TL;DR:

• MCP (Model Context Protocol) is the open standard that lets AI assistants connect to real tools: databases, browsers, design files, deployment platforms, and more.
• Unlike plugins, MCP servers are client-agnostic. One server works with Claude Code, Cursor, Windsurf, VS Code, and any other compliant host.
• This guide covers 10 of the most useful MCP servers for developers in 2026, with setup configs for each.

AI coding assistants are brilliant engines idling in neutral. They can write complex logic, explain architectures, and catch bugs. Without MCP servers, however, they cannot actually do anything in the real world. They cannot check your Figma file, scrape a competitor's docs, run a deployment, or open a browser.

Model Context Protocol changes that. It is the open standard that gives AI assistants a real set of hands.

Since Anthropic released MCP in November 2024, the ecosystem has grown to thousands of servers. OpenAI and Google DeepMind adopted it in early 2025, and it was donated to the Linux Foundation's Agentic AI Foundation in December 2025, cementing its status as the universal interface between AI and the tools developers actually use.

This guide covers the 10 most valuable MCP servers for developers right now, regardless of which AI client you use.

MCP (Model Context Protocol) is an open standard introduced by Anthropic that lets AI assistants connect to external tools, data sources, and services through a single, unified interface.

Before MCP, connecting an AI assistant to any external tool meant building a custom integration. GitHub needed its own connector. Postgres needed another. Notion needed another. Every AI client had its own plugin format, and every tool needed a separate implementation for each client.

Anthropic called this the "N x M problem": N tools multiplied by M clients creates an exponentially growing pile of one-off integrations. The result was a fragmented ecosystem where agents were powerful in demos and brittle in practice.

MCP introduces a universal interface between AI models and the tools they need to access. Think of it as USB-C for AI: one standard connector that works everywhere.

The protocol is built on JSON-RPC 2.0 and defines three primitives:

• Tools: actions an AI can invoke (run a search, create a file, deploy code)
• Resources: data sources the AI can read (files, database records, API responses)
• Prompts: reusable templates the server can expose to the client

MCP supports two transport types:

• stdio: runs locally on your machine, managed by the AI client automatically
• SSE/HTTP: runs remotely or locally, communicated over a network endpoint

The protocol defines three roles:

Each client maintains a one-to-one connection with each server. A host can connect to many servers simultaneously.

Connecting MCP servers to your tools grants real access. Treat it accordingly:

• Start read-only. Grant write access only after you have observed how your AI uses the tools in practice.
• Scope credentials tightly. Use dedicated API keys with minimum required permissions. Never reuse production credentials for MCP.
• Keep secrets out of config files. Store API keys as environment variables so they do not end up in version control.
• Prefer official servers. Use implementations from the service provider (GitHub's own MCP server, Sentry's own MCP server) rather than unreviewed community forks.
• Watch for prompt injection. MCP servers that return web content can be vectors for injected instructions. Review what each server returns before giving it write access.

• modelcontextprotocol.io/examples: official reference implementations
• awesome-mcp-servers: community-maintained curated list
• glama.ai/mcp/servers: searchable marketplace with previews
• Docker MCP Catalog: containerized servers with built-in isolation

You might wonder how MCP compares to browser extensions, IDE plugins, or the ChatGPT plugin system that launched in 2023. They are fundamentally different in three ways.

MCP is client-agnostic. A Chrome extension only works in Chrome. A ChatGPT plugin only works in ChatGPT. An MCP server works with any compliant host: Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Cline, all of them. You build or install a server once, and every AI client you use can access it.

MCP servers can take real actions. Browser extensions typically modify the UI or intercept requests. Plugin systems like ChatGPT's were mostly read-only API calls. MCP servers can read and write: creating GitHub issues, deploying to Vercel, running code in a sandbox, writing files. They are closer to microservices than to browser extensions.

MCP uses a proper open protocol. Rather than a proprietary plugin API that changes with each model provider's whims, MCP is an open standard governed by the Agentic AI Foundation. Your servers do not break when a provider updates their product.

MCP is the standard for agent-to-tool communication. If you are also curious about how AI agents communicate with each other, see MCP vs A2A: which agent protocol should you use?.

MCP is supported across all major AI coding clients as of 2026:

All of them use the same JSON config structure. The only difference is where the file lives:

{
  "mcpServers": {
    "server-name": {
      "command": "npx",
      "args": ["-y", "package-name"],
      "env": {
        "API_KEY": "your-key"
      }
    }
  }
}

The Firecrawl MCP server turns any website into clean, LLM-ready data, stripping navigation, ads, and markup so your AI can work with actual content. With over 85,000 GitHub stars, it is one of the most widely deployed tools in production AI workflows.

What it does:

Firecrawl exposes 12 tools through MCP, covering the full spectrum of web data needs:

• firecrawl_scrape: scrape any URL into clean markdown or structured JSON, with options for mobile rendering, tag filtering, and JavaScript waiting
• firecrawl_search: search the web and extract content from results in a single call, with time-based filtering (qdr:d, qdr:w, qdr:m)
• firecrawl_crawl and firecrawl_check_crawl_status: asynchronously crawl entire sites with configurable depth, deduplication, and domain constraints
• firecrawl_map: discover all indexed URLs on a site before deciding what to scrape
• firecrawl_extract: extract structured data from pages using a JSON schema and an LLM, no scraping rules needed
• firecrawl_agent and firecrawl_agent_status: launch an autonomous research agent that independently browses, searches, and compiles structured reports from across the web
• firecrawl_browser_create, firecrawl_browser_execute, firecrawl_browser_delete, firecrawl_browser_list: create and control persistent browser sessions via CDP for interactive, multi-step automation

Why developers use it:

When you are building a feature and need to audit a competitor's API, pull changelog entries from a library's GitHub releases page, or extract pricing data from a site with no public API, Firecrawl handles it without leaving your editor. The firecrawl_agent tool is especially powerful: give it a research prompt and it plans its own browsing strategy, gathering data from multiple sources before returning a structured result.

Example prompts:

• "Use Firecrawl to scrape the changelog from this library's GitHub releases page"
• "Search for the latest benchmarks comparing Redis and Valkey and summarize the results"
• "Use the Firecrawl agent to research the top 5 vector databases and return a structured comparison table"

Full documentation: Firecrawl MCP Server | Also see: How to set up and use Firecrawl MCP in Cursor

Figma's official Dev Mode MCP server exposes the live structure of whatever you have selected in Figma directly to your AI, including hierarchy, auto-layout rules, variants, text styles, spacing tokens, and component references. Your AI generates code against the real design rather than guessing from a screenshot.

What it does:

• Expose selected Figma layers with full structural detail, not just pixel dimensions
• Surface design tokens, color styles, and typography definitions
• Provide component variants and their properties
• Return auto-layout constraints and spacing rules

Why developers use it:

The design-to-code gap is one of the biggest sources of friction in frontend development. Developers either receive static screenshots and have to guess at spacing values, or they spend time in Figma's inspect panel manually extracting properties. With the Figma MCP, your AI can read the actual design spec and generate component code that respects the design system down to the exact border radius and token name.

Example prompts:

• "Implement the selected Figma component as a React component using Tailwind"
• "What are the spacing tokens and color variables used in this design?"
• "Generate a CSS file that matches the typography styles from this Figma frame"

The Brave Search MCP server gives your AI the ability to search the live web using Brave's independent search index. No Google tracking, no ad-skewed results, and no knowledge cutoff.

What it does:

• Perform web and news searches with ranked results
• Access both general web results and local business data
• Return snippets and URLs your AI can then fetch and process

Why developers use it:

Your AI's training data has a cutoff date. New library releases, recent CVEs, current framework best practices, and emerging tools are not in it. A web search MCP bridges that gap. Brave Search is the most privacy-respecting option with an official MCP implementation, and the API has a generous free tier.

Example prompts:

• "Search for the latest security advisories for Express.js and summarize any critical ones"
• "Find current benchmark comparisons between Bun and Node.js for HTTP throughput"

If you need to retrieve full page content rather than just search snippets, Firecrawl MCP is a more efficient alternative for research-heavy tasks: it searches and scrapes in one step, returning clean structured content ready for your AI to use. See our Brave Search API alternatives guide for a full comparison.

The E2B MCP server gives your AI a secure cloud sandbox to actually run code, not just write it. Any AI client connected to E2B can execute Python or JavaScript, run shell commands, install packages, and inspect outputs, all inside an isolated microVM.

What it does:

• Execute Python and JavaScript in isolated cloud sandboxes
• Run shell commands and inspect stdout/stderr
• Install packages and manage dependencies
• Persist sandboxes across multiple tool calls in a session

Why developers use it:

There is a significant difference between an AI that can write a data processing script and one that can run it, check the output, and iterate. E2B closes that gap safely: the sandbox is completely isolated from your machine and production systems. It is particularly valuable for data analysis, migration scripts, and any task where you want to verify logic before committing to it.

Example prompts:

• "Write a script to analyze this CSV file, find duplicates, and generate a summary report, then run it"
• "Test this regex pattern against these 20 edge cases and show me which ones fail"
• "Run the database migration script against the staging snapshot and report any errors"

Composio takes a different approach: instead of installing a separate MCP server for each service, you connect one Composio MCP server that exposes tools for over 250 platforms, including GitHub, Slack, Gmail, Notion, Jira, Salesforce, HubSpot, and hundreds more. Authentication is managed through Composio's dashboard, so you never handle OAuth flows manually.

What it does:

• Expose tools for 250+ apps through a single MCP endpoint
• Manage OAuth, API key storage, and token refresh automatically
• Let you pick which apps and which specific actions to expose
• Work as a remote server, requiring no local process to run

Why developers use it:

If you need your AI to touch multiple services at once, setting up individual MCP servers for each is tedious. Say you want it to check a Linear ticket, update the corresponding Notion doc, and post to Slack. Composio handles all of that from one configured connection. It is particularly useful for automation workflows that span several platforms.

Example prompts:

• "Check my Linear queue and for each ticket tagged urgent, create a GitHub issue and notify the #engineering Slack channel"
• "Search my Gmail for any unread messages about the API outage and summarize them"

The Playwright MCP server from Microsoft gives your AI control over a real browser: navigating pages, clicking elements, filling forms, and verifying UI behavior. Unlike screenshot-based approaches, it uses Playwright's accessibility tree for interactions, making it faster and more reliable.

What it does:

• Navigate to URLs and interact with web pages
• Click, type, select, and interact with any element
• Take full-page or element-specific screenshots
• Execute arbitrary JavaScript in the browser context
• Run multi-step E2E scenarios

Why developers use it:

Playwright MCP bridges the gap between writing UI code and verifying it works. You can ask your AI to "navigate to localhost:3000, log in as the test user, fill out the checkout form, and confirm the success message appears" and it will do exactly that. This eliminates the most frustrating category of bug: the one that only shows up in a real browser but not in unit tests.

Example prompts:

• "Navigate to localhost:3000/checkout and verify the payment form submits successfully with test card 4242 4242 4242 4242"
• "Take a screenshot of the dashboard on mobile viewport and identify any layout issues"
• "Run through the signup flow and report the exact error message shown when I submit an invalid email"

The official Vercel MCP server gives your AI direct access to your Vercel projects: monitoring deployments, managing environment variables, checking build logs, and creating new projects. For Next.js and full-stack teams, this eliminates most reasons to leave the editor during a deployment cycle.

What it does:

• List and inspect current deployments (production and preview)
• Fetch build logs for failed deployments
• Create and update environment variables
• Trigger new deployments
• Manage domain configuration

Why developers use it:

The "it works locally but not on Vercel" loop is one of the most annoying parts of web development. With Vercel MCP, your AI can pull the actual build logs from the failing deployment, identify the error, and suggest a fix without you ever opening the Vercel dashboard. It also makes environment variable management significantly less tedious.

Example prompts:

• "My last deployment to production failed. Fetch the build logs and tell me what went wrong."
• "Add the NEXT_PUBLIC_STRIPE_KEY environment variable to the staging environment with this value"
• "List all preview deployments for the feature/checkout-v2 branch"

The Linear MCP server connects your AI to your issue tracker. For engineering teams that live in Linear, this eliminates the browser tab you keep open just to log bugs, check sprint status, or update ticket assignees.

What it does:

• Read, create, and update issues and sub-issues
• Manage labels, priorities, assignees, and statuses
• Search across projects and teams by keyword or filter
• Check cycle (sprint) status and progress

Why developers use it:

Developers switch to their issue tracker dozens of times a day for small tasks: logging a bug they just found, checking if something is already tracked, updating a ticket status after a PR merges. Doing all of that through a prompt without leaving the editor adds up to a meaningful reduction in context switching over the course of a week.

Example prompts:

• "Create a bug report in the Backend team project: the /api/users endpoint returns 500 when the email contains a plus sign"
• "What tickets are currently assigned to me in this sprint?"
• "Mark ticket ENG-492 as done and add a comment explaining what the fix was"

Context7 solves a fundamental problem with LLMs: their training data goes stale. It fetches current, version-specific documentation for thousands of libraries at query time and injects it directly into your AI's context window.

What it does:

• Fetch live documentation for any library, pinned to a specific version
• Return actual API references rather than training-data approximations
• Works with no API key required

Why developers use it:

Ask an AI "how do I configure middleware in Next.js 15?" without Context7 and you might get an answer based on how middleware worked in Next.js 13. With Context7, it fetches the actual current docs. This is especially valuable when working with libraries that have changed significantly in recent versions: React 19, Next.js 15, Python 3.13, and anything in the fast-moving AI tooling space.

Example prompts:

• "Using Context7, look up how to implement streaming responses in Next.js 15 App Router"
• "What changed between Pydantic v1 and v2 for model validators? Use current docs."

The Sentry MCP server connects your AI directly to your error monitoring pipeline. Instead of copying a stack trace out of the Sentry UI and pasting it into a chat window, your AI can pull the full issue including breadcrumbs, environment context, and related events, and work from the actual data.

What it does:

• Fetch full error context, stack traces, breadcrumbs, and related events
• Correlate errors with recent releases and deployments
• Search issues by tag, environment, time range, or error message
• Inspect performance data and transaction traces

Why developers use it:

The usual debugging loop is slow and lossy: see error in Sentry, copy stack trace, paste into chat, describe additional context, get a guess. With Sentry MCP, your AI gets the same view you do: the complete issue with all context attached. The fix suggestions are correspondingly more accurate.

Example prompts:

• "Pull the latest unresolved Sentry issues in production tagged payment and rank them by frequency"
• "For error FRONTEND-4821, fetch the full context and suggest a fix"
• "Did the error rate spike after our last deploy? Check Sentry for the past 2 hours."

MCP servers have moved from an interesting idea to an essential part of the AI-assisted development workflow. The ten servers above collectively cover the most common tasks where developers waste time switching context: web research, design handoff, deployment monitoring, issue tracking, browser testing, and error debugging.

The best place to start is Firecrawl. It handles the widest range of tasks with no setup beyond an API key, and its autonomous agent tool can tackle research tasks that would otherwise take hours. From there, add the servers that match your actual friction points: Figma MCP if you work closely with designers, Vercel MCP if you ship to Vercel, Sentry MCP if debugging burns more time than it should.

Continue learning:

• Set up the Firecrawl MCP in your editor: How to set up and use Firecrawl MCP in Cursor
• Build a custom MCP server in Python: FastMCP tutorial for AI developers
• See 15 MCP servers specifically for Cursor: 15 best MCP servers for Cursor
• Compare MCP with Google's A2A protocol: MCP vs A2A — which agent protocol should you use?
• Build AI agents with web data access: 11 AI agent projects you can build today