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    Local MCP server for querying VCD waveform files via SQLite; enables AI agents to ask precise questions about signal values, transitions, and clock cycles without dumping raw VCD text.
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    MIT
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    An MCP server for checking OWASP Top 10 vulnerabilities during API development testing. It analyzes API information and returns security assessment results to help developers identify potential security issues.
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    A lightweight bridge enabling AI agents to perform real-world tasks on Android devices such as app navigation, UI interaction, and automated QA testing without requiring computer-vision pipelines or preprogrammed scripts.
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    MIT
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    A testing server that demonstrates Model Context Protocol features, providing access to user data, todos, and system information through resources, tools for user management and calculations, and prompt templates for various analyses.
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    An MCP server for external computer-use that launches, observes, and interacts with any X11 application via AT-SPI2 accessibility tree and xdotool input injection.
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    MIT
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    A frontend security dependency auditing tool that identifies vulnerabilities in local and remote repositories using the Model Context Protocol. It provides detailed audit information like CVSS scores and dependency chains, generating standardized markdown reports.
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    MIT
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    A lightweight open-source server that enables AI agents to interact with the Windows operating system, allowing for file navigation, application control, UI interaction, and QA testing without requiring computer vision.
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    6,453
    MIT
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    Enables AI agents to control Android devices and emulators through direct UI interaction, allowing app navigation, automated testing, and real-world task execution via ADB without computer vision or scripts.
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    MIT
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    Provides tools to test regex patterns for correctness, performance (ReDoS), and memory usage, and suggests safe rewrites. Enables LLMs to iterate on regex generation with verifiable feedback.
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    MIT
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    An MCP server that runs developer tooling commands (tests, linters, etc.) and returns compact, grouped error summaries to AI agents, reducing token usage and context window pollution. It supports multiple adapters for different tools and provides tools like run_check, diff_runs, and get_log_slice for iterative debugging.
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    MIT