Top 10 MCP Servers for Cursor May 2025

The Model Context Protocol (MCP) has grown from a new concept to a mainstream development standard for mcp server cursor integration in just four months. Developers are experiencing a complete transformation in the way they work with their coding environments.

The data tells a compelling story. MCP servers support more than 250+ tools, which include popular apps like Slack, Notion, Gmail, Linear, and Salesforce. On top of that, platforms like Composio provide 20,000+ pre-built API actions that you can integrate without writing code. This goes beyond being just another technical standard – it serves as a user-friendly interface that standardizes how apps provide context to large language models.

MCP servers are a great way to get value for Cursor IDE users because they make integration simple. Developers can now connect AI applications to multiple tools without writing complex integration code, instead of creating custom connections for each data source. Tools like Firecrawl have proven their worth, with over 1.8k stars on GitHub.

The right MCP server can boost your efficiency and cut down on context-switching. You can implement designs straight from Figma, automate browser tasks with Browserbase, or deploy apps through Heroku without leaving your IDE. We’ve put together this list of the top 10 MCP servers to help you improve your Cursor experience in 2025.

Local File and Document Management: Filesystem MCP

_{Image Source: LinkedIn}

The Filesystem MCP server is the foundation for local file interaction in Cursor. It lets you access files and directories through a controlled API. This server works locally, unlike other MCP servers that connect to external services. Developers who need to work with project files directly through Cursor’s interface will find it perfect.

Filesystem MCP Overview

Filesystem MCP creates a secure connection between Cursor and your local filesystem. It gives you a detailed set of file operations without needing direct system access. This vital tool lets you:

• Read and write files – Access or modify file contents in allowed directories
• Create, list, and delete directories – Keep your project structure organized
• Move files between locations – Change your workspace layout without app switching
• Search for files – Find specific files by name or content
• Retrieve file metadata – See size, type, and timestamp details

You can stay focused in Cursor while managing files that would normally need different apps. The server also adds a security layer that limits operations to specific directories. This prevents accidental access to sensitive system files.

Filesystem MCP Installation

Setting up Filesystem MCP takes just a few steps:

1. Node.js Requirement – Check if Node.js is on your system with node --version in your terminal

2. Configuration Method Options:

   • UI Configuration: Go to Cursor Settings > Features > MCP Servers and click “+ Add New MCP Server”
   • Configuration File: Set up the MCP configuration file at:
     • macOS: ~/Library/Application Support/Cursor/mcp-servers.json
     • Windows: %APPDATA%Cursormcp-servers.json
     • Linux: ~/.config/Cursor/mcp-servers.json

3. Installation Command – Run this NPX command:

   npx -y @modelcontextprotocol/server-filesystem /path/to/directory
   

The /path/to/directory parameter tells the server which directories can be accessed. Multiple directories can be listed to give broader but controlled access.

Filesystem MCP Cursor Integration

Your mcp-servers.json file needs the right setup to work with Cursor. Here’s a sample configuration:

{
  "servers": [
    {
      "name": "filesystem",
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/username/Documents",
        "/Users/username/Projects"
      ]
    }
  ]
}

Restart Cursor after making changes. A hammer icon will appear in the bottom-right corner showing available MCP tools.

The integration lets Cursor handle many file operations through natural language commands:

• Creating or modifying files (“Create a new file called config.json with these settings…”)
• Listing directory contents (“Show me what’s in the src/components folder”)
• Reading specific files (“Open the README.md and tell me what it contains”)
• Moving or renaming files (“Move all .png files from assets to images folder”)

Some users have noticed conflicts between Cursor’s built-in edit_file function and Filesystem MCP Server operations in Composer’s agent mode. Cursor might use the Filesystem MCP Server instead of its native features for file writing.

Give access only to project-relevant directories to stay secure while using Filesystem MCP’s powerful file handling features. This makes it an essential tool for efficient development in Cursor.

Cloud Storage Access: Google Drive MCP

_{Image Source: Shrivu’s Substack – Shrivu Shankar}

Google Drive integration with Cursor creates a bridge to cloud storage that lets you access documents without switching apps. The Google Drive MCP provides continuous connection and helps developers cooperate efficiently when working with cloud-stored files.

Google Drive MCP Overview

Google Drive MCP connects directly to Google Drive through the Model Context Protocol. This connection allows Cursor to handle cloud file operations. You can:

• Search files in Google Drive with customizable queries
• Read contents of documents stored in your Drive
• Access and manipulate data in Google Sheets
• Update specific cell values in spreadsheets

The tool becomes more valuable because it automatically converts file formats. The server processes different Google Workspace file types and converts them to formats that work better with AI processing:

These conversions happen automatically so Cursor can work with any content type in Drive.

Google Drive MCP Installation

The setup requires these Google Cloud components:

1. Create a Google Cloud project
2. Enable the Google Drive API
3. Configure an OAuth consent screen (internal works fine for testing)
4. Add the required OAuth scope: https://www.googleapis.com/auth/drive.readonly
5. Create an OAuth Client ID for “Desktop App” application type
6. Download your client’s OAuth keys

You have two options to install:

Docker Setup:

{
  "mcpServers": {
    "gdrive": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm", 
        "-v", "mcp-gdrive:/gdrive-server",
        "-e", "GDRIVE_CREDENTIALS_PATH=/gdrive-server/credentials.json",
        "mcp/gdrive"
      ]
    }
  }
}

NPX Setup:

{
  "mcpServers": {
    "gdrive": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-gdrive"
      ],
      "env": {
        "GDRIVE_CREDENTIALS_PATH": "/path/to/.gdrive-server-credentials.json"
      }
    }
  }
}

Google Drive MCP Cursor Integration

You can integrate Google Drive MCP with Cursor through global or project-specific configuration. Global access needs the server added to ~/.cursor/mcp.json. Project-specific use requires creating or modifying .cursor/mcp.json in your project directory.

Your browser launches an authentication flow during the original setup. After you complete this, your credentials save for future sessions. This removes the need to authenticate again.

The integration lets you interact with Google Drive using natural language in Cursor:

• “Find my product requirements document in Google Drive”
• “Get the latest sales data from our team spreadsheet”
• “Save this code snippet to a new file in my Project Resources folder”

Teams that keep documentation, specifications, or data in Google Drive while developing in Cursor benefit most from this MCP server. It combines your cloud storage and coding environment smoothly and reduces context switching which boosts productivity.

Team Communication: Slack MCP

_{Image Source: Apidog}

Slack MCP changes how developers talk to their teams right from Cursor IDE. This blend of features lets you interact with workspaces without switching contexts. The integration connects coding and team collaboration, making it a must-have tool for developers who work together.

Slack MCP Overview

The Slack MCP server works as a channel management and messaging solution that connects Cursor with Slack workspaces. You can perform user-level interactions through a standard interface. AI tools handle various Slack operations without leaving your coding environment.

Slack MCP lets you:

• List public channels in the workspace
• Post messages to channels and reply to threads
• Add emoji reactions to specific messages
• Get channel message history
• Access detailed user profiles and information

Slack MCP shines when AI assistants become active members of team communications. This opens up possibilities for automated notifications, quick information gathering, and simplified processes right from Cursor.

Slack MCP Installation

You’ll need to create and set up a Slack application first:

1. Create a Slack App

   • Head to the Slack API Applications page and select “Create New App”
   • Pick “From scratch” and choose your workspace
   • Save your Application ID for later

2. Configure OAuth Scopes

   • Under “OAuth & Permissions,” add these bot token scopes:
     • channels:history (view messages in public channels)
     • channels:read (access channel information)
     • chat:write (send messages as the application)
     • reactions:write (add emoji reactions)
     • users:read (view basic user information)

3. Install to Workspace

   • Click “Install to Workspace” and approve the permissions
   • Keep your “Bot User OAuth Token” (starts with xoxb-) safe
   • Save your Team ID (begins with “T” followed by alphanumeric characters)

4. Install the Server

   • Use npm to install globally: npm install -g @modelcontextprotocol/sdk @modelcontextprotocol/server-slack

Slack MCP Cursor Integration

After getting your tokens, you can set up Cursor with this configuration:

{
  "mcpServers": {
    "slack": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-slack"],
      "env": {
        "SLACK_BOT_TOKEN": "xoxb-your-bot-token",
        "SLACK_TEAM_ID": "T01234567"
      }
    }
  }
}

Add this configuration in Cursor’s Settings > Features > MCP by clicking “+ Add New MCP Server”. Restart Cursor to apply the changes.

The integration lets you talk to Slack using natural language commands in Cursor:

• “Post a message in the development channel about the completed feature”
• “Check recent messages in the project channel for feedback on my PR”
• “Send a direct message to the team lead about the roadblock I encountered”

This integration helps you focus on code while keeping your team updated. Your development workflow becomes faster since you don’t need to switch between apps constantly.

Persistent Memory: Memory MCP Server

_{Image Source: Trickle AI}

Memory MCP adds a game-changing feature to Cursor – persistent storage that keeps information between chat sessions. This tool helps Cursor solve one of the biggest challenges AI assistants face: losing context when conversations end.

Memory MCP Overview

The Memory MCP server creates a semantic memory system based on ChromaDB and sentence transformers. This system keeps conversation context alive through multiple sessions. Your interactions with Cursor become more powerful with:

• Semantic search functionality that finds related memories intelligently
• Tag-based retrieval to organize and locate specific information
• Persistent storage backed by ChromaDB that lasts through restarts
• Automatic database backups every 24 hours
• Debug mode to analyze similarity scores

The server excels at three key tasks: managing long-term conversation context, finding information from past chats through semantic search, and keeping structured data safe with automatic backups.

Memory MCP Installation

Here’s how to set up the Memory MCP server:

1. Clone the repository with git clone https://github.com/doobidoo/mcp-memory-service.git
2. Go to the directory with cd mcp-memory-service
3. Create a Python virtual environment: python -m venv venv
4. Activate the environment: source venv/bin/activate (Linux/Mac) or venvScriptsactivate (Windows)
5. Run the installation script: python install.py

The script checks your system architecture and hardware accelerators to install the right dependencies. Docker offers another way to install that works on any platform:

docker-compose up

Windows users should run the Windows-specific script to fix PyTorch installation issues:

python scripts/install_windows.py

Memory MCP Cursor Integration

You’ll need to add an entry to your MCP servers configuration to set up Memory MCP in Cursor. The standard setup looks like this:

{
  "mcpServers": {
    "memory": {
      "command": "uv",
      "args": [
        "--directory", "your_mcp_memory_service_directory",
        "run", "memory"
      ],
      "env": {
        "MCP_MEMORY_CHROMA_PATH": "your_chroma_db_path",
        "MCP_MEMORY_BACKUPS_PATH": "your_backups_path"
      }
    }
  }
}

Natural language commands help Cursor remember information. You might say “Please remember that my project deadline is June 15th” to store a fact, or ask “What do you remember about my project deadline?” to find it.

The server supports advanced features like finding memories by tags, creating backups, and making the database run better. Your AI assistant can now have context-aware conversations across multiple coding sessions thanks to this memory structure.

Web Scraping: Firecrawl MCP Server

_{Image Source: Firecrawl}

Firecrawl MCP gives Cursor users the power to do complex web scraping without leaving their coding environment. This popular server has over 1.8k stars on GitHub. It brings the web right into your development workflow and eliminates the need to switch contexts when you research or gather web data.

Firecrawl MCP Overview

Firecrawl provides detailed web scraping capabilities through a standardized MCP interface. The main features are:

• Web scraping, crawling, and finding content
• Search and content extraction
• Deep research and batch scraping
• Automatic retries and rate limiting
• Cloud and self-hosted support
• Server-Sent Events (SSE) support

Firecrawl’s value comes from knowing how to handle JavaScript rendering. You can extract data from dynamic websites that create content through client-side code. The batch processing features help manage API limits and prevent server overload when collecting data at scale.

Firecrawl MCP Installation

You need to get an API key from Firecrawl’s website before installation. They have a generous free tier to help you get started.

Installation options are:

1. Using NPX (quickest way):

   npx -y firecrawl-mcp
   

2. Adding to your environment variables:

   env FIRECRAWL_API_KEY=your-api-key npx -y firecrawl-mcp
   

Windows users who face problems can try this command:

cmd /c "set FIRECRAWL_API_KEY=your-api-key && npx -y firecrawl-mcp"

Firecrawl MCP Cursor Integration

Your Cursor version determines how to integrate Firecrawl. Cursor v0.48.6 or later needs this MCP servers configuration:

{
  "mcpServers": {
    "firecrawl-mcp": {
      "command": "npx",
      "args": ["-y", "firecrawl-mcp"],
      "env": {
        "FIRECRAWL_API_KEY": "YOUR-API-KEY"
      }
    }
  }
}

Cursor v0.45.6 requires a different setup. Go to Settings > Features > MCP Servers and click “+ Add New MCP Server”. Enter these details:

• Name: “firecrawl-mcp”
• Type: “command”
• Command: env FIRECRAWL_API_KEY=your-api-key npx -y firecrawl-mcp

Cursor’s Composer Agent detects when web scraping is needed and uses Firecrawl tools. You can ask for it directly by describing your web scraping needs through the Composer. Mac users can access it with Command+L.

You can ask your AI assistant to scrape GitHub’s trending repositories page, research competitor products, or gather data for machine learning projects. All this happens right inside your Cursor environment.

Privacy-Focused Search: Brave Search MCP

_{Image Source: Sebastian Petrus – Medium}

Cursor now features Brave Search MCP that lets developers research topics privately without exposing their sensitive queries to tracking systems. This integration connects to Brave’s independent search index directly and delivers relevant results while protecting user privacy.

Brave Search MCP Overview

Brave Search MCP acts as a secure gateway to web and local search capabilities through the Brave Search API. The server provides these essential functions:

• Web Search – Execute general queries with customizable result count and pagination options
• Image Search – Get relevant images (up to 3 per query)
• News Search – Find current articles and trending topics
• Video Search – Locate video content across the web
• Local Business Search – Find nearby services that fall back to web search if no results appear

Brave Search stands out from other options because of its privacy-first approach. The search engine doesn’t track queries or create user profiles, which makes it perfect for developers who work with sensitive information or proprietary code.

Brave Search MCP Installation

Here’s how to set up Brave Search MCP:

1. Get an API Key

   • Visit https://brave.com/search/api/ and create an account
   • Complete email verification and log in
   • Select a subscription plan (free tier gives you 2,000 queries/month)
   • Generate a new API key from the dashboard

2. Install the Server

   • Using NPX (simplest method):

     npx -y @smithery/cli@latest run @smithery-ai/brave-search --config '{"braveApiKey":"YOUR_API_KEY"}'
     

Docker users can use this alternative:

docker run -i --rm -e BRAVE_API_KEY=your_api_key brave-search-mcp

Brave Search MCP Cursor Integration

The Cursor integration needs proper configuration in your MCP settings:

1. Open Cursor Settings > Features > MCP
2. Click “+ Add New MCP Server”
3. Set up the following:
   • Name: brave-search
   • Type: command
   • Command: env BRAVE_API_KEY=your_api_key npx -y @modelcontextprotocol/server-brave-search

You can use Brave Search through natural language prompts in Composer mode (not Chat mode). To cite an instance:

“Search for ‘cursor mcp server’ and list the Top 5 trending keywords”

This setup works best at the time you research technical documentation, find code examples, or learn about solutions to programming challenges—all while staying in your development environment with complete privacy.

Note that Brave Search MCP works in Composer mode only, not Chat mode. This difference helps you get the expected behavior at the time you search the web through Cursor.

Code Collaboration: GitHub MCP Server

_{Image Source: Firecrawl}

GitHub MCP Server is the official collaboration platform from GitHub that lets you work with repositories through Cursor. This tool connects your coding environment with version control and becomes a valuable part of your development workflow.

GitHub MCP Overview

The GitHub MCP server links Cursor to GitHub’s ecosystem through standard API interactions. The server supports four main categories of functions:

• Repository Management – Create repositories, fork existing ones, clone codebases, and manage branches
• Code Operations – Search through code, edit files, commit changes, and review pull requests
• Issue Management – Create issues, add comments, apply labels, and track milestones
• Collaboration Features – Handle project boards, manage discussions, and control access permissions

This integration helps you maintain coding momentum because you won’t need to switch between applications for repository tasks.

GitHub MCP Installation

You need two things before you start:

1. Node.js – Download the version that matches your system (ARM64 for M1/M2 Macs, x64 for Intel Macs)
2. GitHub Personal Access Token – Go to GitHub Settings > Developer Settings > Personal Access Tokens > Fine-grained tokens

You can install it in two ways:

GitHub MCP Cursor Integration

You can modify your mcp.json configuration file through these steps:

1. Open Cursor Settings with ⌘ + Shift + P (macOS) or Ctrl + Shift + P (Windows)
2. Type “Cursor Settings” and select “MCP”
3. Click “Add new global MCP server”
4. Add the JSON configuration

You can also edit ~/.cursor/mcp.json directly:

{
  "mcp_servers": {
    "github": {
      "command": "npx @smithery/github-mcp --token your_github_token"
    }
  }
}

After you set up the configuration, you can work with GitHub repositories through Cursor. Create issues, search code, or manage pull requests without leaving your coding environment.

Browser Automation: Browserbase MCP

_{Image Source: Geeky Gadgets}

Browserbase MCP is a powerful browser automation tool that helps Cursor users bring web interaction capabilities right into their coding environment. This cloud-based solution makes it possible for LLMs to perform complex web operations without leaving the IDE. It changes how developers work with web content.

Browserbase MCP Overview

Browserbase MCP offers complete browser automation through standardized API interactions. The server excels at:

• Browser control and orchestration in the cloud
• Structured data extraction from any webpage
• Screenshot capture of full pages or specific elements
• Console monitoring for analyzing browser logs
• Web interaction (navigation, clicking, filling forms)

Browserbase’s value comes from its dual-server approach. One server handles direct browser control while another manages natural language-driven workflows through Stagehand. This setup lets you perform both low-level browser manipulation and high-level task automation.

Browserbase MCP Installation

Here’s how to install Browserbase MCP:

1. Create an account at browserbase.com to get your API key and project ID
2. Install the server via NPX with the command:

   npx @browserbasehq/mcp
   

You can also use this configuration pattern in your MCP settings:

{
  "mcpServers": {
    "browserbase": {
      "command": "npx",
      "args": ["@browserbasehq/mcp"],
      "env": {
        "BROWSERBASE_API_KEY": "your-api-key",
        "BROWSERBASE_PROJECT_ID": "your-project-id"
      }
    }
  }
}

Browserbase MCP Cursor Integration

After configuration, your AI assistant can execute powerful web automation tasks through natural language commands. These commands include:

You can ask Cursor to “navigate to the React documentation site and extract the key concepts section” or “take a screenshot of the pricing table on competitor.com.”

The system needs proper setup of environment variables in the configuration file and valid Browserbase and OpenAI API keys. Check Cursor’s logs if you have connection issues with the MCP server.

Browserbase MCP helps you gather competitive intelligence, extract data for analysis, and automate repetitive web tasks while you stay focused in Cursor.

Design Integration: Figma MCP Server

_{Image Source: Medium}

Figma MCP connects design and development. Cursor now directly reads Figma designs through a standard protocol. This powerful merge removes design-to-code friction and creates efficient workflows when developers work with designers.

Figma MCP Overview

The Figma Model Context Protocol server lets AI-powered coding assistants read Figma designs automatically. This smart connection brings several benefits:

• Direct design access – Cursor reads component hierarchies and design systems
• Accurate code generation – AI creates code that matches what designers want
• Elimination of manual measurements – You won’t need to copy color codes or spacing values
• Component structure understanding – The proper nesting and relationships stay intact
• Contextual code suggestions – AI suggests implementations based on design patterns

This integration changes how you implement designs completely. Rather than using screenshots, Figma MCP helps Cursor understand designs through standard data format. The result is precise code generation in one go.

Figma MCP Installation

You need these items to set up Figma MCP:

1. Node.js v16+ (v18 recommended) and npm v7+ (or pnpm v8+)
2. Figma Professional account to access API
3. Figma API token with read permissions

Here’s how to get your Figma API token:

1. Log into your Figma desktop app
2. Click your profile icon → Settings → Security
3. Scroll to Personal Access Tokens
4. Generate a new token (save it safely – you’ll see it only once)

Next, install the server using NPX:

npx @figma/mcp-server --figma-api-key=YOUR_TOKEN

You can also set it up with environment variables:

set FIGMA_API_KEY=your_token_value
npx @figma/mcp-server

Port 3333 serves as the default server port.

Figma MCP Cursor Integration

Here’s how to connect Figma MCP to Cursor:

1. Make sure your Figma-MCP server runs (usually on port 3333)
2. Open Cursor Settings → MCP and click “+ Add New MCP Server”
3. Pick a name (like “Figma Designs”) and select the SSE option
4. Type http://localhost:3333 as the URL
5. Check the connection (green dot means success)

Share this snippet with your team:

{
  "mcpServers": {
    "Figma Designs": {
      "command": "npx",
      "args": ["@figma/mcp-server", "--figma-api-key=YOUR_TOKEN_HERE"]
    }
  }
}

Your workflow becomes much simpler after integration. When implementing a design:

1. Ask your designer for the Figma link to a specific component
2. Right-click the design and select “Copy/Paste As → Copy Link to Selection”
3. Open Cursor Composer with Agent Mode enabled
4. Paste the Figma link and ask for code generation (like “Generate a React component using Tailwind CSS”)

This connection helps Cursor understand the design’s visual elements, structure, and styling. You’ll get accurate code implementations in one try.

App Deployment: Heroku MCP Server

_{Image Source: Heroku}

Heroku MCP Server lifts Cursor’s capabilities with direct app deployment. Developers no longer need to switch between coding and platform management tools. The official integration connects AI-powered tools like Cursor to Heroku’s cloud platform. This creates an uninterrupted flow from development to deployment.

Heroku MCP Overview

The Heroku MCP Server focuses on these core developer workflows:

• App Lifecycle Management – Deploy, scale, restart, and monitor applications
• Database Operations – Execute actions on Heroku Postgres databases
• Add-on Management – Find and manage resources for apps
• Scaling and Performance – Smart adjustment of application resources

The server uses Heroku CLI as its main execution engine. It runs in REPL (Read-Eval-Print Loop) mode which makes command execution faster by a lot. This approach helps Heroku MCP deliver quick responses. This becomes particularly useful for multi-step operations that would need new CLI processes otherwise.

Heroku MCP Installation

You need to generate a Heroku authorization token before installation:

heroku authorizations:create

The generated token will serve as your HEROKU_API_KEY in the next steps. This token provides secure access and ensures proper authentication for all operations.

Heroku MCP Cursor Integration

After you get your token, add these lines to your Cursor’s MCP configuration file:

{
  "mcpServers": {
    "heroku": {
      "command": "npx -y @heroku/mcp-server",
      "env": {
        "HEROKU_API_KEY": "your-heroku-auth-token-here"
      }
    }
  }
}

This integration lets you use natural language for deployment commands. To name just one example, you can ask Cursor to “create a new Heroku app called ‘my-svelte-todo-list'” or “add a Postgres database to my application.” Heroku MCP works with other MCP servers to complete your development lifecycle in Cursor. You can code, test, deploy, and monitor – all from one place.

The integration matches Heroku’s goal of meeting developers where they work. Now you can manage both your code and production applications right inside Cursor IDE.

Comparison Table

Conclusion

MCP servers have changed how developers work with their Cursor IDE environment. This piece explores ten powerful servers that boost productivity by a lot and cut down on context-switching. Each server adds something special to Cursor’s capabilities – from local file operations with Filesystem MCP to smooth cloud deployment through Heroku MCP.

The comparison table shows many tools you can pick from. You’ll find privacy-focused web search with Brave Search MCP and design tools through Figma MCP. Picking the right MCP servers for your workflow can change how you tackle development tasks. Most servers have a simple setup process. You just need an API key and some NPX commands, which makes it easy even if you’re just starting out.

Setting up these servers might look tricky at first, but the benefits are nowhere near the setup effort. MCP servers create one interface between your coding environment and external tools. This cuts down the mental load of switching between different apps. We suggest you try two or three servers that line up with what you need for development.

MCP servers show what integrated development environments will look like in the future. They bring external context right into your coding workflow. The technology has grown quickly in just four months. More powerful integrations will show up soon. Try these MCP servers today and see the productivity boost yourself. Your future self will definitely appreciate the time saved and the better coding experience.

FAQs

Q1. What are the key benefits of using MCP servers with Cursor?
MCP servers enhance productivity by reducing context-switching, enabling seamless integration with external tools, and expanding Cursor’s capabilities beyond traditional coding. They allow developers to perform tasks like file management, web scraping, and app deployment without leaving the IDE.

Q2. How do I install and configure an MCP server for Cursor?
Most MCP servers can be installed using NPX commands. You’ll typically need to obtain an API key or token for the specific service. Then, add the server configuration to your Cursor’s MCP settings, either through the UI or by editing the configuration file directly.

Q3. Which MCP server is best for team collaboration?
The Slack MCP server is excellent for team collaboration. It allows you to interact with Slack workspaces directly from Cursor, enabling messaging, channel management, and emoji reactions without switching applications.

Q4. Can MCP servers help with web development tasks?
Yes, several MCP servers are beneficial for web development. The Browserbase MCP enables browser automation and data extraction, while the Figma MCP allows direct access to design files for accurate code generation. The GitHub MCP facilitates code repository management and collaboration.

Q5. Are there any privacy-focused MCP servers available?
Yes, the Brave Search MCP server provides privacy-centered web search capabilities. It allows developers to research topics without exposing sensitive queries to tracking systems, making it ideal for working with proprietary information or sensitive code.