"A simple test or quiz" matching MCP servers:

This server assists with A/B testing by calculating required sample sizes and necessary test durations. It provides a suite of significance tests, including z-test, chi-square, and Fisher exact methods, while evaluating the risks of premature peeking.

Enables Claude to generate executable test cases from decision tables in CSV, JSON, or Markdown formats. Provides intelligent test planning guidance and generates Playwright/API test code with TypeScript support.

Lets AI agents ask which tests a Playwright or Vitest project contains, which are skipped and why, by reading the test source files statically. Nothing is executed, written, or sent over the network, and answers come back as paged JSON with summaries, filters, and scan diagnostics.

An automated testing MCP server that converts test cases into a fixed JSON format, runs them case-by-case, and provides a visual report with review support for cases that cannot be automatically judged.

An MCP server that gives AI coding agents the ability to analyze a Python repository's test health: coverage, flaky tests, and ML-based pull-request risk prediction.

An intelligent MCP toolset for software testers that monitors code changes, analyzes test impact, recommends tests, and assesses risk, supporting multiple AI coding frameworks via dual transport modes.

Enables AI coding agents to run Foundry tests and builds and receive compact, structured failure reports with decoded reasons, fuzz counterexamples or invariant call sequences, logs, and short traces. It also distinguishes genuine test failures from cases where tests did not run, returning actionable errors instead of false zero-failure results.

Enables management of TestMu AI test projects, test cases, test runs, and integration with Jira, HyperExecute, and AI insights through natural language.

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

Test coverage intelligence MCP server for TypeScript and JavaScript projects. Analyzes coverage reports to find untested functions, computes cyclomatic complexity, and suggests test cases locally.

Gives AI assistants structured access to a test automation stack so they can run suites, inspect failure details and captured output, look up test data, and file Jira bugs (or drafts) only for confirmed defects. It works with any framework that emits JUnit XML, letting assistants triage flaky, environment, and test-data issues alongside real product defects.

Auto-generates and manages test cases for any project from feature descriptions, with tools for adding, listing, updating, and exporting test cases via Claude.

MCP server that generates ISO/IEC/IEEE 29119-3 compliant test plan drafts from project information, providing a structured resource for the standard's test plan outline and a tool for draft creation.

A specialized testing harness that enables AI assistants to thoroughly test other MCP servers by connecting to them, discovering their tools/resources/prompts, executing test calls, and performing end-to-end validation with LLM integration.

Contract-driven test enforcement and reporting for LLM-generated code via MCP, VS Code, Copilot CLI, Claude, Cursor, or Python SDK.

MCP server for self-hosted qTest Manager, enabling natural-language management of projects, modules, test cases, requirements, traceability, and test execution via the qTest REST API.

Enables LLM clients to generate standardized test cases, perform quality control with lint scoring, convert to Xray/Jira format, and compose test suites (Smoke/Regression/E2E) with coverage analysis.

This is an unofficial, community-built MCP server for qTest Manager. Not affiliated with or endorsed by Tricentis. Browse projects, modules, and test cases Create/delete test design modules Create and manage test execution cycles Add test cases into execution suites

Generates test files locally (images, PDFs, CSVs, corrupted files, etc.) from natural language prompts, with configurable sizes and safety limits.

Enables Claude Code to analyze flaky tests, find failure patterns, suggest fixes, and query test history, failure patterns, and correlated failures from Test Ledger.

ocal-first, read-only MCP server that audits repository test architecture across ten language ecosystems. It returns deterministic evidence-backed findings, blockers, rankings, and repository-native test plans without modifying source files.

Generates structured, comprehensive test cases from user stories, API specs, or raw text, with automatic Excel export and Playwright automation code generation.

MCP server voor het maken van testcases. Maar ook voor het inzetten van test technieken en heuristieken.

Enables test execution and management through MCP clients, allowing retrieval of projects, listing tests, executing tests with browser selection, and monitoring results.

Enables AI agents to test WCF/SOAP and REST services directly, with WSDL discovery, authenticated calls, and deterministic assertions. Supports YAML multi-step scenarios with variable extraction, JSONPath/JSON Schema checks, and CI runs without spawning PowerShell or curl.

Enables AI clients to analyze software test results and defect records, producing explainable GO, CONDITIONAL_GO, or NO_GO release-readiness recommendations with deterministic risk scoring, failed-test retrieval, defect hotspot ranking, and regression test planning.

Enables AI assistants to test Terminal User Interface (TUI) applications by launching, interacting with, and verifying programmatic output and behavior.

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.

MCP server for AI-powered QA analysis. It enables analyzing test failures, identifying root causes, suggesting fixes, classifying defects, detecting flaky tests, and generating test cases and bug reports.

Self-healing test automation MCP server that diagnoses and fixes issues in mobile and web apps, including a one-command vibe-check to prioritize broken functionality.

Enables AI models to test and interact with Electron applications using Playwright, supporting CDP connection and app launch modes.

An MCP protocol test server with 35 test tools across 3 transport modes for compatibility verification, plus a development Skill for the Stepfun AI desktop assistant to help write MCP servers.

MCP server for deterministic local test execution and normalized test result reporting, supporting pytest and Jest with coverage summaries.

Enables MCP-compatible clients to execute Playwright-based UI and API test suites (login, inventory, checkout, API, regression) and receive structured JSON test results.

Enables AI agents to run configured test suites, inspect failures and logs, compare runs, and draft or file bug reports only after human confirmation, with safeguards such as selector validation, environment isolation, timeouts, and redaction.

Enables AI clients to search, create, update, link, and inspect ALM projects, work items, LiveDocs, test runs, comments, attachments, and workflow actions in 2023-era deployments, with read-only and dry-run safeguards.

Enables coding agents to deterministically audit test files for fake or fragile tests (no assertions, tautological or swallowed assertions, focused/skipped tests, fixed sleeps, real network calls) and to run the project's actual test runner, treating an exit-0 run with zero tests as a failure and flagging changed source files that lack test coverage.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

An MCP server that generates, runs, and triages tests by introspecting Python modules or web pages, using structured LLM outputs for scenario generation and failure analysis.

Enables agents and users to author, scaffold, validate, mock, and run OpenTofu/Terraform native tests (.tftest.hcl), using the tofu CLI with Terraform fallback. It provides reference, generation, validation, and execution tools for the test framework.