"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP servers:

Enables local knowledge base management with retrieval-augmented generation (RAG), providing semantic search, document reading, listing, and Q&A via MCP tools and REST endpoints, all running locally without cloud dependencies.

Enables deterministic auditing of RAG retrieval faithfulness and hallucination scores, providing structured JSON output for AI agents and MCP-compliant clients.

Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.

Minimal local RAG stack with an MCP server that provides document search for any agent.

A Model Context Protocol server for indexing and searching local document collections with hybrid BM25+vector search, optimized for Korean and English, supporting DOCX, PDF, audio, and more.

Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.

Provides live wait times and office details for all 60 Oregon DMV field offices by scraping the ODOT website, with no authentication required.

A local-first RAG engine that ingests documents (PDF, Markdown, images, etc.) and provides hybrid search, reranking, and LLM answer synthesis via MCP for AI agent integration.

A hosted multimodal RAG MCP server that enables natural language queries over text and visual content (PDFs, images, etc.) with verifiable citations, easily run via npx with a Calypso API key.

Bridges AI agents with watsonx.data document libraries, enabling natural language queries and intelligent retrieval from document libraries.

Enables generating images from text prompts using various AI models (FLUX, Recraft, GPT Image, etc.) and automatically delivering them through Cloudinary's platform.

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.

An open-source platform for Retrieval-Augmented Generation (RAG). Upload documents and query them ⚡

Enables MCP clients to query proxy sub-accounts, traffic, pricing, and inventory, and to place orders, renew, and modify configurations after user confirmation.

MCP server for searching French and European law, providing natural language access to jurisprudence, consolidated legal texts, and related legal information via tools like search_decisions and search_legal_texts.

Enables AI agents to convert files, render web pages to markdown/PDF/screenshots, search the live web, extract structured data, ingest RAG-ready chunks, and monitor pages for changes through a single API key.

A robust Model Control Protocol server that enables AI agents to access real-time cyber threat intelligence and detailed information about vulnerabilities, threat actors, malware, and other cyber-security entities.

Enables AI assistants to rewrite AI-styled English prose into more natural human writing, with tools for humanizing text and retrieving information about the underlying model.

Enables converting web pages to clean Markdown via MCP, compressing token usage by roughly 95% in under 150ms to support LLM context windows and RAG pipelines.

Connect AI assistants to Manticore Search. Execute SQL queries, list tables, get schemas, and fetch documentation. Perfect for building RAG applications and search-powered AI agents.