"Automating a QA End-to-End Workflow" matching MCP servers:

mcp-test-runner is an MCP server that lets your AI client (Claude / Cursor / Codex / Gemini) drive your entire QA loop end-to-end: * Run tests across pytest / Jest / Cypress / Go / Maestro — single MCP surface, one env var to switch * Analyze a URL (Web DOM probe) or a live mobile screen (Maestro hierarchy) to extract testable modules + candidate cases * Generate runna

An MCP server for the comprehensive analysis of Swagger 2.0 and OpenAPI 3.x contracts. It allows users to extract detailed information about endpoints, request/response schemas, parameters, and security configurations from API documentation.

Analyzes Playwright test results to provide structured QA insights, including test metrics, failure analysis, bug report drafts, and release-quality recommendations.

An MCP server that brings senior-QA discipline to AI coding assistants, enabling test planning, TDD, mutation testing, and code review.

Enables an AI assistant to author algorithm storyboards, workflow maps, and UI styling through MCP, serving a local web app for visualizing and reviewing them.

MCP server for end-to-end QA automation: generates test scenarios, discovers Playwright locators, creates TypeScript test code, executes tests, and creates GitHub issues for failures.

A standalone runtime and MCP server that executes Claude Code dynamic workflow files through interchangeable agent providers, ensuring compatibility with Claude Code's workflow format.

Evaluates MCP servers by running read-only checks and returning a graded report with an A-F letter grade.

MCP server providing three QA tools: parse JUnit XML reports, detect flaky tests across multiple runs, and evaluate quality gates with pass/fail decisions.

Provides structured spec-driven development workflow tools for AI-assisted software development with sequential spec creation (Requirements → Design → Tasks). Features a real-time web dashboard for monitoring project progress and managing development workflows.

Evidence-driven quality testing for Model Context Protocol servers. It connects as a real client, captures the complete model-facing contract, identifies agent-usability risks, measures context cost, and executes only explicitly approved target calls.

Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.

A specialized MCP server for QA engineering. It provides tools for user story analysis, test strategy, BDD/Gherkin scenarios, contract testing, integration tests, performance plans, security checklists, and CI/CD pipeline generation, accessible from any MCP-compatible IDE or client.

An intelligent MCP server that helps development teams maintain high-quality project documentation by providing an AI-powered workflow for creating comprehensive specifications through requirements, design, and implementation documents.

An MCP server for issue-driven GitLab development, automating requirement analysis, issue creation, branching, coding, merge requests, and issue updates.

An MCP server that guides QA and verification processes by breaking down tasks into manageable steps and providing LLM-driven, confidence-scored tool recommendations.

A local MCP server that gives Cursor Agent three focused QA tools for analyzing user stories, creating test charters, and drafting bug reports, with built-in safety rules and reusable workflows.

Enables AI assistants like Claude Code and Cursor to get evidence-based tool and workflow recommendations from community data.

MCP server for nopCommerce QA automation using Playwright. Enables running tests, viewing reports, analyzing failures, and generating QA dashboards.

Enables AI-powered building, optimization, debugging, and management of n8n workflows directly from Claude. Features workflow analysis, execution monitoring, security audits, drift detection, and intelligent error debugging with best practices guidance.