"Cybersecurity penetration testing frameworks" matching MCP servers:

Enables AI assistants to write and debug integration tests by providing live access to DOM structure and executing code directly in test environments.

Provides tools for frontend testing including code analysis, test generation, test execution, and React component testing for Jest and Cypress frameworks.

A full-stack API automation testing server that parses OpenAPI/Swagger/Postman/HAR specs, generates comprehensive test scenarios and executable code, and provides AI-powered review and auto-fix.

An MCP server that executes tox commands to run Python tests within a project using pytest, allowing users to run all tests or specific test groups, files, cases, or directories.

Provides an isolated workspace for testing candidate code, runs tests, and returns deterministic pass/fail verdicts. Enables automated grading of software engineering solutions by ensuring reproducible test runs.

AI-optimized Vitest interface that provides structured test output, visual debugging context, and intelligent coverage analysis for more effective AI assistance with testing.

An MCP server that enhances sequential thinking with meta-cognitive capabilities including confidence tracking, hypothesis testing, and organized memory storage through graph-based libraries and structured JSON documents.

Enables R package development, testing, jamovi module building, and arbitrary R code execution from MCP clients like VSCode, using an RStudio Desktop integration.

A minimal reference implementation of an MCP server that responds with "Hello, World" via Streamable HTTP. Serves as a baseline for integration testing and MCP client development with production-ready features including health checks, metrics, and containerized deployment.

Provides a universal developer operations and diagnostics toolkit with sandboxed code execution, algorithm stress testing, security secret scanning, AST-based code fixes, and a visual dashboard for ERD, port/process inspection, and differential testing.

Provides a universal bridge to interact with any OpenAI-compatible LLM API (local or cloud), enabling model testing, benchmarking, quality evaluation, and chat operations with performance metrics.

Enables AI models to generate production-ready, 60fps-optimized GSAP animation code from natural language requests. It provides expert-level tools for creating complex sequences, debugging performance issues, and setting up GSAP within modern web frameworks.

An MCP server that automatically optimizes AI prompts using evolutionary algorithms, helping improve prompt performance, creativity, and reliability through iterative testing and refinement.

Facilitates unified execution and result parsing for various testing frameworks, including Bats, Pytest, Flutter, Jest, and Go, through a Model Context Protocol interface.

A production-ready template for creating Model Context Protocol servers with TypeScript, providing tools for efficient testing, development, and deployment.

A TypeScript-based template for building Model Context Protocol servers, featuring fast testing, automated version management, and a clean structure for MCP tool implementations.

A Model Context Protocol server that provides tools for Xcode-related operations, making it easier to work with iOS project management, building, testing, archiving, and deploying apps to both simulators and physical devices.

An AI-powered MCP server that enables natural language interaction with AO (Arweave Operating system) for creating, running, and testing code and handlers without manual coding.

A multi-task MCP server for AI code optimization and testing that integrates with VSCode/Cursor, CLI tools, and autonomous agents. It provides tools for running tests, generating code improvements, and managing objectives across different environments.

Enables AI assistants to access SAP ADT APIs for reading, writing, debugging, deploying, and testing ABAP code through natural language or DSL automation.