Extensions & Integrations

Cursor is built on VS Code's foundation, which means most VS Code extensions work seamlessly. This gives you access to a vast ecosystem of productivity tools alongside Cursor's AI features.

Installing extensions:

Recommended extensions that complement Cursor's AI:

Extensions to be cautious with:

• Other AI extensions (GitHub Copilot, Tabnine, Codeium) — may conflict with Cursor's built-in AI. Disable them to avoid duplicate suggestions.
• Heavy language servers — large extensions that consume memory may slow the editor. Monitor performance.
• Extensions that modify the editor heavily — some may interfere with Cursor's inline diff display or AI side pane.

Managing extensions:

# Check installed extensions via terminal
code --list-extensions

# Cursor-specific: settings to disable extensions per workspace

In Settings (Cmd+, / Ctrl+,), search for extensions to find:

• Auto-update settings
• Per-workspace enable/disable
• Extension recommendations for your project

Recommending extensions to your team:

Create a .vscode/extensions.json file in your project:

{
  "recommendations": [
    "dbaeumer.vscode-eslint",
    "esbenp.prettier-vscode",
    "eamodio.gitlens",
    "usernamehw.errorlens",
    "Gruntfuggly.todo-tree"
  ]
}

When team members open the project, Cursor will suggest installing these extensions.

Cursor enhances Git workflows with AI-powered features for commit messages, diff understanding, and history analysis.

AI-generated commit messages:

One of the most practical time-savers. Instead of writing commit messages manually:

What the AI considers when generating commit messages:

• The actual diff (lines added, removed, modified)
• File names and paths changed
• The type of change (new feature, bug fix, refactor, docs)
• Your project's existing commit message style (if consistent)

Understanding diffs with AI:

When reviewing changes (yours or from a PR), use AI to explain complex diffs:

1. Open the Source Control panel
2. Click on a changed file to see the diff
3. Select the confusing part of the diff
4. Press Cmd+L / Ctrl+L and ask: "What does this change do and why might it have been made?"

Git history analysis:

Ask the AI about your project's Git history:

# In Chat, reference a file:
@src/components/UserForm.tsx

When was the validation logic last changed?
What was the reason for the most recent changes to this file?

With GitLens installed, you can see inline blame annotations that show who wrote each line. Select a line with its blame info and ask the AI about the history.

Branching workflow with AI:

Use Chat to plan your branching strategy:

I need to implement feature X. It touches:
- The user model (database changes)
- The API routes (new endpoints)
- The frontend form (new component)

Should I do this in one branch or split it up?
What order should I implement these in?

Conflict resolution with AI:

When you hit merge conflicts:

1. Open the conflicted file
2. Select the conflict markers (<<<<<<<, =======, >>>>>>>) and surrounding code
3. Press Cmd+L / Ctrl+L: "Help me resolve this merge conflict. The incoming change is [describe], and the current change is [describe]. I want to keep both behaviours."
4. The AI generates a merged version that preserves both intents

Cursor's integrated terminal is more than just a command line — it connects to the AI for error interpretation, command suggestions, and workflow automation.

Opening and managing the terminal:

AI-powered error interpretation:

When a command fails in the terminal:

1. Select the error output (including the command that caused it)
2. Press Cmd+L / Ctrl+L to send to Chat
3. Ask: "What went wrong and how do I fix it?"

Common terminal errors the AI excels at explaining:

AI command suggestions:

When you're not sure how to accomplish a terminal task:

Chat: "How do I find all TypeScript files that import from the 'lodash' package?"

AI: You can use grep or find for this:
    grep -rl "from 'lodash'" --include="*.ts" --include="*.tsx" src/

Or with ripgrep (faster):
    rg "from 'lodash'" --type ts src/

Terminal + AI debugging workflow:

1. Run your tests: npm test
2. A test fails with output in the terminal
3. Select the failure output
4. Send to Chat: "This test is failing. Here's the test file: @src/utils/format.test.ts and the implementation: @src/utils/format.ts. What's wrong?"
5. The AI has both the error context and the source code — it can diagnose precisely

Running AI-suggested commands safely:

When the AI suggests terminal commands:

• Read the command before running it
• Be especially careful with: rm, sudo, commands that modify system config
• For unfamiliar commands, ask the AI: "What exactly does this command do, flag by flag?"

Cursor can connect to external tools and services to expand what the AI can access and accomplish.

Documentation integration (@docs):

Cursor can reference external documentation:

1. In Chat, type @docs followed by the documentation source
2. Ask questions that reference the documentation
3. The AI retrieves relevant sections and uses them to answer

Example:

@docs Next.js

How do I implement middleware that redirects unauthenticated users?
What's the correct way to handle cookies in Server Components?

Web search ():

For current information beyond the AI's training data:

 What's the latest stable version of React?
 Is there a known issue with Prisma 5.x and PostgreSQL 16?

API documentation for your project:

If your team has internal API documentation, you can reference it:

• Keep API docs as markdown files in your project (e.g., docs/api.md)
• Reference them with @docs/api.md in Chat
• The AI uses this as context for generating correct API calls

Database tools:

For projects with databases:

• Use terminal commands to query your database (psql, mysql CLI)
• Copy schema definitions into Chat for the AI to reference
• Ask the AI to generate migrations based on your current schema

CI/CD integration:

While Cursor doesn't directly integrate with CI/CD pipelines, you can:

• Copy CI error logs into Chat for analysis
• Ask the AI to generate or fix configuration files (GitHub Actions, GitLab CI)
• Use terminal to run CI-like checks locally before pushing

Linter and formatter integration:

Configure your linter/formatter to run on save:

// .vscode/settings.json (works in Cursor too)
{
  "editor.formatOnSave": true,
  "editor.codeActionsOnSave": {
    "source.fixAll.eslint": "explicit"
  }
}

This means AI-generated code gets automatically formatted and lint-fixed when you save — closing the gap between AI output and your project standards.

Model Context Protocol (MCP) is an open standard that allows AI tools to connect to external data sources and services. Cursor supports MCP, enabling you to give the AI access to tools, databases, and APIs beyond your local file system.

What MCP enables:

• Connect the AI to external databases (read schemas, query data)
• Access project management tools (Jira, Linear, GitHub Issues)
• Read documentation from external sources
• Execute actions in external services (create tickets, update records)
• Access custom internal tools your team has built

How MCP works in Cursor:

1. An MCP server runs (locally or remotely) and exposes "tools" the AI can use
2. You configure Cursor to connect to the MCP server
3. When the AI needs information from an external source, it calls the MCP tool
4. The tool returns data that the AI incorporates into its response

Configuring MCP in Cursor:

MCP servers are configured in your Cursor settings or project configuration:

// .cursor/mcp.json (project-level MCP configuration)
{
  "mcpServers": {
    "database": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-postgres", "postgresql://localhost:5432/mydb"]
    },
    "github": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "${GITHUB_TOKEN}"
      }
    }
  }
}

Common MCP servers:

Example: Database-aware coding

With the Postgres MCP server connected:

Chat: "Create a TypeScript function that fetches all orders for a given user,
including the order items and product details. Use our actual database schema."

AI: [Reads schema via MCP] I can see your schema has:
- orders (id, user_id, created_at, status)
- order_items (id, order_id, product_id, quantity, price)
- products (id, name, description, price, category)

Here's the function using your actual table and column names...

Example: GitHub Issues integration

With the GitHub MCP server:

Chat: "What are the open bugs assigned to me? Summarise the top priority ones."

AI: [Queries GitHub via MCP] You have 4 open bugs:
1. #234 - Login fails on Safari (P1, reported 2 days ago)
2. #241 - Cart total doesn't update (P1, reported today)
3. #228 - Profile image upload timeout (P2)
4. #215 - Dark mode toggle flickers (P3)

Security considerations:

• MCP servers have access to external resources — configure with least privilege
• Use environment variables for tokens and credentials (never hardcode)
• Review which tools each MCP server exposes before enabling
• Be cautious with write operations (creating/deleting resources)

The real power comes from combining extensions, integrations, and AI features into a workflow tailored to your specific needs.

Starter toolchain for web development:

Extensions:
- ESLint + Prettier (code quality on save)
- GitLens (enhanced Git context)
- Error Lens (inline errors for easy AI selection)
- REST Client (test APIs without leaving editor)

Settings:
- Format on save: enabled
- ESLint auto-fix on save: enabled
- Cursor AI model: latest available

.cursor/rules:
- Project-specific conventions

.cursorignore:
- node_modules, dist, .next, coverage

Starter toolchain for backend/API development:

Extensions:
- ESLint + Prettier
- Thunder Client or REST Client (API testing)
- Database client extension (view data without leaving editor)
- Docker extension (if using containers)

MCP Servers:
- Database server (read-only, development database)
- GitHub server (issues and PR context)

Terminal setup:
- Split terminal: server | tests | general

Starter toolchain for data/ML work:

Extensions:
- Jupyter extension (notebook support)
- Python extension (language server)
- Rainbow CSV (data file viewing)

Settings:
- Python formatter: Black or Ruff
- Linter: Ruff

.cursor/rules:
- "Use pandas for data manipulation"
- "Prefer vectorized operations over loops"
- "Always include type hints for function signatures"

Evaluating your toolchain:

Ask yourself periodically:

• Are there manual steps I repeat daily that could be automated?
• Do I often copy information from external tools into Chat? (Consider MCP)
• Are there extension-provided features I never use? (Disable for performance)
• Does AI-generated code require consistent manual fixes? (Add to .cursor/rules or lint config)

The feedback loop:

AI generates code
    → Linter catches style issues (auto-fix on save)
    → Tests catch logic issues (run in terminal)
    → You catch design issues (manual review)
    → Update .cursor/rules to prevent recurrence
    → Better AI output next time

This feedback loop is how your toolchain improves over time. Each cycle teaches the AI (via rules) and catches more issues automatically (via extensions).