Essential MCP Plugin Recommendations: Sequential Thinking, Brave Search, Playwright Installation & Usage Guide

Last Friday night, I stared at my Cursor interface for half an hour. Not because I couldn’t write code, but because I suddenly realized—why does my colleague finish requirement analysis in 10 minutes with Cursor, while I spend half an hour manually searching docs, copy-pasting, and organizing information?

The answer came at Monday’s morning meeting. When he shared his screen, I saw a small hammer icon in the bottom-right corner of his Cursor. Clicking it revealed a list of MCP plugins: Sequential Thinking, Brave Search, Playwright… My expression was probably like seeing someone playing a game with cheats enabled.

Honestly, I’ve been using Cursor for about six months, but never seriously explored MCP plugins. That day, I realized that Cursor itself is just an editor—the real “superpowers” are hidden in these MCP plugins. With the right plugins installed, AI isn’t just a “chatbot” anymore; it becomes a “doer” that can search information, operate browsers, and analyze code for you.

Railway - Modern deployment platform, zero-config, go live in minutes

Start Now →Ad

In this article, I’ll share 5 MCP plugins I now use daily. Each plugin comes with complete configuration code (just copy and paste) and real usage scenarios. If you want to make Cursor 10x better, spending 10 minutes reading this is absolutely worth it.

What is MCP? Why Do You Need MCP Plugins?

Let’s start with what MCP is. MCP stands for Model Context Protocol, an open standard launched by Anthropic in November 2024. In simple terms, it’s like giving AI an “interface” to connect with various external tools and data sources.

Think of it this way: AI without MCP plugins is like someone who can only talk but can’t do anything hands-on. When you ask it “help me search for the latest React documentation,” it can only tell you “you can search on Google.” But with the Brave Search MCP plugin installed, it can directly search for you and organize the results.

Some people compare MCP to “giving AI hands and eyes,” and I think that’s quite accurate. With Playwright plugin installed, AI gets “hands” to operate browsers; with Sequential Thinking plugin installed, AI gets a “thinking framework” to break down complex problems into steps.

So why do you need MCP plugins? Simply put, to transform AI from “just chatting” to “actually working.” Now when I write code with Cursor, I basically:

• Let AI search for the latest technical documentation (Brave Search)
• Let AI analyze complex system architecture (Sequential Thinking)
• Let AI automate webpage testing (Playwright)
• Let AI read project files and configs (Filesystem)

I used to do all these things manually. Now? In the time it takes to grab a coffee, AI has it done.

MCP Plugin Installation Basics

Before recommending specific plugins, let’s talk about installation and configuration basics. Don’t worry, it’s not complicated—mainly just editing one JSON config file.

Where is the Config File?

Different tools have config files in different locations:

• Cursor: .cursor/mcp.json in project root (project-level), or ~/.cursor/mcp.json in user directory (global-level)
• Claude Desktop:
  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

I usually use global config so all projects can use it. If a specific project needs special config, then I’ll create a project-level config file separately.

What Does the Config File Look Like?

A typical mcp.json config file structure looks like this:

{
  "mcpServers": {
    "plugin-name": {
      "command": "npx",
      "args": ["-y", "@xxx/package-name"],
      "env": {
        "API_KEY": "${YOUR_API_KEY}"
      }
    }
  }
}

Key field explanations:

• command: Launch command, usually npx (Node.js) or python
• args: Arguments passed to the command, -y means auto-confirm installation
• env: Environment variables for passing API Keys and other sensitive info

How to Manage Environment Variables?

Never write API Keys directly in the config file! The correct approach is:

1. Set in system environment variables (Windows uses system settings, macOS/Linux uses .bashrc or .zshrc)
2. Reference with ${variable_name} in config file

For example:

# macOS/Linux
export BRAVE_API_KEY="your-api-key"

# Windows PowerShell
$env:BRAVE_API_KEY="your-api-key"

In the config file, write:

"env": {
  "BRAVE_API_KEY": "${BRAVE_API_KEY}"
}

Notes for Windows Users

There are several pitfalls when configuring MCP on Windows, which I’ve all encountered:

1. Path issues: Windows backslashes \ need escaping to \\, or just use forward slashes /
2. npx not found: Ensure Node.js is installed and added to system PATH
3. Permission issues: Sometimes need to run Cursor with administrator privileges

Check if Node.js is installed:

node --version
npm --version

If not installed, go to nodejs.org to download and install.

Curated MCP Plugin Recommendations

Alright, basics covered. Now for the main event—5 MCP plugins I use every day.

1. Sequential Thinking - Teaching AI to “Think Slowly”

Why Recommend This?

Have you ever encountered this: you ask AI a complex question, it gives you a direct answer, but you feel it “didn’t think deep enough”? Sequential Thinking solves this problem.

With this plugin installed, AI breaks down complex problems into step-by-step reasoning processes. Like someone working through calculations on scratch paper, you can see its “thinking process.” Moreover, it revises its own ideas during reasoning and even tries different reasoning paths.

I use it most for: system architecture design, technical solution analysis, complex bug troubleshooting. Once I had it analyze a microservices architecture performance bottleneck—it broke the problem into 8 steps, revised its reasoning direction twice midway, and the final solution was indeed more comprehensive than what I thought of myself.

Installation & Configuration

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

Usage Tips

• Suitable for: Complex problems requiring multi-step reasoning
• Not suitable for: Simple fact queries (gets too verbose)
• Prompt tip: Add “analyze with systematic thinking” for better results

2. Brave Search - Privacy-Friendly Search Engine

Why Recommend This?

This plugin lets AI search the web directly. You might ask: why not use Google Search? Two reasons.

First, Brave Search focuses more on privacy and doesn’t track your search history. Second, it offers a free API with 2,000 queries per month, which is plenty for personal use.

I use it most for: checking latest technical docs, learning about a library’s latest version, searching for error message solutions. Once when using a new framework, it threw a weird error. I had AI use Brave Search to look it up, and it found the solution in a GitHub issue—took less than 1 minute total.

Alibaba Cloud - Top choice for developers, exclusive 15% off for Lighthouse servers

Get 15% Discount →Ad

Installation & Configuration

Step 1: Get API Key

1. Go to Brave Search API to register
2. Select the free “AI Data” plan under “Subscriptions”
3. Create a new key under “API Keys”

Step 2: Configure Environment Variable

# macOS/Linux
export BRAVE_API_KEY="your-api-key"

# Windows PowerShell
$env:BRAVE_API_KEY="your-api-key"

Step 3: Config File

{
  "mcpServers": {
    "brave-search": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-brave-search"
      ],
      "env": {
        "BRAVE_API_KEY": "${BRAVE_API_KEY}"
      }
    }
  }
}

Usage Tips

• Restart Cursor or Claude Desktop for config to take effect
• Seeing the hammer icon in bottom-right means config is successful
• Add “search for” or “check latest info” in prompts, AI will auto-invoke search

3. Playwright - Swiss Army Knife of Browser Automation

Why Recommend This?

Playwright is Microsoft’s open-source browser automation tool. With this MCP plugin installed, AI can operate browsers: open webpages, click buttons, fill forms, take screenshots, export PDFs… basically anything you can do in a browser, AI can do.

The best part is, it doesn’t operate webpages by “looking at screenshots”—it directly reads webpage structure (accessibility tree), with super high precision. I’ve had it auto-fill complex multi-step forms, and 99% of the time it succeeds on the first try.

I now use it most for:

• Automated testing: having AI test webpage functionality for me
• Data scraping: extracting dynamically loaded webpage data
• Screenshot monitoring: scheduled screenshots to check if pages are normal

Once I needed to test a multi-step registration flow (fill form → verify email → set password → complete), manual testing took 5 minutes per run. After automating with Playwright, it took 10 seconds and could repeat 100 times without getting tired.

Installation & Configuration

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": [
        "-y",
        "@executeautomation/playwright-mcp-server"
      ]
    }
  }
}

Real-World Example

Let me show you a real example. Last week I needed to test a search function, so I asked AI:

“Use Playwright to open example.com, enter ‘React’ in the search box, click the search button, then screenshot it for me.”

AI automatically executed these steps and returned a screenshot after 20 seconds. If done manually, I’d have to open browser, enter URL, search, screenshot, upload… at least 2 minutes.

Usage Tips

• Suitable for: Repetitive browser operations, UI testing, data scraping
• Not suitable for: Scenarios requiring login CAPTCHAs (can’t handle those)
• Prompt tip: Describe operation steps clearly, like “first… then… finally…“

4. GitHub - Code Repository Analysis Assistant

Why Recommend This?

If you frequently use GitHub, this plugin lets AI directly read your repository info: code, Issues, Pull Requests, Commits… everything is accessible.

I use it most for:

• Analyzing code structure: having AI help me organize project directory structure and dependencies
• Managing Issues: batch viewing unsolved bugs, sorted by priority
• Code Review: having AI analyze PR changes and suggest improvements

Once I took over an old project with over 30,000 lines of code and missing documentation. I had AI analyze the codebase with GitHub MCP, and it generated:

• Project architecture diagram
• Core module descriptions
• Tech stack list
• Potential issues checklist

Manual organization would’ve taken at least two days. AI did it in under 10 minutes.

Installation & Configuration

Step 1: Create GitHub Personal Access Token

Vultr - High-performance NVMe VPS with 32 global locations, one-click Docker deploy

View Pricing →Ad

1. Go to GitHub Settings → Developer settings → Personal access tokens
2. Select “Tokens (classic)”, create new token
3. Check repo (repository access) and read:user (read user info) permissions

Step 2: Configure Environment Variable

# macOS/Linux
export GITHUB_TOKEN="your-token"

# Windows PowerShell
$env:GITHUB_TOKEN="your-token"

Step 3: Config File

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

Usage Tips

• Don’t give too many token permissions, just enough (security first)
• Can have AI analyze specific file’s modification history
• Prompt example: “Analyze this repository’s code quality and suggest improvements”

5. Filesystem - Local File Manager

Why Recommend This?

The Filesystem plugin lets AI read and operate local files. Sounds basic, but super useful.

I use it most for:

• Batch file operations: renaming, moving, deleting
• Log analysis: reading log files, extracting error info
• Config management: modifying project configs, environment variables

For example, last month I needed to unify the naming style of 50+ components in a project (from snake_case to camelCase). Doing it manually would require opening each file, find, replace, save… probably half a day.

I had AI batch process with the Filesystem plugin—done in 3 minutes. It automatically traversed all files, identified what needed changing, replaced uniformly, and even generated a modification report for me.

Installation & Configuration

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/path/to/your/project"
      ]
    }
  }
}

Security Notes

• The path in args determines which files AI can access
• Recommend only giving project directory permissions, not entire disk
• Don’t include sensitive files in config (like .env, key files)

Usage Tips

• Suitable for: Batch file operations, log analysis, config management
• Not suitable for: Sensitive data processing (security risk)
• Prompt tip: “Read all .js files in src directory, find unused variables”

Advanced Tips & Best Practices

After installing these plugins, I hit some snags and figured out some experiences. Sharing with you.

Using Multiple Plugins Together

Individual plugins are already powerful, but combining them is even better.

Combo 1: Sequential Thinking + Brave Search

Suitable scenario: Systematically researching a new technology.

Last week I needed to research “edge computing,” so I asked AI:

“Use systematic thinking to help me research edge computing, search for latest tech trends and use cases.”

AI first used Sequential Thinking to break the research task into 6 steps (definition → technical principles → use cases → mainstream solutions → pros/cons → future trends), then used Brave Search to search for relevant info for each step. After 20 minutes, it gave me a 3,000-word research report, at least 10x faster than if I searched and organized myself.

Combo 2: Playwright + GitHub

Railway - Modern deployment platform, zero-config, go live in minutes

Start Now →Ad

Suitable scenario: Automated testing and submitting results.

Now after testing webpage functionality, I have AI use Playwright to screenshot, then use GitHub MCP to auto-create an Issue with the screenshot and test results attached. The whole process is automated, saving a lot of time.

Performance Optimization Suggestions

More plugins isn’t always better. Initially I got overly excited and installed 10+ plugins, then discovered:

1. Slower startup: Cursor has to load all plugins each startup, time went from 3 seconds to 15 seconds
2. Token consumption explosion: AI has to “think” about whether to call these 10 plugins for each response, wasting lots of tokens
3. Slower response speed: Sometimes I just want to ask a simple question, but it has to “consider” whether to call plugins

My strategy later became:

• Keep frequently-used plugins: Sequential Thinking, Brave Search—high-frequency use, always on
• Enable on demand: Playwright, GitHub—specific scenario use, enable when needed
• Disable unused ones: Add "disabled": true in config file, temporarily disable without deleting config

{
  "mcpServers": {
    "playwright": {
      "disabled": true,
      "command": "npx",
      "args": ["-y", "@executeautomation/playwright-mcp-server"]
    }
  }
}

Security Best Practices

A few security considerations when using MCP plugins—I’ve hit these pitfalls, so you don’t have to:

1. Never hardcode API Keys

   • ❌ Wrong: "env": {"API_KEY": "sk-abc123"}
   • ✅ Correct: "env": {"API_KEY": "${API_KEY}"}

2. Rotate keys regularly

   • GitHub Token, Brave API Key—recommend changing every 3 months
   • In case config file accidentally gets committed to a public repo, keys would be exposed

3. Principle of least privilege

   • Only give GitHub Token the permissions it needs, don’t give admin for convenience
   • Filesystem only gets project directory permissions, not entire disk

4. Add config file to .gitignore

   • If using project-level config, remember to add .cursor/mcp.json to .gitignore
   • Prevent sensitive info from being committed to version control

Troubleshooting Guide

Don’t panic when encountering issues, troubleshoot in this order:

Issue 1: Configured plugin but no hammer icon in bottom-right

Solutions:

1. Check if JSON format is correct (validate with JSONLint)
2. Restart Cursor or Claude Desktop
3. Check developer tools console (Cursor → Help → Toggle Developer Tools)

Issue 2: Plugin call fails with “API Key invalid” error

Solutions:

1. Confirm environment variable is set correctly (check with echo $API_KEY)
2. Check if reference format in config file is correct (${VAR_NAME})
3. Restart terminal/command line to make environment variable effective

Issue 3: npx command not found

Solutions:

1. Check if Node.js is installed: node --version
2. Check if npm is in PATH: which npm (macOS/Linux) or where npm (Windows)
3. Reinstall Node.js and check “add to PATH”

Conclusion

After all that, the core message is one sentence: MCP plugins can transform AI from “chat assistant” to “work partner.”

These 5 plugins are what I use daily:

• Sequential Thinking: Deep thinking on complex problems
• Brave Search: Real-time access to latest info
• Playwright: Browser automation
• GitHub: Code repository analysis
• Filesystem: Local file management

My workflow now is basically: when encountering repetitive tasks, first think if I can automate with MCP plugins. Eight out of ten times it works, and using the saved time for coffee—isn’t that great?

If you haven’t tried MCP plugins yet, suggest starting with Sequential Thinking and Brave Search—these two don’t need API Keys and have the simplest config. Try it after installing, I guarantee you’ll come back to thank me.

Got any other MCP plugin recommendations? Leave a comment and let’s chat—I’d like to discover some new stuff too.

13 min read · Published on: Jan 17, 2026 · Modified on: Feb 5, 2026

Easton

Technology