What Is an MCP Server? (And the Best MCP Servers in 2026)

If you use Claude, ChatGPT, Cursor, or Windsurf, you’ve probably seen MCP mentioned and wondered what it actually is. The short version: MCP servers are what let AI models do things beyond generating text — browse files, query databases, search the web, write to Notion, run code. This guide covers the concept clearly and, more usefully, tells you which MCP servers are actually worth installing.

Quick answer

How MCP works — the three-minute version

Before MCP, connecting an AI to an external service meant writing a custom integration for every pairing: Claude + Notion required one codebase, Claude + Salesforce required another, and so on. Every AI app duplicated this work differently, making integrations brittle and hard to maintain.

MCP standardises this into a client-server model with three types of capability an MCP server can expose:

• Tools — functions the AI can call: query a SQL database, send a Slack message, search the web, run a terminal command.
• Resources — data the AI can read: a file, a document, a database row, a code repository.
• Prompts — pre-defined templates or workflows the AI can trigger, useful for standardising repeated operations.

When you ask Claude Desktop or Cursor to “check my GitHub issues” or “query the users table in Supabase,” the AI client sends a structured JSON-RPC request to the appropriate MCP server, the server executes the logic against the backend, and returns a structured result. The AI model never touches your database credentials directly — the MCP server handles authentication in a single, controlled place.

The practical result: a tool built once as an MCP server works in Claude Desktop, Cursor, Windsurf, Zed, and any other MCP-compatible client. No per-app rewriting.

The best MCP servers in 2026

The official Anthropic registry and the open-source community have produced over 9,500 community-built MCP servers as of mid-2026 (per mcpservers.org, which indexes the broader ecosystem). Most aren’t worth installing. These are the ones that are.

Developer tools

GitHub MCP Server (@modelcontextprotocol/server-github) — The most-used MCP server in the ecosystem. Gives your AI client read and write access to GitHub: list issues, read pull requests, create branches, search code. Essential for any developer workflow. Official Anthropic server, actively maintained. The modelcontextprotocol/servers monorepo (which houses all official Anthropic MCP servers) has 88k+ GitHub stars.

Playwright MCP Server — Browser automation and testing. Ask your AI to navigate to a URL, fill a form, take a screenshot, or scrape a page. Built by Microsoft. The most capable browser-control MCP available; Puppeteer MCP is the lighter alternative if you don’t need full browser context. 34.6k+ GitHub stars.

Filesystem MCP Server (@modelcontextprotocol/server-filesystem) — Local file access. Read, write, list, and search files within directories you specify. The obvious security consideration: only point it at directories you’d be comfortable with your AI model reading and editing. Official Anthropic server.

Context7 MCP Server — Solves one of the most persistent AI coding problems: models trained on data with a cutoff date don’t know the current API signatures for libraries that have shipped major updates. Context7 pulls live, version-pinned documentation for thousands of libraries and injects it into the model’s context. Practically eliminates hallucinated function calls for supported libraries.

Databases and data

Supabase MCP Server — Full access to your Supabase project: query tables, run SQL, read and write rows, manage storage, call Edge Functions. The most practical database MCP for teams already on Supabase or PostgreSQL. Official Supabase-maintained server.

PostgreSQL MCP Server (@modelcontextprotocol/server-postgres) — Direct PostgreSQL access for teams not using Supabase. Read-only by default; configure write access explicitly. Official Anthropic server.

SQLite MCP Server (@modelcontextprotocol/server-sqlite) — Lightweight local database access. Good for prototyping, local development, or any workflow where you want AI assistance over a SQLite file without spinning up a hosted database.

Productivity and collaboration

Notion MCP Server — Read and write to Notion databases and pages. Works well for AI workflows that need to log outputs, pull structured data from a workspace, or update a project board from a conversation. Requires a Notion integration token.

Slack MCP Server (@modelcontextprotocol/server-slack) — Read Slack channels, post messages, search conversation history. Useful for summarising what happened in a channel, or for AI agents that need to report back to a team. Official Anthropic server.

Figma MCP Server — Read Figma files: extract component structures, pull design tokens, read comments and annotations. The primary use case is AI coding assistants that can read the design file and generate frontend code that matches it.

Linear MCP Server — Issue tracking access: list issues, read project status, create and update tickets. Works alongside the Jira MCP for teams that prefer Linear’s interface.

Search and web

Brave Search MCP Server (@modelcontextprotocol/server-brave-search) — Real-time web search inside your AI client. Requires a Brave Search API key (free tier available). The most reliable search MCP for giving Claude Desktop or Cursor access to current information without leaving the IDE. Official Anthropic server.

Fetch MCP Server (@modelcontextprotocol/server-fetch) — Fetch and read any public URL. Simpler than Playwright for cases where you just need to read a webpage’s content without running JavaScript. Official Anthropic server.

Reasoning and memory

Sequential Thinking MCP Server — Structures the AI’s reasoning process into explicit, reviewable steps. Useful for complex multi-step problems where you want the model to slow down and think through dependencies before executing. Not a data connector — a reasoning scaffold.

Memory MCP Server (@modelcontextprotocol/server-memory) — Gives the AI a persistent key-value memory store across conversations. Useful for long-running projects where you want the model to remember decisions, preferences, or context from previous sessions. Official Anthropic server.

How to install an MCP server

The installation method depends on which AI client you use. The MCP server itself is typically an npm package or a Python script; the client config file tells your AI client where to find it and how to start it.

Claude Desktop

Edit the Claude Desktop configuration file:

• Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json — or, if installed via MSIX, WinGet, or the Microsoft Store: %LOCALAPPDATA%\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude\claude_desktop_config.json

Add the server to the mcpServers object. Example for the GitHub MCP server:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your_token_here"
      }
    }
  }
}

Restart Claude Desktop after saving. The server will appear in the tools list.

Cursor

The config format mirrors Claude Desktop’s mcpServers structure. Two scopes are available. Global (recommended): ~/.cursor/mcp.json on Mac/Linux, %USERPROFILE%\.cursor\mcp.json on Windows — accessible via Settings → Features → Add New MCP Server, which opens the file directly in the editor. Project-level: .cursor/mcp.json at the project root isolates tools to a single workspace. The Settings GUI reads and writes the same JSON files; there is no separate format. Note: current Cursor builds on Windows 11 have a known bug where project-level config is occasionally ignored — if tools don’t load locally, fall back to global config.

Windsurf

Go to Settings → MCP Servers in the Windsurf sidebar. Add servers via the GUI or by editing the underlying JSON config. Windsurf surfaces active MCP servers in the Cascade panel during conversations.

Claude Code (CLI)

Run claude mcp add <server-name> in your terminal. Claude Code manages an .mcp.json file in your project or global config automatically. Run claude mcp list to see active servers.

How to build your own MCP server

The MCP SDK is available for Python (mcp via pip) and TypeScript/Node (@modelcontextprotocol/sdk via npm). A minimal Python server that exposes one tool looks like this:

from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp import types

server = Server("my-server")

@server.list_tools()
async def list_tools():
    return [
        types.Tool(
            name="get_weather",
            description="Get current weather for a city",
            inputSchema={
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"]
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "get_weather":
        city = arguments["city"]
        # Your actual logic here
        return [types.TextContent(type="text", text=f"Weather for {city}: sunny, 22°C")]

async def main():
    async with stdio_server() as streams:
        await server.run(*streams, server.create_initialization_options())

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

Point your AI client at this script as the command in the config, and the tool appears automatically. The MCP documentation covers authentication, streaming responses, and resource handling in full.