{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-laskarksmcp-rag-node","slug":"npm-laskarksmcp-rag-node","name":"@laskarks/mcp-rag-node","type":"mcp","url":"https://www.npmjs.com/package/@laskarks/mcp-rag-node","page_url":"https://unfragile.ai/npm-laskarksmcp-rag-node","categories":["mcp-servers","rag-knowledge"],"tags":["mcp","modelcontextprotocol","sdk"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-laskarksmcp-rag-node__cap_0","uri":"capability://tool.use.integration.mcp.compliant.rag.server.instantiation","name":"mcp-compliant rag server instantiation","description":"Bootstraps a Model Context Protocol server that exposes RAG (Retrieval-Augmented Generation) capabilities as MCP resources and tools. Uses the @modelcontextprotocol/sdk to implement the MCP server protocol, allowing Claude and other MCP clients to discover and invoke RAG operations through standardized MCP message handlers. The server registers itself with MCP's resource and tool registries, enabling bidirectional communication with LLM clients.","intents":["Set up a local or remote RAG server that Claude can connect to and query","Expose document retrieval and augmentation capabilities via the MCP protocol","Build a bridge between a knowledge base and LLM-based applications without custom API layers"],"best_for":["Node.js developers building Claude-integrated RAG systems","Teams deploying MCP servers for enterprise knowledge management","Builders prototyping LLM agents with persistent document access"],"limitations":["Node.js runtime only — no Python or other language support","Minimal built-in persistence — requires external vector store or document database integration","No authentication/authorization layer — requires external security wrapper for production","Single-process server — horizontal scaling requires external orchestration (Docker, Kubernetes)"],"requires":["Node.js 16+ (for ES modules and async/await support)","@modelcontextprotocol/sdk npm package","MCP client (Claude, or custom MCP client implementation)"],"input_types":["MCP protocol messages (JSON-RPC 2.0)","Resource URIs identifying documents or collections","Tool invocation payloads with query parameters"],"output_types":["MCP resource responses (document content, metadata)","Tool execution results (retrieved chunks, rankings, augmented context)","MCP protocol messages (JSON-RPC responses)"],"categories":["tool-use-integration","mcp-server"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-laskarksmcp-rag-node__cap_1","uri":"capability://memory.knowledge.document.resource.registration.and.discovery","name":"document resource registration and discovery","description":"Registers documents or document collections as MCP resources with metadata (URI, MIME type, description), allowing MCP clients to discover available knowledge sources via the MCP resource list endpoint. Uses MCP's resource registry to expose documents as first-class protocol objects with standardized metadata, enabling clients to query what documents are available before invoking retrieval operations.","intents":["Let Claude discover what documents or knowledge bases are available in the RAG system","Expose document metadata (title, type, size, last updated) to inform retrieval decisions","Enable clients to browse available resources before making retrieval requests"],"best_for":["RAG systems with multiple document collections or knowledge bases","Scenarios where clients need to understand available resources before querying","Teams building multi-tenant RAG systems with per-user document visibility"],"limitations":["No built-in access control — all registered resources are discoverable by any MCP client","Metadata is static at registration time — dynamic document updates require server restart or custom refresh logic","No pagination or filtering of resource lists — large document collections may cause slow discovery","Resource URIs are opaque strings — no standardized naming convention enforced"],"requires":["MCP client that supports resource discovery (Claude, or custom MCP client)","Document metadata (URI, MIME type, description) prepared before server startup"],"input_types":["Document metadata objects (URI, MIME type, description, optional custom fields)"],"output_types":["MCP resource list responses with standardized resource objects","Resource metadata queryable by MCP clients"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-laskarksmcp-rag-node__cap_2","uri":"capability://search.retrieval.retrieval.tool.invocation.with.query.execution","name":"retrieval tool invocation with query execution","description":"Exposes retrieval operations as MCP tools that clients can invoke with query parameters (e.g., search terms, filters, result limits). When a client calls a retrieval tool, the server executes the query against its knowledge base (implementation-specific: vector search, keyword search, or hybrid), and returns ranked results with content and metadata. Uses MCP's tool registry to define tool schemas (input parameters, return types) and handle tool execution callbacks.","intents":["Execute semantic or keyword searches against a document collection from within an LLM conversation","Retrieve relevant context to augment LLM prompts with domain-specific knowledge","Allow LLMs to dynamically fetch information during reasoning without pre-loading all documents"],"best_for":["LLM agents that need to retrieve context dynamically during task execution","RAG systems where document relevance is determined at query time","Applications where the knowledge base is too large to fit in a single prompt"],"limitations":["No built-in vector database — requires external integration (Pinecone, Weaviate, local embeddings)","Query execution latency depends entirely on the underlying retrieval backend — no caching layer","No ranking or relevance tuning built-in — results are returned as-is from the backend","Tool schema is fixed at server startup — dynamic tool definitions not supported"],"requires":["Retrieval backend implementation (vector store, search engine, or custom retrieval logic)","Document embeddings or indexing (if using semantic search)","Tool schema definition with input parameters (query, filters, limit, etc.)"],"input_types":["Tool invocation payloads with query parameters (search term, filters, result limit)","MCP tool call messages with structured arguments"],"output_types":["Retrieved document chunks with content and metadata","Relevance scores or rankings (if provided by backend)","MCP tool result messages with structured response"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-laskarksmcp-rag-node__cap_3","uri":"capability://tool.use.integration.mcp.protocol.message.handling.and.routing","name":"mcp protocol message handling and routing","description":"Implements the MCP server-side message loop that receives JSON-RPC 2.0 requests from clients (resource list, resource read, tool call), routes them to appropriate handlers, and sends responses back over the MCP transport (stdio, HTTP, WebSocket). Uses the @modelcontextprotocol/sdk's server class to abstract transport details and provide typed message handlers for resources and tools.","intents":["Handle incoming MCP requests from Claude or other MCP clients without implementing raw JSON-RPC","Route resource and tool requests to the correct handler functions","Manage the bidirectional communication protocol between client and server"],"best_for":["Developers building MCP servers who want to avoid low-level protocol implementation","Teams integrating RAG with Claude via MCP without custom protocol code","Builders prototyping MCP servers quickly without protocol expertise"],"limitations":["Transport is determined at server startup — switching between stdio, HTTP, and WebSocket requires code changes","No built-in request validation — malformed tool parameters are passed to handlers as-is","Error handling is basic — server crashes on unhandled exceptions in tool handlers","No request/response logging or observability built-in"],"requires":["@modelcontextprotocol/sdk with server class","MCP client that supports the protocol version implemented by the SDK","Transport configuration (stdio for local, HTTP/WebSocket for remote)"],"input_types":["MCP protocol messages (JSON-RPC 2.0 requests)","Resource list requests, resource read requests, tool call requests"],"output_types":["MCP protocol messages (JSON-RPC 2.0 responses)","Resource list responses, resource content, tool results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-laskarksmcp-rag-node__cap_4","uri":"capability://memory.knowledge.context.augmentation.for.llm.prompts","name":"context augmentation for llm prompts","description":"Retrieves relevant documents or chunks from the knowledge base and formats them as context that can be injected into LLM prompts. The server returns retrieved content in a format suitable for prompt augmentation (e.g., markdown, structured JSON), allowing clients to prepend or interleave context with user queries before sending to the LLM. This enables RAG workflows where the LLM sees both user input and relevant background information.","intents":["Automatically fetch relevant documentation or knowledge before sending a user query to the LLM","Reduce hallucinations by grounding LLM responses in retrieved facts","Build RAG pipelines where context retrieval happens server-side before LLM inference"],"best_for":["Question-answering systems over large document collections","Customer support chatbots that need to reference knowledge bases","Enterprise LLM applications where accuracy and source attribution are critical"],"limitations":["Context formatting is implementation-specific — no standardized format for augmented prompts","No built-in deduplication of retrieved chunks — duplicate context may be included","Context window management is client-side — server doesn't optimize for token limits","No feedback loop — server doesn't learn which retrieved context was actually useful"],"requires":["Retrieval backend that can return document content and metadata","Client-side logic to format retrieved context into prompts","Knowledge of the target LLM's context window and prompt format"],"input_types":["Query strings or structured search parameters","Optional context constraints (max tokens, document types, date ranges)"],"output_types":["Retrieved document chunks with metadata","Formatted context suitable for prompt injection","Relevance scores or source attribution"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-laskarksmcp-rag-node__cap_5","uri":"capability://tool.use.integration.tool.schema.definition.and.validation","name":"tool schema definition and validation","description":"Defines the input and output schemas for retrieval tools using JSON Schema, allowing MCP clients to understand what parameters a tool accepts and what it returns. The server registers tool schemas with the MCP protocol, enabling clients to validate arguments before invocation and display tool documentation. Uses the @modelcontextprotocol/sdk's tool registry to attach schemas to tool handlers.","intents":["Enable MCP clients to validate tool arguments before sending requests","Provide documentation for retrieval tools so clients know what parameters are available","Ensure type safety between client tool calls and server tool handlers"],"best_for":["RAG systems with multiple retrieval tools with different parameter sets","Teams building MCP servers where client-side validation is important","Scenarios where tool documentation needs to be auto-generated from schemas"],"limitations":["Schemas are static — dynamic tool parameters based on runtime state not supported","JSON Schema validation is basic — complex conditional schemas not well-supported","No schema versioning — tool schema changes require server restart","Clients may ignore schemas — no enforcement of schema compliance on the server side"],"requires":["JSON Schema definitions for tool inputs and outputs","@modelcontextprotocol/sdk tool registry API","Understanding of JSON Schema syntax and validation rules"],"input_types":["JSON Schema objects defining tool parameters","Tool descriptions and documentation strings"],"output_types":["MCP tool schema responses","Tool documentation queryable by clients"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (for ES modules and async/await support)","@modelcontextprotocol/sdk npm package","MCP client (Claude, or custom MCP client implementation)","MCP client that supports resource discovery (Claude, or custom MCP client)","Document metadata (URI, MIME type, description) prepared before server startup","Retrieval backend implementation (vector store, search engine, or custom retrieval logic)","Document embeddings or indexing (if using semantic search)","Tool schema definition with input parameters (query, filters, limit, etc.)","@modelcontextprotocol/sdk with server class","MCP client that supports the protocol version implemented by the SDK"],"failure_modes":["Node.js runtime only — no Python or other language support","Minimal built-in persistence — requires external vector store or document database integration","No authentication/authorization layer — requires external security wrapper for production","Single-process server — horizontal scaling requires external orchestration (Docker, Kubernetes)","No built-in access control — all registered resources are discoverable by any MCP client","Metadata is static at registration time — dynamic document updates require server restart or custom refresh logic","No pagination or filtering of resource lists — large document collections may cause slow discovery","Resource URIs are opaque strings — no standardized naming convention enforced","No built-in vector database — requires external integration (Pinecone, Weaviate, local embeddings)","Query execution latency depends entirely on the underlying retrieval backend — no caching layer","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.49000000000000005,"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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-05-03T14:23:42.138Z","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=npm-laskarksmcp-rag-node","compare_url":"https://unfragile.ai/compare?artifact=npm-laskarksmcp-rag-node"}},"signature":"kpqnskoskYbOfEIsEqAFP3PTAbmmyi3oOwqZUtsnWaLLLZr4ylnliQK/1SVOtVn7EKw4PZJQXkM9qOiATX/4Dw==","signedAt":"2026-06-21T20:54:58.971Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-laskarksmcp-rag-node","artifact":"https://unfragile.ai/npm-laskarksmcp-rag-node","verify":"https://unfragile.ai/api/v1/verify?slug=npm-laskarksmcp-rag-node","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"}}