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    Enables control of the iOS Simulator using the xcrun simctl command-line utility. It allows users to manage devices, install and launch apps, send push notifications, and simulate device features like GPS location.
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    Unified MCP server for Xcode + iOS Simulator — build, deploy, screenshot, and interact with your iOS app from Claude Code, Cursor, or any MCP client. Built in Swift. Single binary. No Node/Python runtime required.
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    MIT
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    Enables AI assistants to manage Xcode projects by listing targets, reading configurations, and triggering builds via the Model Context Protocol. It facilitates natural language interaction with macOS developer tools to streamline iOS app development processes.
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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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    Problem: MCP clients can't efficiently use Xcode CLI tools because the build and simulator listing commands return more than 50k tokens, exceeding MCP limits. Solution: MCP tooling using progressive disclosure with intelligent caching.
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    MCP server for Xcode build automation and log parsing that opens Xcode projects, triggers builds directly in Xcode, and parses build logs to extract errors and warnings.
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    Apache 2.0
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    A code-mode MCP server for the Unraid 7.2+ GraphQL API that exposes search and execute tools, allowing LLM agents to introspect and call any GraphQL field via sandboxed JavaScript.
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    MIT
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    Deterministic SNAP eligibility logic, exposed as auditable Model Context Protocol tools. An AI agent can read, reason, and orchestrate, but the eligibility determination itself is made by versioned, tested, cited code that a caseworker, an auditor, or a court can inspect.
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    Enables efficient analysis of large codebases using Claude Code and Kimi K2.5 through the Model Context Protocol, with session caching and parallel processing to reduce costs and time.
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    MIT