"Information on RAG Documents or Processing" matching MCP servers:

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

Safely downloads and extracts public tender documents from PDFs, Office files, and ZIP archives into text with structured signals, while also supporting document comparison and base64 extraction.

Enables AI agents to search the web with clean page text, crawl websites into Markdown, extract articles and structured data, and monitor Google News and press releases for LLM context and RAG pipelines. It works with MCP clients and uses the user's own Apify API token for pay-per-result data retrieval.

Fetches web content and returns relevant chunks using RAG with semantic chunking, query-based scoring, and LexRank, supporting PDFs and 50+ languages.

"primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨

Provides web crawling and RAG capabilities for AI agents, enabling scraping of websites, storing content in a vector database (Supabase), and performing semantic search over crawled data.

Crawls and indexes documentation websites to Supabase with vector embeddings for RAG, using smart sitemap discovery and Jina AI for fast content extraction with multi-project support.

Web crawling and RAG implementation that enables AI agents to scrape websites and perform semantic search over the crawled content, storing everything in Supabase for persistent knowledge retrieval.

A database-agnostic MCP server that integrates web crawling with Retrieval Augmented Generation, supporting multiple AI providers and vector database backends for flexible and intelligent content retrieval.

Enables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.

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.

Self-hosted MCP toolkit providing web search, Wikipedia, and arXiv access without mandatory API keys, delivered as a Docker Compose bundle.

Enables users to retrieve and structure shipment tracking information from DB Schenker's website using natural language queries.

Provides AI agents and assistants with advanced web crawling and RAG capabilities, enabling them to scrape websites and perform semantic search over crawled content.

Provides AI agents with complete web search, crawling, and RAG capabilities through a Docker-based solution combining Model Context Protocol, Crawl4AI, SearXNG, and Supabase.

An MCP server that integrates Crawl4AI with Supabase to enable AI agents to crawl websites, store content in a vector database, and perform RAG queries.

Enables AI assistants to crawl websites, extract and store web content with semantic search capabilities using vector embeddings, and retrieve information through natural language queries with tag-based filtering and intelligent content cleaning.

A local vector database RAG system that integrates with Playwright MCP for web scraping, enabling users to build searchable knowledge bases from web content with multiple embedding providers and Claude-optimized context formatting.

Enables querying a vector database of machine learning FAQs and performing web searches using Bright Data proxies through Claude Desktop with structured agent prompts.

AI-native web scraper: scrape, crawl and map any site to clean markdown over stdio. MIT-licensed.