Best MCP Servers for Full-Stack Developers in 2026
The best MCP servers for full-stack development — Supabase for backend, Stripe for payments, Playwright for testing, Vercel for deployment.
Full-stack development means owning the entire path from database schema to deployed application. On any given day, you might write a migration, build an API endpoint, wire up a payment flow, write a test, and push a deployment. Each of those tasks lives in a different tool with its own dashboard, its own docs, and its own context. The constant switching between your editor, a database GUI, the Stripe dashboard, a browser for testing, and Vercel's deployment view adds up to hours of lost focus every week.
MCP servers collapse this workflow back into one place. Instead of juggling six browser tabs, your AI assistant talks directly to your database, your payment provider, your test runner, and your hosting platform. You describe what you need, and the assistant handles the round-trip. The result is a development loop where code, data, infrastructure, and verification all happen inside the same conversation.
This guide covers six MCP servers that together form a complete full-stack development environment. Each one handles a different layer of the stack, and they connect end-to-end: from writing a database migration to verifying the deployed result in a real browser.
Supabase MCP -- Your Entire Backend in One Server
Author: Supabase (Official) | Tools: 25 | Requires: Supabase access token
Supabase gives full-stack developers Postgres, authentication, file storage, and edge functions under one roof. The MCP server extends that by letting your AI assistant operate on all of it without leaving your editor.
What It Does
The server exposes 25 tools covering database operations, schema management, migrations, edge function deployment, and development branches. Your assistant can run SQL queries, create and alter tables, apply migrations, list extensions, deploy serverless functions, and create isolated branches for safe experimentation. It can also generate TypeScript types from your schema, which is invaluable when your frontend and backend share type definitions.
How It Helps in Practice
You are adding a new feature that needs a subscriptions table. Instead of opening the Supabase dashboard, clicking through the table editor, then manually writing a migration file, you describe the table structure to your assistant. It drafts the SQL, applies it as a tracked migration through the MCP server, and verifies the table was created correctly -- all in one conversation. If you want to test safely, it can create a development branch first, apply the migration there, and merge it to production once you are satisfied.
The TypeScript type generation is another workflow accelerator. After changing your schema, the assistant regenerates your types and updates the relevant frontend code to match. No more out-of-sync interfaces.
Configuration
{
"mcpServers": {
"supabase": {
"command": "npx",
"args": ["-y", "@supabase/mcp-server-supabase"],
"env": {
"SUPABASE_ACCESS_TOKEN": "your-supabase-token"
}
}
}
}
At roughly 12,875 tokens for tool definitions, this is the heaviest server in the stack. But it replaces what would otherwise be your most frequent source of context-switching.
Stripe MCP -- Payments Without the Dashboard
Author: Stripe (Official) | Tools: 15 | Requires: Stripe secret key
Payments are the one part of full-stack development where mistakes cost real money. The Stripe MCP server gives your assistant direct access to the Stripe API through their official agent toolkit, turning billing operations into conversational tasks.
What It Does
The server handles customers, products, prices, subscriptions, invoices, payment links, checkout sessions, refunds, and disputes. Your assistant can create a new pricing tier, check the status of a subscription, look up failed payments, and generate checkout URLs -- all without you navigating Stripe's (powerful but dense) dashboard.
How It Helps in Practice
You are building a SaaS and need to add a new pricing plan. The workflow without MCP: open Stripe dashboard, create a product, add prices for monthly and annual billing, update your frontend to reference the new price IDs, test the checkout flow. With Stripe MCP: describe the plan to your assistant. It creates the product and prices in Stripe, hands you the price IDs, and you wire them into your code in the same conversation.
During debugging, this is even more valuable. A user reports they were charged twice. Instead of switching to Stripe, searching for the customer, reviewing their payment history, and then coming back to your code to find the bug, you ask the assistant to look up the customer's recent charges. It surfaces the data, cross-references it with your subscription logic, and pinpoints the issue.
Configuration
{
"mcpServers": {
"stripe": {
"command": "npx",
"args": ["-y", "@stripe/mcp", "--tools=all"],
"env": {
"STRIPE_SECRET_KEY": "your-stripe-secret-key"
}
}
}
}
Use a restricted API key in production with only the permissions you need. For development, a test mode key keeps things safe.
GitHub MCP -- Source Control as Part of the Conversation
Author: Anthropic (Official) | Tools: 20 | Requires: GitHub personal access token
Source control is the backbone of full-stack development, and the GitHub MCP server makes it part of your AI workflow rather than a separate context switch.
What It Does
Twenty tools covering repositories, pull requests, issues, branches, commit history, GitHub Actions workflows, and code search. Your assistant can create PRs with meaningful descriptions, open issues with proper labels, check CI status, search code across your repositories, and manage branches.
How It Helps in Practice
You finish implementing a feature that touches both the backend API and the frontend components. Instead of switching to the browser to create a PR, writing the description from memory, and hoping you captured all the relevant changes, you ask your assistant to create the PR. It already has the full context of what changed and why, so the description is accurate and detailed.
The code search tool is underrated for full-stack work. You are refactoring a data model and need to find every API endpoint and frontend component that references the old field name. The assistant can search across your entire repository and surface every reference, ensuring nothing gets missed during the migration.
When CI fails after a push, the assistant can check the workflow status, retrieve the error logs, and correlate them with the recent changes -- all without opening GitHub in a browser.
Configuration
{
"mcpServers": {
"github": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "your-github-token"
}
}
}
}
Playwright MCP -- Test the Full Stack in a Real Browser
Author: Microsoft (Official) | Tools: 20 | Setup: Zero-config (npx)
Full-stack developers need to verify that the frontend and backend actually work together. Playwright MCP gives your assistant control over a real browser, letting it navigate your running application, interact with it, and report the results.
What It Does
The server provides full browser automation: navigation, clicking, typing, form filling, file uploads, screenshot capture, accessibility snapshots, tab management, JavaScript evaluation, and network request inspection. The key design choice is that it works with accessibility snapshots rather than screenshots, making interactions more reliable and semantically meaningful.
How It Helps in Practice
You have just wired up a new checkout flow that spans multiple pages: product selection, cart review, Stripe checkout, and a success confirmation. Testing this manually means clicking through four pages every time you change something. With Playwright MCP, you describe the flow and the assistant executes it. It navigates to the product page, adds an item, proceeds to checkout, and verifies that each step renders correctly and the API responses are what you expect.
The network request inspection is particularly valuable for full-stack work. After running through a flow, the assistant can list all the API calls that were made, their status codes, and their response data. This lets you verify that your frontend is calling the right endpoints with the right parameters, without opening browser DevTools.
Configuration
{
"mcpServers": {
"playwright": {
"command": "npx",
"args": ["-y", "@playwright/mcp"]
}
}
}
No API keys, no environment variables. It launches a local browser instance on demand.
Vercel MCP -- Deploy and Monitor From Your Editor
Author: Community | Tools: 12 | Requires: Vercel API token
Deployment is the last mile of full-stack development. The Vercel MCP server lets your assistant check deployments, review build logs, manage environment variables, and configure domains without opening the Vercel dashboard.
What It Does
Twelve tools covering deployment listing and inspection, build log retrieval, project settings management, environment variable configuration, and domain setup. Your assistant can tell you whether your latest deployment succeeded, what failed in the build, and what environment variables are set for each environment.
How It Helps in Practice
You push a change and your deployment fails. Without MCP, you open Vercel, navigate to the project, find the deployment, read through the build logs, identify the error, switch back to your editor, fix it, and push again. With Vercel MCP, you ask the assistant to check the latest deployment. It retrieves the build logs, identifies the failing step, correlates it with the code you just changed, and suggests a fix. The debugging round-trip drops from several minutes to seconds.
Environment variable management is another common pain point for full-stack projects. You often have different variables for development, preview, and production. The assistant can list what is set for each environment, add new variables, or update existing ones -- all from the same conversation where you are writing the code that depends on them.
Configuration
{
"mcpServers": {
"vercel": {
"command": "npx",
"args": ["-y", "vercel-mcp"],
"env": {
"VERCEL_API_TOKEN": "your-vercel-token"
}
}
}
}
Context7 MCP -- Current Docs for Every Layer of the Stack
Author: Upstash (Official) | Tools: 2 | Setup: Zero-config (npx)
Full-stack developers work with more frameworks and libraries than anyone. React on the frontend, Next.js for the framework, Supabase client libraries, Stripe SDK, Tailwind for styling -- each with its own API surface and its own release cadence. Context7 MCP ensures your assistant always references current documentation rather than relying on potentially stale training data.
What It Does
Two tools that punch well above their weight. The first resolves a library name to a Context7-compatible identifier. The second queries that library's documentation with a specific question and returns relevant snippets and code examples. Context7 indexes thousands of libraries and returns version-aware results.
How It Helps in Practice
You are using the Supabase JavaScript client and need to set up a row-level security policy that interacts with the auth context. The Supabase client API has changed several times, and the assistant's training data might not reflect the latest patterns. Instead of guessing, it queries Context7 for the current Supabase RLS documentation and writes the policy using the correct API.
This is even more valuable for full-stack developers because you are working across multiple libraries simultaneously. In a single conversation, the assistant might need to reference Next.js server actions, Stripe webhook handling, and Supabase realtime subscriptions. Context7 ensures each of those references is current.
Configuration
{
"mcpServers": {
"context7": {
"command": "npx",
"args": ["-y", "@upstash/context7-mcp"]
}
}
}
At just 2 tools and roughly 1,030 tokens, this is the lightest server in the stack and arguably the highest value-per-token.
The Stack -- Combining Everything
These six servers cover the complete full-stack development loop:
- Schema and data: Supabase MCP handles your database, auth, and backend logic.
- Payments: Stripe MCP manages the billing layer.
- Source control: GitHub MCP keeps your code organized with PRs, issues, and CI.
- Testing: Playwright MCP verifies the complete application in a real browser.
- Deployment: Vercel MCP pushes your code to production and monitors it.
- Documentation: Context7 MCP keeps every library reference up to date.
The real power is in the connections between them. You can describe a feature end-to-end -- "add a premium tier with monthly billing, create the database table, add the API endpoint, wire up the checkout, and test the flow" -- and the assistant has direct access to every system involved. It creates the Stripe products, writes the migration in Supabase, pushes the code through GitHub, verifies the deployment on Vercel, and tests the checkout in Playwright. That is a workflow that used to span five different browser tabs and an hour of context-switching, compressed into a single conversation.
Getting Started
You do not need all six on day one. Start with the servers that match your current bottleneck:
- Spending most of your time on database and backend? Start with Supabase MCP and Context7 MCP.
- Dealing with payments? Add Stripe MCP when you are ready to monetize.
- Tired of manual testing? Playwright MCP delivers immediate value.
- Want deployment visibility? Vercel MCP keeps you informed without tab-switching.
- Working on a team? GitHub MCP makes PRs and issues part of your AI workflow.
The total token budget for all six servers is approximately 48,400 tokens -- well under 25% of a 200K context window, leaving plenty of room for your code and conversation.
For the complete pre-configured stack, visit stackmcp.dev/stacks/fullstack-web. Select your AI client, enter your API keys, and copy the generated config. The entire setup takes less than a minute.