"User interface design or related topics" matching MCP servers:

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.

Verifies AI agent completion claims by checking that claimed output files exist and are fresh, acting as a lightweight physical telemetry gate.

Detects breaking changes in a TypeScript library's public API and recommends the semver bump, comparing two npm releases, git refs or a working tree. Each finding is graded proven or review-only.

Enables AI coding tools to validate local projects and publish or update applications to Bedengan App Hub via API, with automated checks and live deployment without manual approval.

Deploys and manages projects on Swigs Cloud from any MCP client, turning folders or git repositories into live HTTPS sites in about two minutes.

Enables coding agents to access approved user stories and acceptance criteria via MCP, with tools to list, retrieve, update, and check story requirements against the working tree.

Publishes what an AI made — a page, a markdown or PDF doc, or a whole site — to a live HTTPS link. Runs locally over stdio from the @drop2run/mcp npm package, or hosted at https://mcp.dropto.run/mcp for chats that connect by URL.

Before you move to Python 3.15 (or 3.15t), find every dependency with no wheel for it on your platforms — and the bump that fixes it. CLI, GitHub Action, MCP server.

Lets any MCP-capable client connect to the public Codemagic REST API to list apps and teams, inspect build history, and optionally trigger or cancel builds and manage team settings. Runs locally over stdio with the user's own API token and configurable read-only, build, or administrative permission modes.

Checks whether software dependencies (npm packages, GitHub Actions, MCP servers, Docker images) are alive, dormant, abandoned, archived, or deleted using dated observations from lastseen.dev.

Enables agents and CI pipelines to audit MCP servers and agent tool-chains by statically scanning repositories, local checkouts, tools/list exports, or live endpoints for risks such as destructive actions without confirmation, mismatched safety annotations, injection surfaces, credential or PII exposure, and unguarded command, path, or URL sinks. All checks are read-only and never execute the scanned code or call tools/call.

MCP server for DCI API enabling AI models to extract and analyze DCI jobs, components, and topics, with integrations for Google Drive, Jira, GitHub, and Red Hat support cases.

Enables management of Railway.app infrastructure through natural language, including deploying services from GitHub repos or Docker images, managing environment variables, monitoring deployments, and configuring networking and volumes.

Enables autonomous AI agents and developers to interact with the GitHub REST API for repositories, pull requests, issues, actions, releases, tags, and user/org data through MCP tools. It uses token-efficient response summaries and optional 3-tier read-only safeguards to keep interactions safe and context-window friendly.

A Model Context Protocol server that lets development agents work with Odoo.sh from their development environment, listing branches, watching builds, and streaming logs. It sits on top of the odouche Python library's public interface.

A comprehensive MCP server for Dokploy that provides 14 action-based tools covering the full DevOps lifecycle with minimal token usage. It enables AI assistants to manage projects, applications, databases, domains, servers, and more through a unified interface.

Lets MCP clients such as Claude or ChatGPT find the pull requests waiting on your review, read their details and changed files, check CI status, post comments, and submit approve, request-changes, or comment reviews. It runs locally over stdio with a personal token or as a remote server with its own OAuth 2.1 authorization server (PKCE, dynamic client registration, per-client consent, and refresh-token rotation) so GitHub is only used to sign the user in.

MCP server for agent-controlled deployment on Pier, exposing 14 tools mapped to Pier REST API endpoints for managing deployments via stdio or HTTP.

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.

Enables AI assistants to manage and operate Venelx CI/CD pipelines through the Venelx REST API, including listing projects, triggering builds, checking status, tailing logs, accessing artifacts and workers, and diagnosing signing or GitHub access.

MCP server that runs Flyway repair + migrate across multiple database projects for QA or Development environments.

A local MCP server that exposes Blacksmith.sh's usage and cost data, enabling queries for CI spend through agents, alerts, or dashboards.

Enables AI assistants to answer questions about nixpkgs packages using nixkeeper's public data, covering outdated versions, Hydra build status and failures, update-bot attempts, related PRs and issues, CVEs, and what needs attention for a maintainer or team. It runs locally over stdio, reads only public data, and requires no account or token.

Enables managing GitHub Actions workflows through MCP, including listing and inspecting workflows, triggering workflow_dispatch and repository_dispatch events, and monitoring or canceling workflow runs.

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.

Enables agents to inspect and manage the Nix CI server's projects, jobsets, evaluations, builds, build steps, logs, and queue across multiple instances, and with credentials trigger evaluations, restart or cancel or bump builds, and create or edit projects and jobsets.

Enables management of Amazon Managed Workflows for Apache Airflow (MWAA) environments and operations including DAG management, workflow execution monitoring, and access to Airflow connections and variables through a unified interface.

An MCP server implementing a 7-stage agentic frontend workflow—from design audit to PR review—including AI-driven component generation, browser validation, E2E testing, and CI self-healing.

Self-hosted coding agent that runs Claude Code or Codex in your own infrastructure and works from Slack. Exposes codebase investigation, file edits, branch and PR creation, allowlisted command execution, CI status and an audit log as MCP tools, keeping every credential behind the tool layer instead of handing it to the model.

Connects AI assistants to GitHub Actions workflows to monitor CI/CD pipelines, view run logs, diagnose failures, and optionally trigger or manage workflows with granular permission controls.