"fetch" matching MCP servers:

Fetches web pages and converts them to markdown for LLM consumption, supporting chunked reading and raw content extraction.

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.

An MCP server that enables interaction with the YouTube Data API, allowing users to search videos, get video and channel details, analyze trends, and fetch video transcripts.

A minimal MCP server that enables HTTP requests with any method, headers, and body types, supporting large responses through chunked transfers with disk-backed caching.

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

A Model Context Protocol server that allows LLMs to retrieve and convert web content to markdown without robots.txt restrictions.

Enables AI agents to search and compare live UK product prices from marketplaces like eBay and Amazon, returning normalised JSON with direct buy links.

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.

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

A Model Context Protocol (MCP) server that enables Claude or other LLMs to fetch content from URLs, supporting HTML, JSON, text, and images with configurable request parameters.

Enables Claude Code users with third-party APIs to fetch web pages locally, extracting main content without relying on the official Anthropic API.

Enables LLMs to fetch and extract web content using browser automation, OCR, and multiple extraction methods, handling JavaScript rendering and anti-scraping techniques.

Enables LLMs to fetch and extract web page content as markdown, supporting chunked reading via start_index.

An MCP server that helps AI assistants access text content from websites that implement bot detection, bridging the gap between what you can see in your browser and what the AI can access.

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

MCP server that turns DOI, URL, or paper titles into structured Markdown full-text with metadata and assets, enabling AI agents to read beyond abstracts.

Reduces token consumption by 73-87% by cleaning web and API data before it reaches the LLM context window. Supports fetching URLs, searching the web, optimizing JSON, and more.

Enables efficient extraction of specific data from JSON APIs using JSONPath patterns, reducing token usage by up to 99% compared to fetching entire responses. Supports single and batch operations for both JSON extraction and raw text retrieval from URLs.

A content fetcher that sanitizes, size-caps, and caches fetched content, protecting against injection attacks via the Model Context Protocol.

Enables LLMs to fetch and process web content by converting HTML into markdown for easier consumption. It supports chunked reading via pagination and provides configuration options for robots.txt compliance and proxy usage.