"All items" matching MCP servers:

Compares weekly work reports against original data to identify incorrect values, missing items, and narrative inconsistencies, providing verification results to users.

Runs SQL statements in memory against real PostgreSQL 18 and SQLite 3.49 engines after loading your schema and sample rows, returning each statement's own verdict: errors with line, column, and hints, constraint violations, or the resulting rows. It also compares both dialects to expose portability differences, all with no database server, network, or API key.

Enables AI assistants to read, write, and reorganize Polarion ALM data, including documents, work items, test runs, and traceability links.

Enables coding agents to see the UI they just changed without flooding their context window, returning a structured text summary (console errors, failed requests, overflow, layout shifts, accessibility violations) plus small crops of only the regions that changed since the last capture. All output is fitted into a chosen token budget, with captures stored on disk and evicted automatically.

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

MCP server for autonomous end-to-end testing with Wopee.io. Analyzes web applications, generates and executes Playwright-based functional tests, and validates results — all driven by natural language commands.

Enables AI agents to run the same regex in real Python, JavaScript, and optional regex engines side by side, flag disagreements, confirm ReDoS by timing adversarial input in a killed child process, explain patterns, convert Python/JavaScript syntax, preview substitutions, and generate verified test suggestions. All execution is isolated in throwaway processes with hard timeouts and strict input caps.

MCP server that lets coding agents test AI agents. Create YAML test cases, snapshot golden baselines, check for regressions, and generate visual reports all from inside Claude Code or any MCP-compatible tool. Works with LangGraph, CrewAI, OpenAI, Claude, Mistral, and any HTTP API.

Enables LLMs to systematically develop and validate regex patterns by defining test cases with expected matches, testing patterns against them, and iteratively refining until all requirements are satisfied.

Enables an LLM to control Solibri through 25 MCP tools, covering model inspection, rule checking, BCF issue handling, presentations, and Autorun batch verification, all restricted to a local workspace.

Enables coding agents to read feedback left on a running app—including the thread, page, pointed-at element, and screenshots—and to report back, manage feedback items, and publish review links.

Enables AI assistants to statically lint and explain Dockerfiles, compose files, and .dockerignore rules with line-numbered findings, concrete fixes, layer-cache analysis, multi-stage suggestions, and safe fix previews, all without a Docker daemon.

Runtime quality validation for AI agent outputs. Detect hallucinations, enforce scope compliance, and score output quality — all via MCP.

A local MCP server that connects Claude Desktop, Claude Code, and Claude Chat to your Azure DevOps organization, enabling querying work items, reviewing pull requests, triggering pipelines, searching code, managing test plans, and updating wikis using plain language.

An MCP (Model Context Protocol) server for Codebeamer ALM. Allows Claude and other MCP clients to read and write projects, trackers, and items in Codebeamer using natural language.

Enables AI agents to perform WCAG 2.2 accessibility audits, including contrast ratio checks for color pairs and text-over-image, generate EU legal accessibility declarations, and produce aria-live monitoring snippets, all through local, multilingual tools.

Enables a coding agent to audit a deployed Next.js or Vercel site for SEO and indexing problems such as redirects, robots.txt, sitemaps, canonicals and noindex, returning the CLI's findings as JSON. It also explains issue codes and lists available checks, all read-only over stdio.

The server can be utilized for secure development by listing all packages' CVEs, their affected versions and their fix versions.

Enables AI agents to perform real accessibility audits by controlling a browser, running axe-core scans, reviewing page structure, images, tab order, and WCAG 2.2 color contrast, all over MCP with local session logging.

Enables AI agents to interact with HP ALM / Quality Center for QA workflows including test case management, test execution, defect tracking, and requirements management.

This MCP server lets GitHub Copilot fetch Azure Boards work items by URL or ID and generate QA test cases from the ticket content.

Enables coding agents to run Playwright e2e tests, validate Supabase RLS policies with role-based SQL, typecheck TypeScript, execute npm scripts with parsed test output, and validate Next.js builds, all through a structured MCP interface.

Integrates MantisBT bug tracker into Claude and other MCP clients via the REST API. Read and manage issues, notes, file attachments, tags, relationships, and monitors — with optional offline semantic search across all issues.

Enables read-only exploration of SonarQube projects, explaining why a quality gate is red and searching unresolved issues, reading metrics and source snippets around a finding. All six tools are idempotent and leave the SonarQube instance untouched.

Local read-only MCP server that serves all 66 Suede Agent Skills: skill discovery, install options, SEO and GEO visibility grading, code grading and QA checklists.

Exposes offline LLM-as-judge evaluation tooling to MCP clients so they can benchmark recorded judges against golden labels, diagnose position and verbosity bias, and fit probability calibrators with agreement, cost-frontier, and rubric-template utilities. It deliberately omits command judges, keeping all analysis standard-library and network-free.

Enables AI clients to drive a UI-testing toolbox with 35 tools, including a headless Playwright runner, live Chromium browser with screenshots, test management, and read-only database queries, all through the Model Context Protocol.

Gives AI agents bidirectional control of a Perchance.org generator: reading and editing its source, running eval, driving the live UI, and reading console/errors. It also offers a fast browser-free path for pulling and searching generator source, all through 27 tools over stdio.

Lets AI coding agents simulate venues, replay traces through positioning filters, pinpoint error windows and nearby beacons, and A/B test configuration fixes across scenarios on identical seeds. All results come from the engine's own replay and metrics code, so accuracy claims and regressions can be verified rather than guessed from source.

A test MCP server that exercises all protocol features including tools, resources, prompts, and sampling to help client builders validate implementations.