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"Information about Fetch function or service" matching MCP servers:

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    An MCP server that provides information about Utkarsh, including bio, skills, work experience, and portfolio projects, accessible via local stdio or remote HTTP with OAuth.
    6
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    An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.
    11
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    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.
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    MIT
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    Servo-powered MCP server for JS-aware web fetching, content extraction, crawling, and software-rendered screenshots — Chromium-free single binary.
    6
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    Apache 2.0
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    A minimal Model Context Protocol (MCP) service for integration with external platforms. Provides CRUD operations, reference data fetching, and authentication capabilities.
    7
  • A
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    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.
    2
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    Apache 2.0
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    An MCP server that enables AI assistants to fetch web content in multiple formats (HTML, JSON, text, Markdown) with intelligent content extraction, chunk management, and browser automation support.
    5
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    MIT
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    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.
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    MIT
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    MCP server for web page fetching (converting to Markdown/text with automatic fallback between Tavily and Firecrawl) and web search via Tavily.
    2
    MIT
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    Enables browser-based web page fetching with automatic cookie management and CSS selector extraction, supporting JavaScript rendering and authenticated sessions through a Chrome extension.
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    MIT
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    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.
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    1