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    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.
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    MIT
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    Enables AI agents to extract clean, structured web content (articles, tables, links, visual layouts) optimized for LLM token efficiency, with fast response times and optional JavaScript support.
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    MIT
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    A reconnaissance MCP server that discovers web data sources by monitoring network requests and extracting API structures, enabling AI to understand data location and format without full crawling.
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    MIT
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    A local MCP server providing web search, page extraction, and safe browser automation tools for Hermes, Claude Code, and other MCP clients.
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    MIT
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    Provides web search and page fetch tools for LLM assistants, extracting content and enabling text search within fetched pages.
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    MCP server for multi-engine web search and web page fetching, supporting parallel search, content extraction, and optional LLM-powered search summarization and deep search.
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    MIT
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    Enables AI agents to search DuckDuckGo and fetch web content with human-assisted CAPTCHA handling, designed for local use where humans can intervene when bot detection occurs.
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    MIT
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    Converts web pages to Markdown for MCP clients like Claude, with support for single and batch reads, caching, fallback rendering, and SSRF protection.
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    MIT
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    An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.