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    MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.
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    MIT
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    Enables automated access to LoadRunner Cloud APIs for retrieving performance test data, managing projects, and collecting test results. Supports integration with AI clients for building performance engineering workflows and dashboards.
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    Magga lets coding agents prove their changes before claiming they’re done by building the code and verifying that newly added tests fail before the fix and pass after it. It returns evidence-backed verdicts through the verify_change tool and supports 16 languages, including Rust, Go, Python, Solidity, and C/C++.
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    398
    Apache 2.0
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    A multi-language debugger with real-time web UI and 64 MCP tools for LLM-driven debugging, supporting Python, JavaScript, TypeScript, C/C++, Rust, Java, Scala, and WebAssembly.
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    MIT
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    AI-powered exploratory QA agent. Explores web apps like a real user — 18 MCP tools for clicking, filling forms, and navigating. Automatically verifies that actions persist (fake deletes, failed edits). Runs 16 detection types including dead links, SEO, accessibility, and performance checks.
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    MIT
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    A high-performance, multi-protocol API mock server with 18 built-in MCP tools for managing HTTP, GraphQL, gRPC, WebSocket, MQTT, SSE, and SOAP mocks, chaos engineering, stateful CRUD resources, and request verification.
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    Apache 2.0
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    Agent Audit runs Lighthouse against any URL and converts the output into structured fix packs your coding agent can execute. Each fix pack includes the failing audit evidence, CSS selectors to locate the issue in your repo, implementation steps, and acceptance criteria to verify the fix. Covers performance (Core Web Vitals), accessibility, technical SEO, LLM visibility, and llms.txt readiness. Wo
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    MIT
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    Semantix-Verify is an MCP server for semantic validation of AI/LLM outputs. It exposes a single tool, verify_text_intent(text, intent_description, threshold), which uses a local quantized NLI cross-encoder (INT8 ONNX) to return a 0.0–1.0 probability that the text satisfies the given intent — and, when it doesn't, a structured correction prompt for agent retry loops. Useful for building com
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    MIT
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    Enables Claude to perform thorough code reviews by integrating with Codex and Gemini CLIs to provide senior-level feedback on code quality, security, performance, and best practices. Supports reviewing code snippets, individual files, or entire directories with automatic detection of available review tools.
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    4