"A platform for collaborative design and prototyping (Figma)" matching MCP servers:

This MCP server renders UI design artifacts headlessly, runs deterministic linters, and manages stateful design review loops with an independent vision critic.

Enables high-craft web design and engineering through OKLCH color systems, fluid typography, Three.js 3D scenes, frontend security audits, multi-viewport capture, and Python-backed design verification.

Provides software design guidance via MCP, including SOLID principles, design patterns, pattern scaffolding, and heuristic code-smell detection to help review and refactor code.

An MCP server that provides design-pattern selection expertise to AI coding agents.

Analyzes codebases for cross-platform compatibility issues, detecting platform-specific APIs, file path problems, line ending inconsistencies, and shell command incompatibilities across Windows, macOS, and Linux.

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.

Enables mapping Figma designs to a project's own component library — shadcn/ui, antd, mui, or Astryx — by resolving variants against authoritative component contracts instead of guessing. It compiles Figma nodes into code and then deterministically writes the resulting files into the codebase, cleaning node-id artifacts and updating exports.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

Enables AI coding tools like Cursor to access Figma design data through the Model Context Protocol, allowing accurate implementation of designs across frameworks. Based on NCDS (NHN Commerce Design System) with enhanced source generation capabilities.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables AI assistants to discover, document, and generate React code using the pastel-themed Baby Design UI component library. It provides tools for retrieving design tokens and component specifications to streamline the development of user interfaces.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables AI coding agents to extract Figma design data and convert it into Flutter widgets and screens. Supports theme setup, component analysis, asset exports, and provides Flutter implementation guidance from Figma designs.

Provides a real API for coding agents to look up design system components, props, and tokens, preventing guessed answers.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Enables coding agents to read ground truth about a React design system — components, props, cva variants, tokens and docs — over MCP so they write UI that matches it. It also exposes a check_ui linter agents run on their own output, returning rule ids, locations and concrete fixes before review.

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.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Enables MCP-compatible AI clients to inspect Godot project and editor state, observe opted-in running scenes, manage processes, and apply permission-scoped guarded edits. It starts read-only and exposes additional capabilities only through explicit command-line flags.

Uses Claude as an independent second-model reviewer inside Codex, performing reviews on disposable Git snapshots with structured findings.

Connects Figma designs to AI agents, enabling extraction of production-ready code, assets, and design tokens through natural language descriptions. Supports React, Vue, CSS, and Tailwind with real-time design system analysis.

Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.

Codebase Q&A agent with git-history awareness. Explore a repo with list_files, read_file, and search, plus git log, blame, and diff and a repo map to answer "why did this change?" questions. Runs locally as an MCP server for Claude Code and other MCP clients.

Provides intelligent design pattern recommendations using semantic search through a comprehensive catalog of 200+ patterns across 20 categories. Users can describe programming problems in natural language to discover appropriate design patterns with contextual recommendations.

Transforms codebases into a living knowledge graph with AI-powered code analysis, security scanning, and persistent semantic memory, leveraging Oracle 26ai vector and property graph capabilities.

MCP server giving Claude (and any MCP-compatible client) live access to SAP systems via ADT for reading, searching, editing, and activating ABAP objects.

Fetches and structures Ant Design v4 component documentation into JSON format, enabling AI agents to search, analyze, and retrieve component metadata, APIs, and examples.

MCP server that brings Trustabl static reliability scanning into Cursor, letting agents inventory agents/tools, flag risky patterns, score production readiness, and write SARIF reports.

MCP server that enables AI agents to analyze legacy code, plan and execute software migrations, generate modern source code, and validate production-ready artifacts through a multi-agent pipeline.

A high-performance MCP server integrated with a Django analytics dashboard, featuring 19 tools for AI interaction tracking, AST code analysis, and real-time token efficiency metrics.