Gemini CLI tells: What Tools Do the Gemini CLI Use for AI Coding? Comparing Gemini, Claude, and Cursor

This content originally appeared on DEV Community and was authored by Javeed Ishaq

It’s a great question that gets to the heart of how different AI coding assistants operate. As Gemini, a large language model from Google, my approach to AI coding is unique. I don’t “use” a tool in the traditional sense; rather, I am the engine, interacting with your local development environment through a specific command-line interface (CLI) tool.

How I Operate

This CLI tool equips me with a set of capabilities—like reading, writing, and running shell commands—that allow me to understand your project and act on your requests. My process is conversational and action-oriented:

1. You give me a task.
2. I analyze your request and use my available tools (e.g., read_file, glob, run_shell_command) to gather context.
3. I process that context to form a plan.
4. I execute the plan by modifying files (write_file, replace) or running commands.
5. I verify the results, often by running tests or linters.

Comparison with Cursor and Claude

Let’s break down how my current implementation compares to other prominent AI coding tools like Cursor and general-purpose models like Claude.

Me (Gemini in this CLI Environment)

• Interaction Model: Conversational command-line agent. You tell me what to do in natural language, and I execute a series of tool-based actions.
• Pros:
  • Direct Filesystem Access: I can read, write, and modify files directly in your project. This is my biggest advantage.
  • End-to-End Task Automation: I can perform complex workflows involving multiple steps, such as: “Refactor this service, then run the associated tests to ensure nothing broke, and finally, stage the changes for commit.”
  • Environment-Aware: I can run shell commands (npm install, git status, tsc), allowing me to interact with your project’s build tools, dependencies, and version control system.
  • Deep Project Context: I can be directed to read any file or search the entire codebase to build a comprehensive understanding, rather than being limited to what’s in an open editor tab.
• Cons:
  • Less Visual and Interactive: The interaction is turn-based. You don’t see my changes in real-time within a GUI, which can feel less direct than an in-editor experience.
  • Higher Friction for Small Edits: For a simple one-line change, my workflow (read, plan, write) can be more verbose than just typing it yourself or using an inline AI suggestion.
  • Dependent on Toolset: My capabilities are strictly defined by the tools I’m given. If a tool doesn’t exist for a specific action, I can’t perform it.

Cursor

• Interaction Model: An “AI-first” code editor (a fork of VS Code) with deeply integrated AI features.
• Pros:
  • Seamless IDE Integration: The AI is part of the editor, not a separate entity. You can generate, edit, and chat about your code without leaving your natural workflow.
  • High Interactivity: Features like inline edits (Ctrl+K) provide immediate, visual feedback. You see the proposed change as a diff and can accept or reject it instantly.
  • “Ambient” Context: The AI is inherently aware of the code you have open, your cursor position, and selected text, making it very intuitive for in-context tasks.
  • Rich GUI Features: It combines the power of a modern IDE (debugger, terminal, extensions) with AI, offering features like “Auto-debug” and “Generate Tests” from a right-click menu.
• Cons:
  • Limited Automation: While excellent at generating and editing code blocks, it’s less designed for autonomous, multi-step tasks across the entire project (e.g., running a sequence of build and test commands). The user is still the primary driver of the high-level workflow.
  • No Direct Command Execution (by the AI): The AI suggests code or commands, but the user typically executes them in the integrated terminal. It doesn’t decide on its own to run npm test.

Claude (or other general-purpose chat models)

• Interaction Model: A web-based chat interface. The user copies and pastes code and context into the chat window.
• Pros:
  • Excellent for Brainstorming and Learning: Fantastic for asking conceptual questions, generating boilerplate code, refactoring isolated snippets, or explaining complex algorithms.
  • Zero Setup: It’s a website. There’s nothing to install or configure in your local environment.
  • Platform Agnostic: It works the same regardless of your operating system, editor, or project setup.
• Cons:
  • No Filesystem Access: This is the critical limitation. It cannot read your files, see your project structure, or verify its own suggestions. The user must manually provide all context and apply all changes.
  • High Potential for Errors: Because it lacks real-world context, it can suggest code that uses incorrect file paths, non-existent dependencies, or APIs that are not available in your project.
  • Tedious for Complex Tasks: Manually copying and pasting large amounts of code and error logs is inefficient and error-prone.

Summary Table

This content originally appeared on DEV Community and was authored by Javeed Ishaq