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    Open behavioral litmus for MCP servers — grades A–F across tool-output injection, egress, sensitive-data, and adversarial-input, with reproducible, content-addressed evidence. Tools: run_litmus, verify_attestation.
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    Apache 2.0
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    An MCP server that gives AI assistants the ability to check open-source packages for vulnerabilities, enrich findings with real-world exploit intelligence, and statically analyse whether vulnerable code is actually reachable in your project.
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    Apache 2.0
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    MCP server for VibeCheck bug reports. Gives AI assistants direct access to screen recordings, console logs, network requests, user actions, and web vitals — right inside your editor.
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    MIT
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    Enables AI assistants to write and debug integration tests by providing live access to DOM structure and executing code directly in test environments.
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    MIT
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    MCP server for managing Apidog test cases, scenarios, suites, and test data, providing full read/write access to Apidog's test management features.
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    MIT
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    Provides comprehensive access to the W3C WAI-ARIA specification for querying roles, states, properties, and accessibility requirements. It enables developers and AI agents to validate ARIA attributes and receive smart role suggestions based on UI component descriptions.
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    MIT
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    AI-powered code review tool that detects AI-generated code defects invisible to traditional linters — hallucinated packages, deprecated APIs, cross-file contradictions, hidden security anti-patterns, and over-engineering. Works as a standalone CLI, GitHub Action, or MCP server. Supports TypeScript, Python, Java, Go, and Kotlin. Free for individuals, no API key required.
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    Enables deterministic security testing of AI agents that use tools by serving synthetic MCP environments with poisoned data, fake secrets, and privileged actions. Records agent tool calls and evaluates security invariants (e.g., canary leaks, forbidden access, approval binding) without an LLM judge or real systems.
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    MIT
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    Provides programmatic access to BlazeMeter's performance testing platform through MCP tools. Enables users to retrieve test runs, analyze performance data, view error reports, and manage testing resources via natural language interactions.
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    MCP server for Godot Engine enabling AI to autonomously develop, test, and debug games with deterministic playtesting, multiplayer testing, DAP debugging, LSP integration, and token efficiency.
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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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    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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