"A server or tool using RAG for documentation scraping, storage, and retrieval with SSE support" matching MCP servers:

An MCP server that retrieves relevant PDF chunks via local embeddings and returns them to IDE agents (Cursor, Kiro, Claude Code) for answer generation.

Provides persistent, inspectable memory storage for AI agents using SQLite. Agents can store, recall, and search memories across sessions via three MCP tools.

Provides customer lookup, transaction history, knowledge-base search, and ticket management tools to any MCP client.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.

Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.

Enables retrieval-augmented generation over a local markdown corpus, allowing grounded, cited answers via an MCP tool or CLI.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

Enables Claude to perform retrieval-augmented generation using LangChain, ChromaDB, and HuggingFace models for domain-aware reasoning with PDF embedding, smart retrieval, reranking, and citation-based responses.

Enables AI agents to search and retrieve over 89K skills on-demand at runtime, eliminating the need to manually install skills upfront.

Exposes a provenance-aware knowledge graph to AI agents over MCP, providing tools like get_fact and search_documents that return precise answers with source citations.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.

A lightweight knowledge base MCP server that enables full-text search and retrieval of markdown documents from indexed sites using Orama BM25.

Central registry for AI tools with vector search and knowledge graph, enabling intelligent tool discovery, chaining, and execution via MCP.

Enables semantic search and question answering over a knowledge base using hybrid retrieval and grounded answers, all running offline with no API keys.

Enables coding agents to retrieve project context from an AnythingLLM RAG at session start and write back human-approved durable facts via five workflow-shaped tools.

Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.