"How to generate HTML code from Figma design files" matching MCP servers:

The pixel-diff verifier for Figma-to-code: converts a frame to HTML, React, Next.js or Tailwind with design tokens, then renders it and pixel-diffs it against Figma until it converges. Also verifies your own running app.

Automates the conversion of Figma designs into TypeScript React components and integrates with GitHub to create pull requests for the generated code. It includes visual regression testing with Playwright and accessibility validation to ensure implementations match the original designs.

Compares Figma frames to live pages, checking colors, fonts, and border radii, and generates a shareable HTML report.

Generates test files locally (images, PDFs, CSVs, corrupted files, etc.) from natural language prompts, with configurable sizes and safety limits.

Thin HTTP wrapper for the Generate-Data.com API, enabling data generation, schema proposal, and field type listing through MCP tools.

Review AI-generated HTML slides or reports in the browser, anchor comments to specific passages, and have the agent fix them via MCP, with tools for inspection, commenting, layout checking, and export to PDF/PPTX.

Enables AI coding agents to audit rendered web pages for design and UI defects by measuring geometry, styles, contrast and motion in a local browser and judging them against evidence-tiered rules. Findings come back as short, fixable items expressed in the project's own design tokens, while only sanitized numeric measurements leave the machine.

Enables agents to compare a live web page against a Figma frame and receive a bounded JSON verdict of design deviations, without sending screenshots or mockups through the model context.

Compare design and implementation screenshots using pixel-by-pixel analysis, generating visual diff images and metrics.

Enables AI assistants to read and write Figma designs, manage design tokens and variables, create components and FigJam boards, and run accessibility and design-code parity checks through a bidirectional Figma integration.

Enables AI assistants to surgically extract and analyze code, reducing token usage and costs by up to 200x compared to reading entire files.

Extracts an analytics event taxonomy from Figma designs, flags drift against later design changes and exports it as a tracking plan.

Enables AI coding agents to submit code, files, or diffs for deterministic checks and optional re-verified LLM repair, returning cleaned code, structured findings, and a pass/fixed/reject verdict.

An MCP server that measures how faithfully one UI reproduces another, returning a score and actionable findings for improvement.

Enables automated HTML export quality assurance with structured receipts, audit logs, and reviewer-ready evidence through a paid remote MCP server.

Enables creating, validating, and simulating Crack-style story chat through a unified MCP server, with template and keyword management, automated project audits, session play, and persona-separated testing to prevent omniscience.

Wraps Claude Code's code-review skill so any MCP client can run reviews over a PR, branch, working directory, or last commit, with five parallel reviewer subagents, low-to-max effort levels, and optional fix mode that edits files in place. Also supports follow-up questions on a previous review session and a free environment self-check.

AI-powered continuous code assistant for Cursor, VS Code, Antigravity, and Claude Code via MCP, providing tools for code review, testing, documentation, and quality analysis.

Enables AI coding assistants to generate, score, run, and auto-heal Playwright E2E tests while enforcing domain-driven page object architecture and anti-flakiness rules.

Enables AI assistants to perform comprehensive code reviews of local uncommitted changes by combining git diffs with static analysis from linters like ESLint and TypeScript. Returns structured JSON feedback with findings, suggestions, and quality assessments powered by Google's Gemini CLI.

Enables MCP-compatible agents to audit an entire repository through one question, detecting stacks and running parallel checks for dead code, dependencies, security, CVEs, complexity, and lint across Laravel/PHP, Python, TypeScript/React, and Flutter/Dart. It returns normalized, severity-sorted findings with false positives flagged and secret values kept out of the output.

Scaffolds new MCP servers for the OpenSIN-Code ecosystem with templates for Python, Node, Go; provides tools to add tools, test, validate, register, publish, and audit servers.

Enables turning requirements documents into RFCs, functional test scenarios, and a traceability matrix, with tools to parse, diff, and verify coverage.

AI-powered code review tool that detects AI-generated code defects invisible to traditional linters — hallucinated packages, deprecated APIs, cross-file contradictions, hidden security anti-patterns, and over-engineering. Works as a standalone CLI, GitHub Action, or MCP server. Supports TypeScript, Python, Java, Go, and Kotlin. Free for individuals, no API key required.

Hybrid code quality analysis server combining fast logic-based checks with AI-powered deep analysis, offering tools for summaries, quick wins, and trend tracking.

Enables LLMs to access AST-based code structure maps, token-efficient skeletons, and analyze diff impact and blast radius for regression testing.

Enables auditing codebases for production readiness, including linting, testing, CI/CD, security, branch conventions, architecture, and an A–F quality scorecard via MCP tools.

Enables Codex CLI to perform code reviews on uncommitted changes via an MCP tool, keeping review context separate from the main session.

Enables comprehensive GitHub PR reviews through Cursor's AI by fetching PR diffs, running static analysis tools (ESLint, Prettier, TypeScript, Semgrep), executing tests, and generating detailed code review reports with inline comments.

A code review MCP server built on FastMCP, supporting security audit, style check, complexity analysis, and bug pattern detection for Python code.

An MCP server for verifying AI-generated code quality, security, and performance, addressing trust gaps in AI coding assistants.

MCP server providing code review, auto-fix, test running, linting, formatting, and refactor suggestions for Claude Code.

Enables AI coding agents to verify code patches locally with sub-50ms neuro-symbolic analysis, detecting structural issues like dependency cycles and interface drift without consuming output tokens.

An MCP server for automated code review of GitHub pull requests. It checks security, quality, and license issues, and provides tools to list, inspect, and review PRs.

Enables coding agents to select bounded Python source context, run configured checks, and retrieve compact failure reports, diagnostic comparisons, and freshness evidence tied to the checked code.

Gives coding agents local access to a project's code-quality rules, located findings, previewable safe fixes, schema details, dependency advisories, and quality-gate results, with analysis confined to the project root and secrets masked.

MCP server for automated code review using AI agents. It analyzes code diffs or file paths for bugs, security issues, and style violations.

Enables AI agents to validate design system constraints, including WCAG accessibility, token relationships, and consistency, through MCP tools like validate, explain, and suggest-fix.

This MCP server provides a tool to generate manual test cases in Markdown or CSV format from documentation files and custom rules. It supports text and PDF inputs and can leverage LLM sampling to automate the creation of detailed test scenarios.

Enables agents to turn a plain-language description of a device into a complete, validated build package — parts list, 3D-printable parts, wiring, firmware code, picture assembly guide, and tests — that anyone can assemble without electronics experience.