"A digital currency wallet" matching MCP servers:

Enables an AI to build, inspect, test, and render logic circuits as .dig files, then open them in the Digital app.

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.

A local MCP server that routes QA reviews through deterministic profiles and risk-based escalation while keeping source evidence and final decisions with the host.

Renders HTML in a headless browser and detects layout defects numerically, such as text overlaps, placeholder leftovers, and element overflow, without requiring baseline images.

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

AI-powered QA testing platform — catches JS errors, accessibility failures, visual regressions, API loops, security issues, and more via Chrome DevTools MCP

MCP bridge that lets any MCP client drive the Alethia runtime for agent-native E2E testing. It provides a per-step policy gate that blocks destructive actions with a cryptographic audit trail.

Enables MCP clients to run fresh audits of any public web page on mobile or desktop, returning category scores, lab metrics, and prioritized findings for performance, accessibility, best practices, and SEO. Each call performs a new uncached audit, so users can compare results within a conversation.

An MCP server that reads test reports and provides regression analysis tools for comparing runs, identifying regressions, fixes, and persistent failures.

Enables AI assistants to summarize Playwright test runs, identify flaky tests, heuristically triage failures, generate Playwright test stubs, and retrieve recent GitHub CI status through MCP tool calls.

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

Automated QA for .NET microservice backends and React frontends, enabling API contract testing, UI crawling, YAML flows, and run diffing against a stage environment.

Enables passive security scanning of HAR files from QA test automation to detect missing security headers, insecure cookies, CORS misconfigurations, and leaked secrets, without making any live requests.

Zero-Config, Fully AI-Managed End-to-End Testing for all code gen platforms.

Enables AI tools to operate IBM i 5250 green screens, driving and testing interactive RPG programs by seeing screens, typing into fields, and pressing keys.

Enables coding agents to independently verify web changes by scanning pages, inspecting UI structure, generating Playwright reproductions, and executing tests with structured QA evidence.

Probes your live API and classifies why each endpoint failed (root cause, evidence, and a calibrated confidence level), exposed over MCP so your AI assistant debugs from evidence instead of guessing. Works with FastAPI, Express, Next.js, tRPC, and GraphQL.

An MCP server that enables automated quality control for generated images, evaluating them against reference images and rules to return structured scores and issues.

Reads a small-business website like a customer would and shows where they get stuck. Every AI answer has to quote the page, and the server checks the quote before it counts.

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

Enables editorial content QA over the Model Context Protocol with four local tools that analyze readability, AI-sounding language, SEO on-page factors, and produce full reports where every finding includes an actionable fix.

Compares Figma frames to live pages, checking colors, fonts, and border radii, and generates a shareable HTML report.

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

A reusable MCP server for QA automation testing that enables running tests across web apps, APIs, CLI tools, and more directly from Claude Desktop.

Checks a site for AI crawlers: which of 11 AI crawlers its robots.txt lets in, whether its llms.txt follows llmstxt.org, and what its home page says without scripts. Findings with evidence; no scores, no model calls, no telemetry.

Enables users to check HTTP status codes, find broken same-origin links, review SEO basics, and inspect common security headers with polite, permission-based crawling from any MCP client.

Provides an LLM with a real Chromium browser to perform web tasks, recording every action into a structured trace for later verification of goal completion.

Turns JUnit XML test reports from CI into queryable QA metrics, letting AI assistants answer plain-English questions about failures, flaky tests, regressions pinned to a commit, root-cause clusters, and performance drift. Also generates ready-to-share QA health reports and self-contained HTML dashboards without logging into separate tools.

Gives AI agents a real Chromium browser via Playwright to QA-test web apps, with pass/fail assertions, network mocking, recorded traces and reports, and safety guards that block live payment traffic. Test outcomes come back as explained verdicts suitable for review or email delivery.

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.