Best MCP Servers for Frontend Developers in 2026

Frontend development has always been about bridging gaps. The gap between design and implementation. The gap between writing code and verifying it actually works. The gap between "I think this API changed" and finding the current docs. Model Context Protocol (MCP) servers close these gaps by connecting your AI coding assistant directly to the tools you already depend on.

Instead of copy-pasting Figma specs, switching tabs to read React docs, or manually firing up a browser to test a layout, MCP servers let your editor do it within the conversation. You describe what you need, and the assistant has real access to your designs, your browser, your documentation, and your component libraries.

This guide covers the best MCP servers for frontend developers right now, what each one actually does, and how they work together as a stack.

Playwright MCP -- Browser Testing Without Leaving Your Editor

Author: Microsoft | Tools: 20 | Setup: Zero-config (npx)

Playwright MCP is the most capable browser automation server available. Built by Microsoft on top of the Playwright testing framework, it gives your AI assistant direct control over a real browser -- Chrome, Firefox, or WebKit. That means your assistant can navigate to pages, click buttons, fill out forms, take screenshots, and read accessibility snapshots, all within your coding session.

What It Does

The server exposes around 20 tools that cover the full range of browser interaction. Your assistant can navigate to any URL, capture the accessibility tree of a page (which is more useful than a screenshot for understanding structure), interact with elements by clicking or typing, manage tabs, handle file uploads, and evaluate arbitrary JavaScript in the page context.

The key distinction from older browser tools: Playwright MCP works with accessibility snapshots rather than relying solely on screenshots and coordinates. This makes interactions far more reliable. The assistant sees the page the way a screen reader does -- structured, labeled, and semantically meaningful.

How It Helps in Practice

Say you are building a multi-step form and want to verify the tab order and validation behavior. Instead of opening the browser, clicking through each field, and checking the console, you can ask your assistant to navigate to localhost:3000/signup, fill in each field, submit the form with invalid data, and report what error messages appear and whether focus moves correctly.

Or you have a responsive layout that needs to look right at three breakpoints. The assistant can resize the viewport, take a screenshot at each width, and tell you if anything overflows or collapses unexpectedly.

The feedback loop drops from minutes to seconds. You stay in your editor, the assistant handles the browser, and you see the results immediately.

Configuration

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["-y", "@playwright/mcp"]
    }
  }
}

No API keys, no environment variables. It runs locally and launches a browser instance on demand.

Figma MCP -- Design Handoff That Actually Works

Author: Community | Tools: 8 | Requires: Figma access token

The perennial frontend problem: a designer hands you a Figma file and you spend the next hour eyeballing spacing values, guessing at color tokens, and trying to match the layout in code. Figma MCP connects your AI assistant directly to the Figma API, so it can read design files, extract tokens, and inspect individual layers without you opening Figma at all.

What It Does

Figma MCP provides tools to access your Figma files and extract structured data from them. Your assistant can pull design tokens (colors, typography, spacing), inspect specific components and their properties, read auto-layout settings, and understand the layer hierarchy of any frame or page.

This is not about generating pixel-perfect code from a screenshot. It is about giving the assistant the same information you would get from the Figma inspect panel -- padding values, font sizes, color hex codes, border radii -- so it can write accurate CSS or Tailwind classes the first time.

How It Helps in Practice

You are implementing a card component. Instead of switching to Figma, clicking on the card, noting the 16px padding, the #1c1917 text color, the 8px border radius, and then translating all of that into Tailwind classes, you tell your assistant to look at the "ProductCard" component in your Figma file. It reads the design data and writes the component with the correct spacing, colors, and typography already applied.

This is especially valuable for teams where designers update Figma frequently. Rather than manually re-checking specs every time something changes, the assistant can pull the latest values directly.

Configuration

{
  "mcpServers": {
    "figma": {
      "command": "npx",
      "args": ["-y", "figma-mcp"],
      "env": {
        "FIGMA_ACCESS_TOKEN": "your-figma-token"
      }
    }
  }
}

You will need a Figma personal access token, which you can generate from your Figma account settings.

Context7 MCP -- Documentation That Is Actually Current

Author: Upstash | Tools: 2 | Setup: Zero-config (npx)

Every frontend developer has experienced this: you ask your AI assistant how to use a React 19 feature, and it gives you an answer based on React 17 patterns. Or you ask about the Next.js App Router and get Pages Router code. The assistant's training data is always somewhat out of date, and frontend frameworks move fast.

Context7 MCP solves this by pulling live documentation and code examples directly from library sources into the conversation context. When your assistant needs to reference how useActionState works in React 19 or how server actions are defined in Next.js 15, it fetches the current docs rather than relying on memorized (and potentially stale) information.

What It Does

The server has just two tools, but they are powerful. The first resolves a library name to a Context7-compatible ID. The second queries that library's documentation with a specific question and returns relevant documentation snippets and code examples. Context7 indexes documentation for thousands of libraries and frameworks, and the results include version-specific information.

How It Helps in Practice

You are migrating a project from Next.js 14 to Next.js 15. The API for route handlers changed, generateStaticParams has new behavior, and the caching defaults are different. Instead of keeping the migration guide open in a browser tab and cross-referencing it manually, you ask your assistant to look up the specific changes. It queries Context7, gets the current documentation, and applies the correct patterns to your code.

Or you are using a library like Framer Motion for the first time. Rather than getting suggestions based on an older API (the assistant's training data might predate recent breaking changes), Context7 ensures the assistant sees the latest docs and writes code that actually works with the version you have installed.

Configuration

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp"]
    }
  }
}

No API key required. It works out of the box.

Magic MCP (21st.dev) -- Production-Ready UI Components on Demand

Author: 21st.dev | Tools: 4 | Requires: 21st.dev API key

Building UI components from scratch is time-consuming. Even with a component library like shadcn/ui, you still spend time customizing, composing, and styling. Magic MCP connects your assistant to 21st.dev's library of professionally designed React and Tailwind components, letting you generate and integrate polished UI elements directly from a description.

What It Does

Magic MCP provides four tools: a component builder that generates new components matching your requirements, a component search to find existing components in the 21st.dev library, a component refiner to improve existing components in your codebase, and a logo search to find brand SVG logos. The components it generates use React and Tailwind CSS, and they come styled, accessible, and ready to drop into your project.

How It Helps in Practice

You need a pricing table with three tiers, feature comparison, and a highlighted "popular" plan. Instead of building it from scratch or hunting through component libraries, you describe what you need. Magic MCP generates a complete, styled component that matches modern design patterns. You review it, adjust the content, and move on.

The refiner tool is particularly useful when you have a working component that looks rough. Point it at your existing file, describe what you want improved -- "make the spacing more consistent, add hover states, improve the responsive behavior" -- and it returns an upgraded version.

This is not a replacement for a design system. It is a way to get from zero to a solid starting point faster, especially for landing pages, marketing sites, dashboards, and other UI-heavy work where you need professional-looking components quickly.

Configuration

{
  "mcpServers": {
    "magic": {
      "command": "npx",
      "args": ["-y", "@21st-dev/magic"],
      "env": {
        "TWENTY_FIRST_API_KEY": "your-api-key"
      }
    }
  }
}

You will need an API key from 21st.dev.

Browserbase MCP -- Cloud Browser Automation at Scale

Author: Browserbase | Tools: varies | Requires: Browserbase API key

Playwright MCP runs a browser on your local machine, which is great for development and quick checks. But what if you need to test against multiple browser configurations, run automation in CI, or interact with pages that require specific geolocation or network conditions? Browserbase MCP gives your assistant access to cloud-hosted browser sessions managed by the Browserbase platform.

What It Does

Browserbase provides headless browser infrastructure in the cloud. The MCP server connects your assistant to this infrastructure, letting it create browser sessions, navigate pages, interact with elements, capture screenshots, and extract content -- all running on remote browsers rather than your local machine.

The main advantages over local browser automation: you can run sessions in parallel, configure proxy and geolocation settings, and avoid the overhead of running browsers locally. For frontend developers, this is useful for cross-browser testing, visual regression checks against production URLs, and scraping reference implementations.

How It Helps in Practice

You want to check how your deployed staging site renders across different scenarios. Your assistant can spin up a Browserbase session, navigate to your staging URL, capture screenshots at different viewport sizes, and compare them. Since the browser runs in the cloud, this works even if you are on a low-powered machine or running in a CI environment.

Another use case: you are building a component and want to see how a competitor implements a similar pattern. The assistant can navigate to their site, capture the DOM structure, and extract the relevant CSS -- giving you reference data without leaving your editor.

Configuration

{
  "mcpServers": {
    "browserbase": {
      "command": "npx",
      "args": ["-y", "@browserbasehq/mcp"],
      "env": {
        "BROWSERBASE_API_KEY": "your-api-key",
        "BROWSERBASE_PROJECT_ID": "your-project-id"
      }
    }
  }
}

You will need a Browserbase account and API key from browserbase.com.

Bonus: Tailwind CSS MCP

Worth a quick mention since it pairs naturally with the servers above. Tailwind CSS MCP gives your assistant full context about Tailwind's utility classes, configuration options, and plugin system. If you are using Tailwind (and most frontend projects are at this point), this server helps the assistant write correct, idiomatic Tailwind classes instead of guessing or using outdated utility names.

It is especially useful alongside Magic MCP and Figma MCP. When the assistant generates a component or translates a design, Tailwind MCP ensures the class names it reaches for actually exist in your version of Tailwind.

The Frontend Developer Stack -- Combining Everything

Each of these servers is useful on its own. Together, they cover the entire frontend development workflow:

1. Design: Figma MCP reads your design specs. Magic MCP generates the component shell.
2. Implementation: Context7 MCP ensures the assistant uses current framework APIs. Tailwind MCP keeps the styling accurate.
3. Verification: Playwright MCP opens a browser, navigates to your running app, and checks that the result matches expectations.

Here is what that workflow looks like in practice. You tell your assistant: "Look at the Hero section in our Figma file and build it as a React component." It reads the design tokens from Figma, checks the current Next.js docs via Context7 to use the right patterns, generates the component with Magic MCP as a starting point or writes it from scratch using Figma's specs, applies correct Tailwind classes, and then opens the page in Playwright to verify the layout renders properly.

That is five context switches and two browser tabs eliminated. The assistant handles the mechanical parts while you focus on the decisions that require human judgment -- does this feel right, does the interaction make sense, is the hierarchy clear.

Getting Started

You do not have to set up all five servers at once. Start with the ones that address your biggest pain points:

• Spending too much time testing in the browser? Start with Playwright MCP.
• Constantly switching to Figma to check specs? Add Figma MCP.
• Getting outdated API suggestions? Context7 MCP fixes that immediately.
• Building lots of UI from scratch? Magic MCP accelerates component creation.

Once you have a feel for how MCP servers work, adding more is straightforward. Each one is a few lines of JSON in your editor's configuration file.

For a pre-configured setup with all the servers above, check out the Frontend Developer Stack on stackmcp.dev. It includes the full configuration for Claude Code, Cursor, Windsurf, and other supported clients -- copy, paste, and start building.