{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-needle","slug":"needle","name":"Needle","type":"mcp","url":"https://github.com/needle-ai/needle-mcp","page_url":"https://unfragile.ai/needle","categories":["rag-knowledge","deployment-infra"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-needle__cap_0","uri":"capability://memory.knowledge.document.indexing.with.semantic.embeddings","name":"document-indexing-with-semantic-embeddings","description":"Indexes documents by converting them into semantic embeddings and storing them in a vector database, enabling similarity-based retrieval without keyword matching. The system processes documents through an embedding pipeline that chunks content, generates vector representations, and persists them in a searchable index optimized for production workloads. This approach enables semantic understanding of document content rather than relying on lexical matching.","intents":["I want to index my company's internal documents so they can be searched semantically","I need to build a RAG system that understands document meaning, not just keywords","I want to set up document indexing without managing vector database infrastructure myself"],"best_for":["teams building production RAG systems with document collections","developers integrating semantic search into existing applications","organizations needing out-of-the-box indexing without infrastructure setup"],"limitations":["Embedding quality depends on the underlying embedding model chosen; no fine-tuning of embeddings per domain","Index updates may require re-embedding large document collections, which can be time-consuming","Vector database scaling characteristics depend on the backend storage implementation"],"requires":["Document files in supported formats (PDF, TXT, Markdown, or other text-based formats)","Embedding model API access or local embedding model deployment","Vector database backend (implementation details depend on Needle's architecture)"],"input_types":["text documents","PDF files","markdown files","code files"],"output_types":["vector embeddings","indexed document metadata","searchable document store"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-needle__cap_1","uri":"capability://search.retrieval.semantic.document.retrieval.with.ranking","name":"semantic-document-retrieval-with-ranking","description":"Retrieves documents from the indexed collection by computing similarity between a query embedding and stored document embeddings, then ranks results by relevance score. The retrieval system converts incoming queries into the same embedding space as indexed documents, performs vector similarity search (likely using cosine similarity or dot product), and returns ranked results with confidence scores. This enables context-aware document selection for LLM prompts.","intents":["I want to retrieve the most relevant documents for a user query to pass to an LLM","I need to find documents similar to a given query with relevance scoring","I want to implement retrieval-augmented generation without building search infrastructure"],"best_for":["LLM application developers building RAG pipelines","teams implementing question-answering systems over document collections","builders needing semantic search without Elasticsearch or Solr complexity"],"limitations":["Retrieval quality is bounded by embedding model quality; poor embeddings produce poor retrieval","No built-in query expansion or synonym handling; queries must be semantically similar to indexed content","Ranking is purely similarity-based; no learning-to-rank or business logic customization visible"],"requires":["Pre-indexed document collection from document-indexing capability","Query text or query embedding","Access to the same embedding model used for indexing"],"input_types":["text query","query embedding"],"output_types":["ranked document list","document chunks with similarity scores","metadata for retrieved documents"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-needle__cap_2","uri":"capability://tool.use.integration.mcp.protocol.document.search.tool","name":"mcp-protocol-document-search-tool","description":"Exposes document search and retrieval as an MCP (Model Context Protocol) tool that Claude and other MCP-compatible clients can invoke directly. The implementation registers search functions as MCP resources with defined input schemas and output formats, allowing language models to call document retrieval as part of their reasoning loop without requiring external API calls or custom integration code. This enables seamless integration of RAG into Claude conversations and agentic workflows.","intents":["I want Claude to search my documents directly during conversations","I need to give my LLM agent access to document search as a native tool","I want to build a Claude-powered chatbot that retrieves from my knowledge base"],"best_for":["Claude users building knowledge-base chatbots","developers creating LLM agents with document access","teams integrating Needle with Claude API or Claude Desktop"],"limitations":["MCP tool invocation adds latency for each search call; no batching of multiple queries","Tool schema must be predefined; dynamic schema generation based on document structure not visible","Limited to MCP-compatible clients; no REST API or GraphQL interface apparent"],"requires":["MCP server running (Needle MCP implementation)","Claude or other MCP client configured to connect to Needle","Indexed document collection available"],"input_types":["MCP tool invocation with query parameters"],"output_types":["MCP tool result with retrieved documents","structured JSON with document chunks and metadata"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-needle__cap_3","uri":"capability://data.processing.analysis.multi.format.document.ingestion","name":"multi-format-document-ingestion","description":"Accepts documents in multiple formats (PDF, TXT, Markdown, code files) and converts them into a unified internal representation for indexing. The ingestion pipeline likely includes format-specific parsers that extract text content, preserve structure metadata, and normalize content before chunking and embedding. This abstraction allows users to index heterogeneous document collections without format-specific preprocessing.","intents":["I want to index PDFs, text files, and code files in a single operation","I need to handle mixed document types without writing custom parsers","I want to preserve document structure (headings, code blocks) during indexing"],"best_for":["teams with diverse document sources (internal wikis, PDFs, code repositories)","developers building knowledge bases from mixed content types","organizations migrating documentation from multiple systems"],"limitations":["Format support is fixed; custom document types require code changes","OCR for scanned PDFs not mentioned; likely text-only PDF support","Metadata extraction depends on document format; some formats may lose structural information"],"requires":["Documents in supported formats: PDF, TXT, Markdown, code files","File access permissions for reading documents"],"input_types":["PDF files","plain text files","Markdown files","source code files"],"output_types":["normalized document chunks","extracted metadata","indexed embeddings"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-needle__cap_4","uri":"capability://data.processing.analysis.chunking.strategy.for.semantic.coherence","name":"chunking-strategy-for-semantic-coherence","description":"Splits documents into semantically coherent chunks before embedding, using strategies that preserve meaning boundaries (e.g., paragraph-aware or sentence-aware chunking rather than fixed-size windows). The chunking system balances chunk size for embedding quality against retrieval granularity, ensuring that individual chunks contain enough context to be meaningful while remaining small enough for efficient retrieval and LLM context windows. This prevents embedding fragmented content that loses semantic meaning.","intents":["I want document chunks that preserve semantic meaning when retrieved","I need to avoid splitting sentences or code blocks across chunk boundaries","I want to optimize chunk size for my LLM's context window"],"best_for":["teams building RAG systems where chunk coherence affects answer quality","developers working with long-form documents that need intelligent splitting","organizations optimizing for specific LLM context window sizes"],"limitations":["Chunking strategy is likely fixed; no apparent configuration for custom chunk boundaries","Overlap between chunks not mentioned; may result in duplicate content in retrieval results","No visible support for hierarchical chunking (e.g., document → section → paragraph)"],"requires":["Document content in supported formats","Chunking configuration (if configurable)"],"input_types":["parsed document content"],"output_types":["document chunks with preserved boundaries","chunk metadata (source, position)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-needle__cap_5","uri":"capability://automation.workflow.production.deployment.ready.rag.system","name":"production-deployment-ready-rag-system","description":"Provides a complete, production-ready RAG system with built-in considerations for scalability, reliability, and operational concerns. The system includes indexing, retrieval, MCP integration, and likely includes features like error handling, logging, monitoring hooks, and deployment patterns suitable for production workloads. This eliminates the need to assemble RAG components from multiple libraries and handle production concerns separately.","intents":["I want to deploy a RAG system to production without building infrastructure from scratch","I need a RAG solution with production-grade reliability and monitoring","I want to avoid managing multiple components and their integration points"],"best_for":["teams deploying RAG systems to production environments","organizations needing managed RAG without building custom infrastructure","developers prioritizing time-to-deployment over custom optimization"],"limitations":["Production readiness claims not substantiated by visible documentation; actual reliability characteristics unknown","Scaling characteristics and performance under load not documented","No apparent multi-tenancy support; likely single-tenant deployment model"],"requires":["Deployment environment (cloud or on-premises)","Document collection for indexing","MCP client or API consumer"],"input_types":["configuration","documents"],"output_types":["running RAG service","search results"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-needle__cap_6","uri":"capability://memory.knowledge.vector.database.abstraction.layer","name":"vector-database-abstraction-layer","description":"Abstracts the underlying vector database implementation, allowing Needle to work with different vector storage backends without exposing database-specific details to users. The abstraction layer handles index creation, embedding storage, similarity search, and result retrieval through a unified interface, enabling users to swap vector database implementations (e.g., Pinecone, Weaviate, Milvus) without changing application code. This decouples RAG logic from infrastructure choices.","intents":["I want to use a specific vector database without rewriting my RAG code","I need to migrate from one vector database to another","I want to avoid vendor lock-in to a specific vector database"],"best_for":["teams evaluating different vector database options","organizations with existing vector database infrastructure","developers building database-agnostic RAG systems"],"limitations":["Abstraction may not expose advanced features of specific vector databases","Performance characteristics vary by backend; no unified performance guarantees","Supported backends unknown; may be limited to specific vector database implementations"],"requires":["Vector database backend (implementation-specific)","Database connection credentials","Vector database client library"],"input_types":["embeddings","queries"],"output_types":["stored vectors","similarity search results"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-needle__cap_7","uri":"capability://memory.knowledge.context.window.aware.document.selection","name":"context-window-aware-document-selection","description":"Selects and ranks retrieved documents based on the LLM's context window constraints, ensuring that the final prompt with documents and query fits within token limits. The system likely tracks token counts for retrieved chunks, prioritizes high-relevance documents, and may truncate or exclude lower-relevance results to fit within context budgets. This prevents context overflow errors and optimizes information density in prompts.","intents":["I want to retrieve documents that fit within my LLM's context window","I need to avoid token limit errors when passing retrieved documents to Claude","I want to maximize the number of relevant documents in my prompt without exceeding limits"],"best_for":["developers building RAG systems with strict context window constraints","teams using smaller or older LLM models with limited context","applications where token efficiency directly impacts cost"],"limitations":["Context window awareness requires knowing the LLM's token limit; may not auto-detect for all models","Truncation strategy not specified; may lose relevant information when fitting to context","No apparent support for dynamic context allocation based on query complexity"],"requires":["LLM context window size specification","Token counting mechanism","Retrieved document list with token counts"],"input_types":["retrieved documents","context window size","query"],"output_types":["context-window-fitted document selection","token count metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Document files in supported formats (PDF, TXT, Markdown, or other text-based formats)","Embedding model API access or local embedding model deployment","Vector database backend (implementation details depend on Needle's architecture)","Pre-indexed document collection from document-indexing capability","Query text or query embedding","Access to the same embedding model used for indexing","MCP server running (Needle MCP implementation)","Claude or other MCP client configured to connect to Needle","Indexed document collection available","Documents in supported formats: PDF, TXT, Markdown, code files"],"failure_modes":["Embedding quality depends on the underlying embedding model chosen; no fine-tuning of embeddings per domain","Index updates may require re-embedding large document collections, which can be time-consuming","Vector database scaling characteristics depend on the backend storage implementation","Retrieval quality is bounded by embedding model quality; poor embeddings produce poor retrieval","No built-in query expansion or synonym handling; queries must be semantically similar to indexed content","Ranking is purely similarity-based; no learning-to-rank or business logic customization visible","MCP tool invocation adds latency for each search call; no batching of multiple queries","Tool schema must be predefined; dynamic schema generation based on document structure not visible","Limited to MCP-compatible clients; no REST API or GraphQL interface apparent","Format support is fixed; custom document types require code changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.49999999999999994,"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":"2026-06-17T09:51:03.579Z","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=needle","compare_url":"https://unfragile.ai/compare?artifact=needle"}},"signature":"mmg94tZeqsRY9cpkYpmg3mu18p0AmOH6LCxgNrmfvE9okVYvaZQxvHrV03xUWV19jAkhu058Y7hoPlrpvbakDg==","signedAt":"2026-06-21T09:01:19.536Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/needle","artifact":"https://unfragile.ai/needle","verify":"https://unfragile.ai/api/v1/verify?slug=needle","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"}}