---
title: "Best MCP Servers for Web Development in 2026"
description: "Curated list of the most useful MCP servers for web developers. Browser debugging, database access, API testing, and more."
keywords: [best MCP servers, MCP servers list, MCP tools, MCP servers web development, Model Context Protocol, AI coding agent]
sidebar_label: "Best MCP Servers"
---

# Best MCP Servers for Web Development in 2026

The **best MCP servers** are the ones that solve real problems without getting in your way. The Model Context Protocol ecosystem has grown significantly since its introduction, and there are now hundreds of servers available across registries, GitHub, and npm. Most web developers do not need hundreds. They need a handful of well-built servers that cover the core workflows: seeing what is happening in the browser, querying a database, making HTTP requests, and managing infrastructure.

This guide covers the most useful MCP servers for web development, organized by category. Rather than listing every server that exists, it focuses on the categories that matter and the criteria you should use to evaluate any server you consider adding to your stack. The ecosystem is still young -- many servers are early-stage, documentation varies, and quality is uneven. That is worth keeping in mind as you browse.

For each category, the recommendations lean toward servers that are actively maintained, have clear tool descriptions, and work reliably across MCP clients. Where a specific server has earned trust through real usage, it is called out. Where the landscape is still fragmented, the guide points you toward the right search terms and evaluation criteria instead of inventing endorsements.

## What Makes a Good MCP Server

Not all MCP servers are created equal. Before diving into categories, here is what separates useful servers from frustrating ones:

**Focused scope.** The best servers do one thing well. A browser debugging server should not also manage your Docker containers. Focused servers have cleaner tool descriptions, fewer bugs, and less surface area for things to go wrong.

**Clear tool descriptions.** Your AI agent reads tool descriptions to decide which tool to call and how. Vague descriptions like "do stuff with the database" lead to incorrect tool calls. Good servers have precise descriptions with parameter documentation that helps the AI construct valid requests on the first try.

**Low friction.** If installing a server requires editing five config files and setting up authentication tokens before you can make a single tool call, most developers will abandon it. The best servers work with a single install command and sensible defaults.

**Reliable error handling.** A server that crashes on invalid input is worse than one that returns a clear error message. Good servers return structured error results that the AI can interpret and recover from. They do not leave the AI guessing about what went wrong.

**Active maintenance.** The MCP specification continues to evolve. Servers that were last updated six months ago may not support current transport features or may have compatibility issues with newer clients.

## Browser and Frontend Debugging

This is where MCP servers provide the most dramatic improvement for web developers. Without a browser-connected server, your AI agent is blind to everything happening in the browser -- layout issues, JavaScript errors, network failures, rendering problems. With one, the AI can see what you see.

### agnt

[agnt](https://github.com/standardbeagle/agnt) provides browser debugging through a reverse proxy with JavaScript injection. It captures screenshots, inspects DOM elements with computed styles, tracks JavaScript errors in real time, logs network traffic, runs accessibility audits, and executes arbitrary JavaScript in the browser context. It also includes a floating indicator for sending messages from the browser to your AI agent, a sketch mode for wireframing, and a design mode for AI-assisted UI iteration.

```bash
# Claude Code marketplace
claude mcp add agnt --plugin agnt@agnt-marketplace

# Manual install
go install github.com/standardbeagle/agnt/cmd/agnt@latest
claude mcp add agnt -s user -- agnt mcp
```

**Strengths:** Deep browser integration through proxy architecture (not just screenshots), real-time error capture, works with any web framework, comprehensive diagnostic API with ~50 primitives. **Limitations:** Requires routing your dev server traffic through the proxy. Adds a layer between your browser and backend that, while lightweight, is a moving part.

### Playwright MCP

The [Playwright MCP server](https://github.com/microsoft/playwright-mcp) from Microsoft uses Playwright for browser automation. It can navigate pages, fill forms, click elements, and extract content. It operates more like an E2E testing tool than a debugging tool -- good for scripted interactions, page navigation, and content extraction.

```bash
npx @anthropic-ai/create-mcp -t @anthropic-ai/mcp-playwright
```

**Strengths:** Full browser automation, handles SPAs and dynamic content, supports multiple browser engines. **Limitations:** Heavier than a proxy-based approach, oriented toward automation rather than live debugging of an already-running dev server.

### Choosing between them

agnt and Playwright MCP solve different problems. agnt connects to your running dev server and gives the AI live visibility into what is happening. Playwright launches a browser and drives it programmatically. If you are debugging a visual bug in your existing app, agnt is the better fit. If you need the AI to navigate through a flow, fill forms, and verify outcomes, Playwright is more appropriate. They can coexist -- different tools for different tasks.

## Database and Data

Giving your AI agent direct database access eliminates the copy-paste cycle of running queries, copying results, and pasting them into chat. The AI can inspect schemas, run queries, and understand your data model firsthand.

| Category | What to search for | Notes |
|----------|-------------------|-------|
| PostgreSQL | "postgres mcp server" on GitHub or npm | Several options exist. Look for ones that support read-only mode as a safety default. |
| MySQL | "mysql mcp server" | Similar landscape to PostgreSQL. Evaluate connection handling and query limits. |
| SQLite | "sqlite mcp server" | Good for local development databases. Lower risk since the data is local. |
| Supabase | Supabase's official MCP server | Supabase-specific operations beyond raw SQL: auth, storage, edge functions. |
| MongoDB | "mongodb mcp server" | Fewer mature options. Check for proper connection pooling. |

**Evaluation tips for database servers:** Always check whether the server supports read-only mode. An AI agent with unrestricted write access to your production database is a serious risk. Look for servers that let you configure connection strings per environment and that limit query result sizes to avoid overwhelming the AI's context window.

## File System and Code

Most AI coding agents -- Claude Code, Cursor, Windsurf -- already have built-in file system access. They can read files, write files, search codebases, and navigate directory structures. You usually do not need a separate MCP server for basic file operations.

Where additional MCP servers become useful:

- **Git operations beyond the basics.** Some servers provide richer git integration: interactive rebasing assistance, conflict resolution, blame analysis, or cross-repository operations.
- **Code search and indexing.** Semantic code search servers can find symbol definitions, call hierarchies, and references faster than grep-based approaches. These are particularly useful in large codebases where file-level search is too slow.
- **Workspace management.** Servers that manage multiple repositories, monorepo navigation, or project scaffolding.

For most web development workflows, the built-in capabilities of your AI agent cover file and code operations well enough. Add a specialized server only when you hit a specific limitation.

## API and HTTP

Web development involves constant interaction with APIs -- your own, third-party services, webhooks. MCP servers in this category let the AI make HTTP requests, inspect responses, and work with API collections.

**General HTTP/fetch servers** let the AI make arbitrary HTTP requests. This is useful for testing endpoints, checking API responses, or verifying webhook payloads. Search for "fetch mcp server" or "http mcp server" to find current options.

**API platform integrations** like those for Postman or Insomnia can give the AI access to your saved collections, environments, and test suites. This is valuable if your team already organizes API workflows in one of these tools.

**Evaluation tips:** Check whether the server supports authentication headers, request bodies, and different content types. A fetch server that only handles GET requests is not useful for real API testing. Also check response size limits -- API responses can be large, and the server should truncate or summarize them rather than dumping megabytes into the AI's context.

## Search and Documentation

AI agents have training data cutoffs. When you need the AI to reference current documentation, recent changelogs, or live web content, search and documentation MCP servers fill the gap.

**Web search servers** let the AI search the web and retrieve results. Useful for finding current documentation, looking up error messages, or checking the latest version of a library. Several are available -- search for "web search mcp server" and evaluate based on result quality and rate limits.

**Documentation-specific servers** provide focused access to documentation for specific frameworks or tools. These tend to be more reliable than general web search for technical content because they index structured documentation rather than arbitrary web pages.

**Evaluation tips:** Web search servers often have rate limits or require API keys for search providers. Check the setup requirements before committing. Documentation servers are only as good as their indexing -- test with a few queries for your specific framework before relying on one.

## DevOps and Infrastructure

For teams that manage their own infrastructure, MCP servers can give the AI direct access to containers, clusters, and cloud resources.

| Category | What to search for | Notes |
|----------|-------------------|-------|
| Docker | "docker mcp server" | Container management: list, start, stop, logs, build. Useful for local dev environments. |
| Kubernetes | "kubernetes mcp server" | Cluster operations. Use with extreme caution -- read-only mode recommended. |
| AWS | "aws mcp server" | The official AWS MCP toolkit covers S3, Lambda, CloudWatch, and other services. |
| GCP | "gcp mcp server" | Google Cloud operations. Check for service-specific servers vs. general-purpose. |
| Azure | "azure mcp server" | Similar landscape to AWS and GCP. |
| Terraform | "terraform mcp server" | Infrastructure-as-code operations. Plan review and drift detection are the safest starting points. |

**A word of caution:** Infrastructure servers carry real risk. An AI agent that can delete Kubernetes pods or modify cloud IAM policies can cause significant damage through a misunderstood instruction. Start with read-only access. Add write operations only for specific, well-understood workflows. Prefer servers that support dry-run or plan modes.

## How to Evaluate MCP Servers

When considering any MCP server, run through this checklist:

1. **Read the tool descriptions.** Before installing, check what tool names and descriptions the server exposes. If the descriptions are vague or misleading, the AI will struggle to use the tools correctly.

2. **Test error handling.** Send an invalid request on purpose. Does the server return a structured error, or does it crash? Good servers return `isError: true` results with useful messages. Bad ones hang, crash, or return empty responses.

3. **Check transport compatibility.** Most local MCP servers use stdio transport. If your AI agent expects stdio and the server only supports HTTP, it will not work. Verify compatibility before investing setup time.

4. **Review the security model.** What permissions does the server need? Does it run with your user credentials? Can it be configured for read-only access? Does it phone home or send telemetry?

5. **Verify maintenance status.** Check the last commit date, open issues, and release cadence. A server that has not been updated in months may have compatibility issues with current MCP clients.

6. **Test with your specific AI agent.** MCP is a standard, but implementations vary. A server that works perfectly with Claude Code might behave differently with Cursor due to how each client handles tool discovery, parameter validation, or response rendering.

## Composing Multiple MCP Servers

The real power of MCP shows when you combine multiple servers. Each server handles its domain, and the AI orchestrates them together based on your request.

A practical example for full-stack web debugging:

- **agnt** for browser state: screenshots, DOM inspection, JavaScript errors, network traffic
- **A database server** for querying application data and inspecting schema
- **A fetch server** for testing API endpoints directly

With these three servers active, you can ask your AI agent something like "the user list is showing stale data after I update a user's name" and the AI can:

1. Take a screenshot to see what the browser is rendering
2. Check the browser's network traffic for the update request and response
3. Query the database to verify the data was actually persisted
4. Inspect the API endpoint directly to confirm the response payload
5. Check for JavaScript errors that might indicate a caching issue

Each step uses a different MCP server. The AI chains the results together to diagnose the problem end-to-end, across the browser, the API, and the database. No single tool provides this full picture on its own.

Keep the number of active servers reasonable. Every server adds startup time, increases the tool list the AI must reason about, and introduces a potential point of failure. Three to five well-chosen servers typically cover most web development workflows.

## See Also

- [What is MCP?](/guides/ecosystem/what-is-mcp) -- Understand the Model Context Protocol from the ground up
- [Getting Started](/getting-started) -- Install and configure agnt
- [agnt with Claude Code](/guides/ai-tools/claude-code) -- Detailed setup guide for Claude Code integration
- [MCP Specification](https://modelcontextprotocol.io) -- Official protocol documentation
- [MCP Server Registry](https://github.com/modelcontextprotocol/servers) -- Community directory of MCP servers
