Top 10 MCP Servers You Should Use in 2026

The Model Context Protocol (MCP) has become the standard way to connect AI assistants to external tools and data sources. Instead of pasting context into chat windows manually, MCP lets your AI agent directly query databases, read documentation, interact with APIs, and manipulate design files.

With hundreds of MCP servers now available, finding the ones worth installing can be overwhelming. This guide covers the 10 most useful MCP servers across categories, with setup instructions for each.

What Is MCP?

MCP (Model Context Protocol) is an open standard created by Anthropic that provides a universal interface between AI models and external systems. Think of it like USB for AI: one protocol, many devices. An MCP server exposes tools and resources that any MCP-compatible client (Claude Code, Claude Desktop, Cursor, Cline, Windsurf) can use.

How MCP Servers Work

Your AI Client (Claude Code, Cursor, etc.)
    |
    |-- MCP Protocol (JSON-RPC over stdio/SSE) -->
    |
MCP Server (exposes tools + resources)
    |
    |-- Connects to -->
    |
External System (database, API, filesystem, etc.)

Each MCP server defines a set of tools (functions the AI can call) and resources (data the AI can read). The AI decides when and how to use them based on your conversation.

The Top 10 MCP Servers

Rank	Server	Category	What It Does
1	Filesystem	Core	Read, write, search, and manage local files
2	PostgreSQL	Database	Query and explore Postgres databases
3	GitHub	DevOps	Manage repos, issues, PRs, and actions
4	Figma	Design	Extract designs, components, and tokens
5	Puppeteer	Web	Control a browser for testing and scraping
6	Sentry	Monitoring	Query errors, stack traces, and performance data
7	Supabase	Backend	Manage Supabase projects, tables, and auth
8	Notion	Productivity	Read and write Notion pages and databases
9	Slack	Communication	Read channels, send messages, search history
10	Memory	AI	Give your AI persistent memory across sessions

1. Filesystem MCP Server

The filesystem server is the most fundamental MCP server. It gives your AI assistant controlled access to read, write, search, and organize files on your local system.

Why use it: Claude Desktop and other GUI clients do not have native file access. This server bridges that gap safely.

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/you/projects",
        "/Users/you/documents"
      ]
    }
  }
}

Tools provided: read_file, write_file, list_directory, search_files, move_file, get_file_info

Best for: Giving Claude Desktop the same file access that Claude Code has natively.

2. PostgreSQL MCP Server

This server connects your AI assistant directly to a PostgreSQL database. It can read schemas, run queries, and help you debug data issues without leaving your conversation.

Why use it: Instead of switching between your AI chat and a database client, let the AI query the database directly.

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://user:password@localhost:5432/mydb"
      ]
    }
  }
}

Tools provided: query (read-only by default), list_tables, describe_table

Safety note: The default configuration is read-only. Enable write access only if you trust the AI with your data and have backups.

Best for: Data analysis, debugging data issues, writing and testing SQL queries.

3. GitHub MCP Server

The GitHub MCP server lets your AI assistant interact with GitHub repositories, issues, pull requests, workflows, and more through the GitHub API.

Why use it: Manage your entire GitHub workflow from within your AI conversation -- create issues, review PRs, check CI status.

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

Tools provided: create_issue, list_issues, create_pull_request, get_pull_request, search_repositories, get_file_contents, push_files

Best for: Automating GitHub workflows, creating issues from bug reports, reviewing PRs with AI assistance.

4. Figma MCP Server

This server lets your AI read Figma designs and extract component structures, styles, layout properties, and design tokens.

Why use it: Go from design to code in a single conversation. The AI reads the Figma file and generates matching frontend code.

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

Tools provided: get_file, get_node, get_styles, get_components, get_images

Best for: Design-to-code workflows, extracting design tokens, generating pixel-accurate components.

5. Puppeteer MCP Server

The Puppeteer server gives your AI control over a headless Chrome browser. It can navigate pages, take screenshots, click elements, fill forms, and extract content.

Why use it: Automated testing, web scraping, visual verification of your UI, and debugging frontend issues.

{
  "mcpServers": {
    "puppeteer": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-puppeteer"]
    }
  }
}

Tools provided: navigate, screenshot, click, fill, evaluate, select

Best for: End-to-end testing, scraping structured data, visual QA of web applications.

6. Sentry MCP Server

This server connects to your Sentry account to query error reports, stack traces, performance data, and release information.

Why use it: When a bug is reported, the AI can pull the full stack trace from Sentry and start debugging immediately.

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": ["-y", "@sentry/mcp-server"],
      "env": {
        "SENTRY_AUTH_TOKEN": "sntrys_your_token_here",
        "SENTRY_ORG": "your-org-slug"
      }
    }
  }
}

Tools provided: list_issues, get_issue_details, get_event, search_issues, get_performance_data

Best for: Bug triage, error debugging, performance analysis.

7. Supabase MCP Server

This server provides full access to your Supabase projects, including database management, auth configuration, edge functions, and storage.

Why use it: Manage your entire Supabase backend from your AI conversation without switching to the Supabase dashboard.

{
  "mcpServers": {
    "supabase": {
      "command": "npx",
      "args": [
        "-y",
        "supabase-mcp-server",
        "--supabase-url", "https://your-project.supabase.co",
        "--supabase-key", "your-service-role-key"
      ]
    }
  }
}

Tools provided: query, create_table, manage_auth, deploy_function, manage_storage

Best for: Full-stack development with Supabase, database schema management, auth setup.

8. Notion MCP Server

The Notion server lets your AI read and write Notion pages, databases, and blocks. It can search your workspace, create new pages, and update existing content.

Why use it: Turn your Notion workspace into a knowledge base your AI can access directly. Great for pulling specs, writing docs, and updating project trackers.

{
  "mcpServers": {
    "notion": {
      "command": "npx",
      "args": ["-y", "notion-mcp-server"],
      "env": {
        "NOTION_API_KEY": "ntn_your_integration_token"
      }
    }
  }
}

Tools provided: search, get_page, create_page, update_page, query_database, append_blocks

Best for: Documentation workflows, project management, syncing specs between Notion and code.

9. Slack MCP Server

This server connects to your Slack workspace, letting your AI read channel messages, search history, and send messages.

Why use it: Pull context from Slack discussions into your coding session. Find bug reports, feature requests, or deployment notes without leaving your AI conversation.

{
  "mcpServers": {
    "slack": {
      "command": "npx",
      "args": ["-y", "@anthropic/slack-mcp-server"],
      "env": {
        "SLACK_BOT_TOKEN": "xoxb-your-bot-token"
      }
    }
  }
}

Tools provided: list_channels, read_messages, search_messages, post_message, get_thread

Best for: Pulling context from team discussions, automated status updates, searching for bug reports.

10. Memory MCP Server

The Memory server gives your AI persistent memory across sessions. It stores key-value pairs, user preferences, and conversation context that persists between restarts.

Why use it: Normally, AI assistants forget everything between sessions. This server lets your AI remember your coding conventions, project context, and personal preferences.

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"]
    }
  }
}

Tools provided: store_memory, retrieve_memory, search_memories, list_memories, delete_memory

Best for: Long-running projects where you want the AI to remember decisions, conventions, and context.

How to Install MCP Servers

All the examples above use Claude Desktop's configuration format. The config file location depends on your platform:

Platform	Config File Path
macOS	`~/Library/Application Support/Claude/claude_desktop_config.json`
Windows	`%APPDATA%\Claude\claude_desktop_config.json`
Linux	`~/.config/Claude/claude_desktop_config.json`

For Claude Code, add MCP servers via the CLI:

# Add a server to your project
claude mcp add postgres -- npx -y @modelcontextprotocol/server-postgres postgresql://localhost:5432/mydb

# List configured servers
claude mcp list

# Remove a server
claude mcp remove postgres

For Cursor, add servers in Settings > MCP Servers, or edit .cursor/mcp.json in your project root.

Frequently Asked Questions

Are MCP servers secure? MCP servers run locally on your machine. Data does not pass through third-party servers. However, the AI can use any tools the server exposes, so review the available tools before connecting sensitive systems.

Can I build my own MCP server? Yes. The MCP SDK is available in TypeScript and Python. A basic server takes about 50 lines of code to implement.

Do MCP servers work with all AI clients? MCP servers work with any MCP-compatible client: Claude Code, Claude Desktop, Cursor, Cline, Windsurf, Continue, and others. Not all clients support all transport types (stdio vs. SSE).

How many MCP servers can I run at once? There is no hard limit. Each server runs as a separate process. Most developers run 3-5 servers simultaneously without performance issues.

Wrapping Up

MCP servers transform AI assistants from isolated chatbots into connected tools that understand your entire workflow. Start with the filesystem and GitHub servers, then add database and design servers as needed. The ecosystem is growing fast, so check the MCP server registry regularly for new additions.

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