"Information about RAG (Retrieval-Augmented Generation)" matching MCP servers:

A Retrieval-Augmented Generation system that enables uploading, processing, and semantic search of PDF documents using vector embeddings and FAISS indexing for context-aware question answering.

MCP server that provides 8 local RAG tools using LlamaIndex and Ollama, enabling AI-powered document querying, summarization, analysis, and comparison over PDFs, DOCX, XLSX, and CSV files.

An MCP server that enables generating scripts based on specified topics and keywords, while also providing functionality to store and summarize notes.

Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.

MCP server that fetches YouTube video transcripts and optionally summarizes them. Supports multiple transcript formats (text, JSON, SRT, WebVTT), multi-language retrieval, and flexible YouTube URL parsing.

Generates natural, fluent Chinese content that reads like human writing using the Qwen3-Next-80B-A3B model. Supports web search for latest information and direct file saving with customizable writing rules.

Enables everyday utilities like calculation, text analysis, encoding, hashing, UUID generation, datetime lookup, and note management through MCP tools, resources, and prompts.

Unofficial MCP server wrapping crawl4ai that enables extraction and analysis of content from web pages, PDFs, Office documents, YouTube videos, and more, with AI-powered summarization and Google search integration to reduce token usage while preserving key information.

A powerful MCP server for summarizing YouTube videos with transcript fetching, intelligent summarization, key point extraction, and metadata retrieval.

Enables AI-driven analysis of aggregated news and hot topics from 35+ platforms, providing tools for trend tracking, sentiment analysis, and similar-content retrieval through natural language conversations.

Analyzes YouTube videos using Google's Gemini API, allowing users to get summaries or ask questions about video content via direct URL input.

Provides MCP tools for querying and summarizing Northwest A&F University campus information, including trending QQ channel posts, official website notices/events/competitions, and cross-site custom searches with Markdown output and source links.

An MCP server that enables web searching, URL content extraction, and summarization without requiring API keys. It also provides advanced mathematical evaluation and multi-language Wikipedia summary retrieval tools.

distill-mcp-v2 is a high-performance, network-dependency-free Python FastMCP server designed to aggressively optimize Large Language Model (LLM) context windows. It provides specialized tools for compressing and analyzing massive AI-agent payloads without losing critical semantic information.

MCP server for Glanceway, the macOS menu bar information app, enabling AI assistants to read sources and items.

Content writing toolkit for headline generation, readability scoring, tone analysis, outline building, and plagiarism similarity checking.

Enables search, retrieval, and summarization of Japanese National Diet meeting minutes via MCP tools.

A Model Context Protocol server that enables access to YouTube video content through transcripts, translations, summaries, and subtitle generation in various languages.