"Resources for Frontend and UI Design" matching MCP servers:

Enables AI coding agents to plan, build, and review websites and product interfaces with a persistent, user-led process, including design direction, component contracts, and implementation review.

Enables AI assistants to securely access design files, artboards, tokens, PRDs, and design-to-code workflows, letting users implement or match designs from delivery URLs.

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

Enables discovery of a company's public feature board (Canny, Featurebase, Frill, UserVoice, Productboard) from its domain and retrieval of its most-voted requests with votes, status, comments, and the integrations customers ask for. Supports monitoring runs that surface only new requests, status changes, and vote jumps since a prior run using a saved state key.

Enables conversion of business briefs or authenticated saved projects into structured handoffs for the Lovable plugin, with both anonymous basic mode and authenticated full mode including knowledge base, SEO, WCAG, security, and roadmap guidance.

Enables a local product design workflow in Goose, from evidence and framing through journey approval, handoff, and QA.

Enables agents to fetch app reviews from Apple's App Store and Google Play per country store, newest first, filtered by date or star rating, with each scheduled run returning only reviews not seen before. It also returns a per-app, per-country rating trend row with the current rating, rating count, change since the last run, and the star split of new reviews.

Enables transforming grounded AI answers into interactive information views such as comparisons, plans, checklists, timelines, and decision views, while preserving a plain-language fallback.

MCP server for AI-first UI design that enables Codex to create, preview, and commit UI changes to a self-hosted design tool.

Inline review comments for markdown specs and design docs. Agents request human review mid-task via MCP and pause until you send feedback.

Enables natural language control of OmniPlan 4 on macOS, allowing creation and manipulation of tasks, resources, dependencies, and project metadata through MCP-compatible agents.

Turns product feedback pinned to a live UI into an actionable backlog for Claude Code — list comments, open one with its target element's HTML, computed styles and screenshot, and update its status.

Enables traceable requirement discovery, technical alignment, and ISO-aligned process checking through deterministic MCP tools and resources, without requiring an embedded LLM.

Enables AI assistants to create interactive prototypes, annotate UI elements, generate PRDs, and track which annotations and documents need updating after changes.

Enables reading release lifecycle status, recent written reviews and bulk monthly report CSVs for allowlisted apps, and searching the full pinned Play API catalog with each method's implementation status. Read-only by design, with keyless authentication, explicit app allowlists and bounded, GET-only access to official Google interfaces.

Model work domains using Cognitive Work Analysis, score tasks for AI suitability, and design organisations.

Enables coding agents to generate software engineering documents — PRD, SRS, ERD, SDD, design spec, traceability matrix, and agent instructions — as Markdown so they build from written specifications instead of guessing. Unsupplied inputs are marked [NOT SUPPLIED] or UNMAPPED rather than filled with invented data.

Enables sellers to read Turkish marketplace order, return, stock, review, and price data, detect SLA breaches, stock and price traps, and unanswered reviews, and generate a prioritized daily Turkish action report. It is read-only by design and never changes seller accounts.

Enables agents to create and drive instrumented Design Thinking sessions, inspect the append-only journal, and retrieve dossiers and handoff specs through MCP tools and resources.

Guides product design through mandatory phases—discovery, definition, prioritization, flows, and verification—so an AI assistant can run a project from problem to verified scope, with strict or free progression modes.