"A server for finding indoor design ideas and inspiration" matching MCP servers:

Provides read-only access to over 100 UX/UI design checklists from Checklist.design, enabling users to search and retrieve design guidance for specific flows and screens. Runs locally without an API key.

Provides access to Ant Design component library documentation and information for automated code generation. Enables searching components by category or keyword, retrieving component props, examples, API documentation, and available icons.

A local-first FastMCP server that indexes offline frontend catalogs (components, patterns, motion libraries) and exposes discover, search, compare, and recommend tools to AI agents via stdio.

Enables AI assistants to retrieve and cite South Korean national construction standards (KDS/KCS) with tables and formula images via the KCSC OpenAPI, and to use design decision trees, validation, and blank Excel templates directly in the chat interface.

A Model Context Protocol server that exposes an airline design system as a queryable knowledge base, enabling AI to discover components, find components for use cases, and scaffold prototypes.

Exposes iDRAC design-system knowledge via a single get_component tool, currently returning placeholder data for known components.

Provides access to Appian's Aurora design system documentation via GitHub, enabling LLMs to query components, layouts, and patterns. It supports both public and internal repositories with features for keyword searching and detailed component guidance.

Turns real technical work from a Claude Code session into a saved, professional design doc with one slash command, no copy-pasting or signup required.

Provides AI agents with component documentation, usage examples, and design tokens from the WordPress Design System, enabling them to follow the latest design system guidance for creating user interfaces.

A standalone MCP server that provides AI assistants with comprehensive access to BrowserStack's design system components, enabling component search, detailed prop analysis, and usage examples from Storybook.

The Wellx Design System MCP enables AI agents to access the single source of truth for Wellx design, including tokens, component specs, rules, and search, ensuring consistent design implementation across surfaces.

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

MCP server for design decisions during a build, providing live UI/animation library docs (via llms.txt) and a curated knowledge base of UX laws and UI conventions. It enables agents to look up real component APIs and apply design best practices, running locally via stdio or hosted on Vercel.

Provides component information, usage guidelines, and code generation for AWS Cloudscape Design System in React, with search and pattern library capabilities.

Serves the complete Sekura Design System—tokens, components, layouts, UX patterns, accessibility contract, and paste-ready code—to MCP-capable tools, enabling agents to build accessible, dark-mode-first interfaces with verified values.

An MCP server that enables AI to interact with the private 'my-design' React component library and design tokens for UI generation and technical support. It provides tools for component searching, API documentation retrieval, and migration guidance based on specific internal design specifications.

Enables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.

A tool that helps users conduct comprehensive research on complex topics by exploring questions in depth, finding relevant sources, and generating structured, well-cited research reports.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.