epf-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=rag-memory-epf-mcp","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"}}