Working with Cursor

You already know how Cursor integrates AI capabilities natively into the software development experience. Now, let's explore the depths of Cursor's functionality, starting with predictive coding via Cursor Tab and moving to the autonomous capabilities of the Agent. We'll also cover essential architectural features, including context management, MCP integrations, and lifecycle hooks, empowering you to fully customize the development process.

Core features

One of the core features of Cursor is code completion using Tab. This is a specialized model for code completion. You type a comment or partial code, and Cursor predicts the next edits or blocks of code. You can then press Tab to accept these suggestions. Of course, you may not prefer all the recommendations it makes. You can reject suggestions by pressing Esc. The more you use this feature, the better Cursor becomes at predicting your next move.

Besides just code completion, Tab allows you to:

• Modify multiple lines across files.

• Get suggestions based on changes, errors, and previously accepted snippets.

• Add imports.

Next, you can use the Inline Edit feature to edit code or ask questions directly in your editor. Just select the code and press ⌘/Ctrl K. It will open an input field for your selected code where you can give instructions to edit the code, ask relevant questions, or send the selected code to chat:

If you don't know the syntax of a command you need, use ⌘/Ctrl K in the terminal to generate commands from natural language:

With ⌘/Ctrl L, you can select and send code snippets in the Chat. This is useful for larger, multi-file edits and other advanced features that require multiple turns.

Every AI-generated change appears in color-coded lines in a review interface. You can:

• Inspect each diff before accepting.

• Stage selected chunks.

• Roll back changes via Git if needed.

You can also use Cursor to find issues in version control. Simply open the source control tab and click Agent Review.

Cursor CLI

Cursor is an IDE, but it provides the cursor-agent command line tool for terminal-based development. The terminal might be a bit bland when compared to an IDE, but it lets you work across multiple development environments without having to switch between them constantly. This means you can use Cursor's AI coding features from the terminal of another IDE as well.

You can install the Cursor CLI using the following command in your terminal (use WSL on Windows):

curl https://cursor.com/install -fsS | bash

To use it, you can simply use this command:

cursor-agent 

In the following demo, we're using Cursor's capabilities to generate code from PyCharm's terminal:

If you've ever used Anthropic's Claude Code, the Cursor agent CLI tool provides a similar experience.

Cursor Agent

Agent is a coding assistant that can plan entire projects, complete complex coding tasks independently, run terminal commands, and perform complex refactors. You can access it from the Agent pane (⌘/Ctrl I) where you can issue commands. There are various modes for interacting with the agent:

• Ask

• Plan

• Agent

• Debug

You can start by finding answers to common questions that come up when first developing an application or joining an existing project. For this, you can use the Ask mode. This is a read-only mode for asking questions relevant to the project. The agent will search your existing codebase and provide answers without making any changes. This is most helpful when you only want to inquire and learn more about the project you're working on.

Next, you can use the Plan mode to create detailed implementation plans before writing any code. The agent will carefully analyze the request, ask clarifying questions, analyze your existing codebase, and generate a reviewable plan that you can edit before building. It will also ask you clarifying questions on the scope of the project and the necessary features. Then, it will create a detailed plan in a virtual markdown file:

You can review the plan and then build the project by pressing ⌘/Ctrl Enter or clicking on Build. The project will be built using the Agent mode. In this mode, the agent can create, read, and write files, make edits, run commands, fix errors, and perform complex multi-step workflows. It can execute commands in the integrated terminal, such as npm test, docker-compose up, and other CLI tools depending on your stack and workflow.

During build, or once everything is built, you can review the changes by pressing Review in the chat panel. You can either keep the changes or undo them based on your decision. You can also use the Restore Checkpoint option to roll back to a previous request. Then, you can repeat the loop: Ask mode to understand how certain parts of the code work, create plans for new additions and refactors, and implement changes using the Agent mode.

For debugging, you can use the Debug mode:

The agent also automates web app testing by directly interacting with the browser and fixing errors it encounters on the fly:

You'll realize that conversations with the agent can quickly get long. Using tabs (⌘/Ctrl T) helps maintain separate context, history, and model settings for each task. As the conversation continues for multiple turns, the quality of responses and the overall effectiveness of the agent will degrade. This happens because you will exceed the model's context window limit. To prevent this, Cursor automatically summarizes and manages context to keep your chats efficient. You can also manually trigger summarization using the /summarize command.

Context management

Cursor builds an index of your codebase so the underlying LLMs can "understand" your files, functions, modules, and tests. When you ask deep questions about the project, Cursor uses relevant chunks for the best results. You need to regularly update the index if you add new folders or perform major refactors. To avoid this, you can enable Cursor Settings -> Indexing & Docs -> Index New Folders to index new files automatically.

In chat, you can add context using @ mentions. Here are some sources of context you can mention:

• Files and folders.

• Documentation — the most common and popular docs are already available, and you can also add documentation manually via @ -> Docs -> Add new doc.

• Terminals — the agent will be able to look at the terminal context, gaining visibility into command outputs.

• Browser — as seen earlier, you can mention the browser for the agent to use. To enable it, go to Cursor Settings -> Tools & MCP -> Tools -> Browser -> Browser Automation. Depending on this selection, Cursor will be able to interact with either its built-in browser or the external browser.

• Source control — you can also mention branches and commits.

• Past chats and images.

However, you don't want Cursor to look at and index every single file in your project. This includes large binary directories like node_modules, build, and dist, which don't need AI analysis. Also, you don't want the agent to access files containing sensitive data. To exclude these files and folders from indexing or AI analysis, you can create a .cursorignore file and place the files and folders there. This ensures better performance, prevents unnecessary token usage, and avoids exposing private data.

MCP resources

There are two ways to add MCP servers in Cursor: one-click install for featured MCP servers or manual JSON configuration for custom ones. For featured servers, simply choose one of the available servers from the official MCP Directory and click Add to Cursor. For example, if you try to add a server like Notion, the Cursor settings will open directly, prompting you to install it:

After installing the MCP Server, you can connect to the server, authenticate with a valid account, and review its access:

Once the setup is complete, you can see the available resources, tools, and prompts:

For custom servers, you need to provide a JSON configuration under Settings > MCP & Integrations > New MCP server:

{
  "mcpServers": {
    "DockerMCP": {
      "command": "docker", 
      "args": [
        "mcp",
        "gateway",
        "run"
      ]
    }
}

Hooks

As seen earlier, the Cursor Agent can perform various actions autonomously. However, you might want to gain visibility into and control of this agent loop. Hooks are pre-defined scripts or commands that can be set to run before or after certain stages of the agent loop.

They allow you to:

• Enforce rules, such as blocking risky operations (like reading a protected folder).

• Observe what the agent is doing, thus logging and debugging its behavior.

• Scan and sanitize inputs such as masking PII.

• Run tools such as formatters, linters, and build tools after edits.

To set up hooks, create a hooks.json file in your home directory ~/.cursor/hooks.json or at the project scope .cursor/hooks.json:

{
  "version": 1,
  "hooks": {
    "afterFileEdit": [
      {
        "command": "./hooks/lint-and-test.sh"
      }
    ]
  }
}

Then, create your hook script. Because hooks operate by spawning a process and communicating via stdio, they are language agnostic. This means you can use virtually any language, like Bash for simple tasks, PowerShell in Windows environments, and Python for more complex logic. Simply create the file and modify the hook command accordingly (like python3 ./hooks/sanitize_prompt.py for Python scripts). Here, let's create a Bash script at ~/.cursor/hooks/lint-and-test.sh:

#!/usr/bin/env bash

echo "[Cursor Hook] Running lint + tests..."

# Linting using ruff
ruff check .

# Testing using pytest
pytest

echo "[Cursor Hook] Lint and tests passed."

Use chmod +x to make the hook script executable:

chmod +x ~/.cursor/hooks/lint-and-test.sh

Finally, you need to restart Cursor. Now, every time the agent makes edits, the hook will run to lint and test your code with Ruff and Pytest. However, ensure that these commands are available in your shell environment.

Here are additional hooks and when you'd use them:

Conclusion

We've explored Cursor's AI-native ecosystem in depth, moving beyond simple code completion to full-scale project work. We examined how features like Cursor Tab and Inline Edit speed up daily coding tasks, while the Agent modes—Ask, Plan, Debug, and Agent—transform the IDE into a coding partner capable of handling complex tasks. We also explored the Cursor CLI, which extends these AI capabilities beyond the Cursor IDE.

Furthermore, we covered efficient context management and expanded Cursor's functionality through MCP tools. Finally, we implemented hooks to maintain strict control over and enhance the agent loop. You are now fully equipped to use Cursor as an intelligent partner in your software development process.