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    Enables AI coding assistants to control a persistent Chrome browser through DevTools Protocol. Supports web navigation, screenshots, element interaction, mobile testing, console logs, and network debugging in a stable Docker environment.
    10
    8
    MIT
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    Provides tools for generating fake data using Faker.js, including person, lorem, internet, and more, with support for multiple locales and customizable options.
    175 npm
    MIT
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    A production-oriented reliability, benchmarking, and evaluation system that layers multi-version protocol negotiation, adaptive streaming, connection pooling, and resilience onto an MCP agent runtime.
    3
    Apache 2.0
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    Read visual bug reports from Feedbug in your AI coding agent: screenshot, console logs, failed network requests, session replay and the DOM context of the element the tester clicked. Trace the bug to its source file, comment and resolve without leaving the editor.
    7
    13 npm
    MIT
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    An MCP server that gives coding agents hands and eyes on an Android device, providing 19 tools for building, testing, installing, UI inspection, input, logs, and toolchain diagnosis. It returns concise, structured results to make the build-debug loop efficient.
    19
    1
    MIT
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    Enables AI coding agents to perform real-browser accessibility scanning of localhost pages using Playwright and axe-core, returning WCAG 2.1 violations with structured fix plans.
    3
    48 npm
    1
    MIT
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    Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.
    3
    53 npm
    MIT
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    Enables AI coding agents to diagnose and repair Python Selenium UI automation using real evidence from live DOM and test runs, with enforced safety boundaries and verifiable fixes.
    2
    MIT
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    Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures. Tool: compact_output. Run: npx -y logslim logslim-mcp
    1
    6 npm
    5
    MIT