Turning Your Codebase into a Single Prompt for AI Models with Code2Prompt

Have you ever found yourself manually copying and pasting code files into an AI chat to get help with analysis or generation? It’s time-consuming, right? What if there was a way to automate that process, pulling your entire codebase into a structured prompt that’s ready for large language models? That’s where Code2Prompt comes in. In this article, we’ll walk through what Code2Prompt is, how it works, and step-by-step ways to get it set up and running. I’ll address common questions along the way, like how to install it or customize prompts, based on real usage scenarios.

Let’s start with the basics. Code2Prompt is a tool that converts your codebase into a single, well-formatted prompt for AI models. It handles everything from scanning directories to filtering files, and it integrates features like git diffs and token tracking. Whether you’re a developer working on code analysis or building AI agents, this can save you a lot of manual effort.

What Exactly Is Code2Prompt and Why Might You Need It?

Imagine you’re working on a project and need an AI to review your code for bugs or suggest improvements. Instead of selecting files one by one, Code2Prompt traverses your directories, builds a tree structure, and gathers file information. It turns that into a prompt that’s easy for models like LLMs to process.

From what I’ve seen in practice, this tool shines in scenarios like code analysis, generation, or even integrating with AI agents. It has several components: a core library for integration, a CLI for quick prompt generation, an SDK for Python scripts, and an MCP server for local services.

How does it help specifically? It automates code processing, applies smart filtering with glob patterns and .gitignore respect, uses flexible templating with Handlebars, tracks tokens to fit context limits, and pulls in git details like logs and diffs. No more tedious formatting – it organizes files with line numbers and options for better readability.

If you’re wondering, “Is this just another code viewer?” No, it’s built for AI interaction. It processes codebases of any size into readable prompts, making it practical for developers who rely on LLMs.

Breaking Down the Components of Code2Prompt

Code2Prompt isn’t a one-trick pony; it has layers that cater to different needs. Let’s look at each part.

The Core Library

At its heart, Code2Prompt’s core is a library you can integrate into other applications. It handles directory traversal, tree building, and file info gathering. This makes it versatile for custom tools where you need to ingest code programmatically.

The Command Line Interface (CLI)

The CLI is what most users start with. It generates prompts directly from your codebase, copies them to your clipboard, and optionally saves to a file. You can customize with Handlebars templates – there are examples in the documentation for different use cases.

The Software Development Kit (SDK)

For Python users, the SDK provides bindings to the core library. Install it via pip, and it’s ideal for AI agents or automation scripts that need seamless codebase interaction.

The Model Context Protocol (MCP) Server

This runs as a local service, allowing LLMs to access structured codebase context automatically. It’s like giving your AI a tool to fetch prompts on demand.

These components work together to make code ingestion straightforward. For instance, if you’re building an AI assistant, the MCP server can provide context without manual intervention.

Key Features That Make Code2Prompt Stand Out

What sets Code2Prompt apart? Here are the main features, explained simply:

• Automatic Code Processing: It scans your codebase and formats it into a prompt, handling large projects without hassle.
• Smart Filtering: Use glob patterns to include or exclude files, and it honors .gitignore to avoid unnecessary files.
• Flexible Templating: Handlebars lets you tailor prompts for specific tasks, like analysis or generation.
• Token Tracking: Monitors usage to ensure prompts fit within LLM limits, preventing cutoffs.
• Git Integration: Includes diffs, logs, and branch comparisons to add context like recent changes.
• Developer-Friendly Options: Automatic clipboard copy, line numbers for code snippets, and file organization tools.

In practice, these features mean you can focus on using AI for insights rather than preparing inputs.

How to Install Code2Prompt: Step-by-Step Guides

Installation varies by component, but it’s straightforward. Let’s cover the main ways.

Quick Install for the CLI

For the command-line tool, you have a couple of options.

1. Using Cargo (if you have Rust installed):

   cargo install code2prompt
   

2. Using Homebrew (on macOS):

   brew install code2prompt
   

That’s it – you’re ready to generate prompts.

Installing the SDK with Pip

If you need Python bindings:

pip install code2prompt-rs

This pulls in the core functionality for scripts.

Alternative Installation Methods

For binary releases, download the latest for your OS from the GitHub releases page.

Building from source requires Git, Rust, and Cargo:

git clone https://github.com/mufeedvh/code2prompt.git
cd code2prompt/
cargo install --path crates/code2prompt

These methods ensure you get a working setup without extras.

Setting Up the MCP Server for AI Agents

The MCP server is great for integrations with tools like Claude Desktop or Cursor. Here’s how to set it up.

First, prerequisites: Install Rye for dependency management on Linux or macOS:

curl -sSf https://rye.astral.sh/get | bash

Follow prompts to add Rye to your PATH.

Then:

1. Clone the repository:

   git clone https://github.com/odancona/code2prompt-mcp.git
   cd code2prompt-mcp
   

2. Install dependencies:

   rye build
   

   This creates a virtual environment and installs packages from pyproject.toml.

3. Run the server. Options include:

   • Activate the venv and run:

     source .venv/bin/activate
     python code2prompt_mcp.main
     

   • Or directly with Rye:

     rye run python code2prompt_mcp.main
     

   Run this in the cloned directory.

For global access, create a config file for your LLM app, like this JSON:

{
  "mcpServers": {
    "code2prompt": {
      "command": "bash",
      "args": [
        "-c",
        "cd /path/to/code2prompt-mcp && rye run python /path/to/code2prompt-mcp/src/code2prompt_mcp/main.py"
      ],
      "env": {}
    }
  }
}

Replace paths as needed.

To verify:

1. Restart your LLM app.
2. Test with a command like: “Please get context from /path/to/project for AI analysis using Code2Prompt.”

Using Code2Prompt in Real Scenarios

Once installed, how do you actually use it? Let’s go through examples.

Generating a Prompt from Your Codebase

With the CLI, navigate to your project directory and run code2prompt. It scans, generates the prompt, and copies it to clipboard. Save with an output flag if needed.

For customization, use templates – check the docs for Handlebars examples.

Integrating with AI for Codebase Analysis

In an AI chat: “Can you analyze the code in my project at /path/to/project? Please use Code2Prompt MCP to get the context.”

This pulls structured info via the server.

Filtering Specific Files

Want only Python files, excluding markdown and tests folder? Command: “Please get all python files and remove markdown files and the folder tests, use Code2Prompt MCP for context.”

It applies filters on the fly.

Common Questions and Answers (FAQ)

I’ve pulled together some frequent questions based on user experiences.

What is Code2Prompt and how does it work?

Code2Prompt is a tool that turns your codebase into a prompt for AI models. It works by scanning directories, filtering files, and formatting content with options like git integration and token tracking.

How do I install Code2Prompt on my machine?

For quick CLI install, use cargo install code2prompt or brew install code2prompt. For SDK, pip install code2prompt-rs. Details above for source or MCP.

Can I customize the prompts generated by Code2Prompt?

Yes, with Handlebars templates. The docs have examples for various use cases like code analysis.

Does Code2Prompt respect .gitignore files?

Absolutely – it uses .gitignore rules for filtering, plus glob patterns for includes/excludes.

How does the MCP server integrate with AI tools?

It runs locally and provides context on demand. Configure it in your LLM app’s settings, then reference it in prompts like “use Code2Prompt for context from /path/to/project.”

What if I need to include git history in the prompt?

Code2Prompt supports git diffs, logs, and branch comparisons. Enable these in your command or template.

Is there a way to track token usage?

Yes, it monitors tokens to keep prompts within LLM limits.

How do I build Code2Prompt from source?

Clone the repo, cd in, and cargo install --path crates/code2prompt. Needs Git, Rust, Cargo.

Can Code2Prompt handle large codebases?

It processes any size, with filtering to manage scope.

What’s the license for Code2Prompt?

It’s under the MIT License – check the repo for details.

How can I contribute to Code2Prompt?

Suggest features, report bugs, fix issues via pull requests, help with docs, or spread the word.

Step-by-Step How-To: Setting Up and Using Code2Prompt for Your First Prompt

Here’s a detailed how-to guide, marked up for clarity.

How to Generate Your First Prompt with the CLI

1. Install via Cargo or Homebrew as above.
2. Open terminal, navigate to your codebase: cd /path/to/your/project.
3. Run code2prompt. It generates and copies the prompt.
4. Paste into your AI model for analysis.

How to Set Up the MCP Server for Ongoing Use

1. Install Rye.
2. Clone and build as in the installation section.
3. Run the server.
4. In your AI tool, add the config JSON.
5. Use in prompts: “Analyze /path/to/project using Code2Prompt context.”

How to Customize Filtering and Templating

1. For filtering: Add glob patterns, e.g., include *.py, exclude *.md.
2. For templates: Create a Handlebars file, reference it in the command.
3. Test with a small project to see the output.

Project Stats and Community

Code2Prompt has badges for crates.io version, license, PRs welcome, lines of code, and Discord. Join the Discord for updates, suggestions, help, or bug reports.

The community is active – it’s a place to engage on features.

Wrapping Up: Making Code2Prompt Part of Your Workflow

By now, you should have a clear picture of how Code2Prompt simplifies turning code into AI prompts. Whether through CLI for quick tasks or MCP for integrated setups, it reduces manual work. If you’re dealing with codebases regularly, give it a try – start with the quick install and build from there.

Remember, the documentation has more details on templates and advanced usage. If something doesn’t click, the Discord channel is there for real help.

This tool evolved from needs like yours, so contributing back keeps it improving. What’s your first use case going to be?