"Hybrid RAG System for Indexing DevOps eBooks and Online Documentation" matching MCP servers:

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.

Gives Claude control of a real, headed Chromium browser via Playwright, enabling web navigation, clicking, typing, screenshots, and JavaScript evaluation.

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 ✨

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

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.

An MCP server that retrieves and cleans official documentation for popular AI/Python libraries via web search and LLM processing, exposing a get_docs tool.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

A fault-tolerant, stealth-enabled Model Context Protocol (MCP) server for web searching and content fetching. Built for AI Agents (Cursor, Claude Code, OpenCode), it uses a stealth browser engine to fetch pages, dynamically handles SPAs/React, and converts bloat into token-optimized Markdown.

This server crawls documentation sites and provides search and page-retrieval tools through MCP, enabling users to query indexed documentation content.

Provides a local MCP server for AI-powered web crawling on Windows, enabling agents to directly crawl and extract web content without requiring WSL.

📈 提供股票、加密货币的数据查询和分析功能MCP服务器

A Model Context Protocol server that provides web search capabilities using Google Custom Search API and webpage content extraction functionality.

Enables AI assistants to fetch, search, and organize menu information from For Five Coffee café. Provides access to complete menu data, category filtering, and item search capabilities through both MCP and REST API interfaces.

Enables intelligent search through FastMCP documentation using TF-IDF indexing, along with utility tools for arithmetic operations, text hashing, and web page content extraction via Jina Reader.

Enables AI agents to drive real Google Chrome with stealth and captcha handling, using Patchright to avoid bot detection.