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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.
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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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    Connects AI assistants to Swiss environmental data from BAFU, including air quality, hydrology, natural hazards, and open datasets, with no API keys required.
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    MIT
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    Enable Claude Code, Cursor, or your favorite LLM to interact with Xcode, building your projects the same way you do, and seeing the same errors. Greatly increases productivity when working on iOS, iPadOS, macOS, visionOS, tvOS projects & Swift packages - or any time you might use Xcode.
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    MIT
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    Provides tools to monitor host system health including CPU load, disk usage, and network status while enabling file system management tasks like searching and moving files. It includes built-in safety guards to prevent operations on critical system directories.
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    Assists in migrating HeroUI v2 and NextUI projects to HeroUI v3 beta through automated project scanning, file analysis, and guided code rewrites. It provides specialized tools for auditing Tailwind configurations and comparing component changes using an integrated documentation corpus.
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    AGPL 3.0
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    An MCP server that enables autonomous code development by pulling GitHub issues, fixing them in a sandboxed environment, running tests, opening PRs, and promoting to production upon human approval.
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    AGPL 3.0
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    Provides AI assistants direct access to 700+ statistical datasets about the Czech Republic, including population, economy, prices, wages, employment, industry, agriculture, trade, tourism, environment, and more.
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    MIT
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    DevEyes is a Model Context Protocol (MCP) server that captures screenshots from your local development environment and automatically optimizes them for LLM consumption.
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    MIT