"Markdown MCP (Model Context Protocol) Server" matching MCP servers:

Extracts clean markdown content from web pages using Playwright, automatically filtering out navigation, headers, and ads while preserving formatting. Handles JavaScript-heavy sites and dynamic content, making web content easily readable and processable.

An MCP server that enables users to download webpages as markdown files using r.jina.ai service, with features for configurable download directories and automatic date-stamped filenames.

Provides live web and document intelligence, including search, company news, scraping, crawling, structured extraction, document parsing, brand intelligence, screenshots, website monitoring, and batch jobs.

Gaia Protocol—a planetary DAO for global resource management using quantum-entangled ledgers and algorithmic governance.

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.

MCP server that converts web page URLs to Markdown via the Zyla API.

Converts any URL to clean, LLM-ready Markdown by removing ads, navigation, and other clutter, using Mozilla Readability and Turndown.

MCP server for AI agents -- fetch any URL with full JavaScript rendering (Playwright/Chromium) and convert to clean, token-efficient markdown. Works on React, Vue, Angular, and any JS-heavy page. Includes web search, batch fetching, binary file download, LRU cache, SSRF protection, and structured output.

Local-first documentation for AI agents

Fetches web pages and converts them to clean, readable markdown format by extracting main content while removing navigation, ads, and other non-essential elements to minimize token usage.

Enables converting any webpage to clean LLM-ready markdown, with extraction, article, and news modes, using a real browser with anti-bot handling.

An MCP server that fetches web pages and extracts clean, AI-usable context from them, enabling tools for link discovery, content search, and integrated fetch-and-search operations.

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 website content and converts it to Markdown format with AI-powered content cleanup, ad removal, and full OpenAPI/Swagger specification support for easy processing by AI assistants.

Enables AI assistants to summarize, take notes on, and answer questions about YouTube, Bilibili, and Xiaohongshu videos by providing subtitles and local speech transcription with timestamps via MCP.

Enables MCP clients to look up domains, URLs, IPs/ASNs, email addresses, phone numbers, crypto wallets, and business records; run autonomous investigations; collect crawl artifacts; and perform evidence-backed scam checks with traceable provenance.

Crawl entire websites, sitemaps, and documentation portals into clean, LLM-ready Markdown without ads, cookie popups, or noise for RAG pipelines and AI agents.

Converts raw HTML to clean Markdown, optimized for LLM token efficiency and deterministic processing.

Fetches and caches public site pages from llms.txt and returns structured answers with source URLs, enabling verification of claims against live public content.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

Enables converting Markdown and HTML tables into structured JSON records with automatic column type inference and format normalization. It also offers supporting web-agent primitives such as DOM semantic pruning, form field auto-mapping, and execution trajectory evaluation, all via a zero-dependency MCP server.

Converts any webpage URL to clean, well-formed Markdown with title and H1 preservation, URL validation, and graceful fallbacks. One of 16 single-purpose servers in an open-source monorepo with full CI/CD to PyPI.

Fetch web pages into clean, RAG-ready Markdown with a verifiable provenance record — source URL, UTC fetch time, SHA-256 of the response body, redirect chain, and an extraction-fidelity metric. Writes WARC archives and honours robots.txt/ai.txt, so every document an agent reads can be re-verified and cited.

A read-only MCP server that gives AI agents the web as compact, ranked, verified evidence — no API keys, no cloud retrieval, all models local.

Extracts the content from any URL and returns it in clean, optimized Markdown for LLMs. Supports standalone monetization via Dodo Payments (HTTP 402).

Enables AI agents to extract clean LLM-ready markdown and readable articles from web pages, parse metadata, tables, links, feeds, and contact info, audit paywalls, SEO, and XSS, and access these tools via Streamable HTTP or a stdio bridge.

Classifies company sites as B2B or B2C and as SaaS, online store, agency, manufacturer, wholesaler, distributor, or marketplace, while matching your own buyer phrases and returning the quoted text and page as evidence. Runs deterministically on Apify, giving one row per domain with grace-free status rows for unreachable, blocked, or empty sites.

Bintu fetches a public webpage through MCP and returns cleaned text or chunks for AI-agent workflows. It is designed to reduce page clutter while preserving the source content needed for context.

Transforms social media links (TikTok, Instagram, YouTube) and videos into text context with transcription, OCR, and metadata for AI agents like Claude and Cursor.

Scrapes dynamic web pages via headless Chrome, stripping clutter and ads, and returns clean Markdown or JSON content. It integrates with Cloudflare Workflows and monetizes each extraction through the x402 protocol.