"Methods to Scrape a Website and Fetch Markdown Files from GitHub" matching MCP servers:

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

Fetches a URL and returns the main content as clean Markdown, using plain HTTP when possible and headless Chromium for JavaScript-rendered or bot-protected pages.

Enables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.

Recommends study designs for a research question, states the identifying assumptions to verify, and returns runnable R, Python, or Julia code with a formatted citation for every source, drawn from methodological textbooks, an R package, and research articles on causal inference, econometrics, statistics, and machine learning.

Enables AI models to fetch real web pages while automatically bypassing anti-bot protections, login walls, and JS rendering, then returns clean, token-efficient article content.

Enables Claude Cowork on Windows to read, write, edit, list, find, and regex-search files inside a WSL distribution over stdio, accepting Windows and Linux path spellings. Access is confined to server-fixed roots, with secrets files refused, key-shaped strings redacted, and no delete or move operations.

Fetch URLs and return clean, LLM-ready markdown with metadata and layered prompt injection defense. Configurable timeouts, word limits, JS rendering, and link extraction. All-in-one MCP server + CLI.

Enables LLMs to fetch and extract web content as markdown with browser impersonation to bypass basic bot detection.

A dedicated web content fetching and conversion MCP server that provides tools for fetching, converting, and extracting data from web pages.

Generates test files locally (images, PDFs, CSVs, corrupted files, etc.) from natural language prompts, with configurable sizes and safety limits.

Enables agents to run Google searches across web, images, videos, news, books, shopping, scholar, patents, and AI Mode without an API key, using a cookie-bootstrapped HTTP fast path with a headless browser fallback, proxy rotation, and adaptive cooldowns. Every response reports an explicit status (ok, blocked, rate_limited, empty, error) so results are never fabricated.

Write your resume in Markdown and export to PDF with AI assistance, using a built-in stylesheet and tools for conversion.

A web-fetch MCP server for LLM agents that fetches pages with an escalation ladder through different engines, raising FetchBlocked instead of returning blocked content.

This server enables LLMs to retrieve and process content from web pages, converting HTML to markdown for easier consumption.

Enables AI assistants to access content from authenticated web pages by opening a real browser for manual login and session capture. It saves browser profiles locally so users only need to log in once per service for future automated access.

Provides AI-visibility scoring and site auditing capabilities for websites, enabling agents to check how sites appear in AI engines like ChatGPT and Perplexity, run full SEO/security audits, and monitor changes over time.

Provides tools to fetch web content in HTML, JSON, text, or Markdown formats via MCP. Supports custom headers, length limits, and start index.

MCP server for web page fetching (converting to Markdown/text with automatic fallback between Tavily and Firecrawl) and web search via Tavily.

A generic Markdown vault MCP server with FTS5 full-text search, semantic vector search, frontmatter-aware indexing, incremental reindexing, and non-markdown attachment support that exposes search, read, write, and edit tools.

A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.