"Information about a cursor" matching MCP servers:

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 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.

MCP server that brings Trustabl static reliability scanning into Cursor, letting agents inventory agents/tools, flag risky patterns, score production readiness, and write SARIF reports.

Agent Ready is an AI agent readability scanner — point it at any public URL and get back a 0–100 score plus per-check remediation hints for every failing check. This package wraps the same engine that powers agent-ready.dev as an MCP server, so Claude Desktop, Claude Code, Cursor, Cline, VS Code, and Windsurf can run scans inline and explain the results.

Enables recurring website maintenance checks — domain registration, SSL certificates, uptime, technical SEO and accessibility — across a portfolio of client sites, using publicly available information. Results are returned as structured data and can be sorted by urgency into a client-ready report.

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.

MCP server for Allure TestOps — lets an LLM agent (Claude Code, Cursor, Windsurf, etc.) inspect test quality without leaving the chat.

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

Scan any project or website for privacy compliance issues directly in your AI coding tool. Detects tracking tech, cookies, and third-party data collection. Works in Claude Code, Cursor, and Windsurf.

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 coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

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.

Exposes six evidence-oriented tools over stdio so MCP clients can navigate unfamiliar code, ask typed questions about repository evidence, review completed changes, and select existing tests.

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

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.

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.

Prediction stability engine for AI agents. Evaluate model stability, detect ghosts, probe any LLM for instability, monitor fleet drift. 20 tools + 1 resource. Works with Claude, Cursor, VS Code.

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

Enables Cursor AI to query SonarQube for pull request metrics, issues, file issues, and duplication reports directly, without manual data export.

Enables AI agents to verify that every number, money amount, percentage, count, date and time in an AI-written Japanese summary or meeting minutes actually appears in the source text, normalizing kanji numerals, 万/億, full-width digits, Japanese era years, 午前/午後 times and 割/分 rates before comparison. Exposes a read-only check tool that reports each fact as supported, approximate, mismatched or unsupported, plus an extraction tool that lists the quantities in a text.

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

MCP server for Mockzilla. Lets coding agents like Claude Code, Claude Desktop, Cursor and Gemini CLI install the Mockzilla CLI and run API mocks from an OpenAPI spec or a single endpoint. Log in from the agent to deploy hosted mocks on mockzilla.org

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.