"User interface design or related topics" 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.

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

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

Enables AI assistants to research topics, map and analyze codebases, curate development context, and generate product requirement documents, user stories, task lists, fullstack starter kits, and multi-step workflows. It runs as an MCP server over stdio or SSE, or as a CLI/REPL interface for natural-language software development tasks.

Enables fetching daily Product Hunt launches as dataset rows with maker names, product website and company domain, X and LinkedIn links, votes, rank, and topics. Users can filter by specific days or days back, topics, keywords, and minimum votes, or skip already-seen launches on a schedule.

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

MCP server that synthesizes user feedback from multiple sources into actionable insights. Aggregates reviews, support tickets, and survey responses to identify patterns and priorities.

An MCP server for accelerator programs to query, review, and manage startup applications via natural language. It enables searching applications by sector or impact score, fetching full application details with reviewer notes, listing pending reviews, and flagging applications for panel discussion.

Enables read-only queries across App Store Connect and Google Play from one toolset, letting users list apps, track release states, and review merged store feedback without extra dependencies or credentials in demo mode.

Enables AI assistants to validate drafted user stories against INVEST and acceptance-criteria rules, suggest splits for oversized stories, and export finished backlogs to Markdown, CSV, or Jira CSV.

MCP server for AI agents providing 300 curated Japanese home & lifestyle products across 31 categories. Features mm-precision dimension search, related-item chains (1 product → 3-5 accessories), shelf+storage coordination, and Rakuten/Amazon affiliate links.

Enables AI coding agents to inspect user feedback on live web apps, read full reports and recording timelines, update status, and reply in threads so fixes can be checked and confirmed by reporters.

Lets AI assistants query a live feedback collection database in plain language, retrieving entry counts broken down by type and classification status, and listing individual submissions filtered by type, urgency, or sentiment. Access is read-only and privacy-preserving, so answers can be produced without exposing submitter email addresses.

Creates synthetic user profiles from research data to validate service designs through natural conversation.

Enables agents to securely collect and submit user feedback (voices) into VOD, with per-submission confirmation, batch size limits, host-identity sourcing, and declarative guardrails that block database access and credential reads. Also exposes read-only tools for version info, auth self-check, and reviewing locally submitted entries.

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.

Generate SWOT analyses, competitive comparisons, and browse 100+ pre-built industry examples for any company, brand, or topic. Supports 12 languages.

MCP server for reading public Featurebase feedback boards, enabling agents to query user feedback, posts, comments, and statistics.

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

Provides access to Product Hunt data via the Product Hunt API v2, enabling queries for posts, collections, users, topics, comments, goals, and maker groups.

Enables AI agents to generate structured PRDs, decompose epics into testable user stories, validate Given/When/Then acceptance criteria, and export markdown backlogs.

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.

MCP server for product analytics using dot plots to visualize individual user activity, with tools for user classification, aha moment detection, retention analysis, and generating shareable HTML reports.

Captures and organizes requirements as markdown, tracks relationships between personas, requirements, use cases, and user stories.

An AI-powered Product Management Operating System that provides tools for PRDs, user stories, prioritization, competitor analysis, roadmaps, KPIs, meeting notes, and product strategy via MCP clients.

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.

Enables management of Azure DevOps work items including Epics, Features, User Stories, Tasks, and Bugs through natural language. Supports CRUD operations, WIQL queries, work item relationships, and retrieval of project metadata such as iterations and area paths.

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.