"Information on Security Testing" matching MCP servers:

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

An MCP Server that enables users to interact with Google's On-Demand Scanning API (v1beta1) through natural language, likely providing capabilities for vulnerability scanning and security analysis of containers and artifacts.

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

Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code r

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

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.

A local Ethereum contract analysis tool that retrieves on-chain information, integrates Etherscan and RPC data, and automatically executes view functions to provide comprehensive contract insights.

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

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK. Includes example tools for calculations and greetings, plus system information resources.

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 real-time access to up-to-date library documentation and code examples for any programming library. Helps AI coding assistants deliver accurate, current information instead of relying on outdated training data.

Audits npm package dependencies for security vulnerabilities, providing detailed reports and fix recommendations with MCP integration.

Provides comprehensive documentation about AWS AgentCore framework to GenAI tools, enabling users to build production-ready AI agents with enterprise-grade security, observability, and scalability. Offers guidance on identity management, API integration, monitoring, code execution, memory storage, and tool integration for AI agents.

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

A client library for connecting to and interacting with MCP servers hosted on smcphub.com, allowing users to find, publish, and call tools on MCP servers.

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real