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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.
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    A small, dependency-light MCP server that gives AI agents instant Windows file and folder discovery through Voidtools Everything.
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    MIT
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    MCP server that integrates Everything file search engine on Windows, enabling fast file and folder searches using Everything syntax, plus version and status checks.
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    MIT
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    Enables lightning-fast file searching on Windows using Everything SDK, with tools for search, version check, status, and file info retrieval.
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    MIT
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    A single Python package that bundles 18 plug-and-play MCP servers for Claude, allowing users to access Hacker News, Wikipedia, weather, GitHub, and many other services directly from conversations.
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    MIT
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    Enables AI assistants to search local files on Windows using the Everything search engine, supporting basic and complex file searches with filters like date, size, media type, and document type.
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    MIT
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    Integrates Everything Search with Claude Code for lightning-fast file searching across entire drives, up to 400x faster than traditional methods.
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    Enables querying mobile ad revenue from Google AdMob and Huawei Petal Ads through natural language, with read-only tools for account, app, ad unit, and revenue reports.
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    Provides integration with Everything Search Engine allowing powerful file search capabilities through the Model Context Protocol with advanced search options like regex, case sensitivity, and sorting.
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    Enables instant file and folder searching on Windows using Everything's blazing-fast search engine, supporting powerful search syntax including wildcards, regex, size filters, date filters, and comprehensive file information retrieval.
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    Enables instant file searches on Windows using Everything's native SDK, supporting advanced filters, duplicate detection, and content search through MCP tools.
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    MIT
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    Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.
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