# LLMDocumentation

# Mantine with LLMs

Mantine provides LLM-friendly documentation to help AI tools like **Cursor**, **Windsurf**, **GitHub Copilot**, **ChatGPT**, and **Claude** understand and work with the Mantine UI library.

`llms.txt` documentation is updated with every Mantine release.

## Documentation

Links:

* [llms.txt](/llms.txt) – compact
* [Download](/llms-full.txt) full documentation in single document (~1.8MB)

The LLM documentation includes:

* **Getting Started** - Installation and setup guides
* **Components** - All Mantine components with props, examples, and usage
* **Hooks** - Complete hooks documentation with examples
* **Theming** - Theme customization and MantineProvider setup
* **Styles** - CSS modules, CSS variables, and styling approaches
* **Frequently Asked Questions** - Common questions and solutions

## Cursor

In Cursor, you can reference the documentation using the `@Docs` feature:

1. Type `@Docs` in your prompt
2. Reference the Mantine documentation URL: `https://mantine.dev/llms.txt`
3. Ask questions about Mantine components, styling, or implementation

## Windsurf

For Windsurf users:

1. Reference the documentation using `@https://mantine.dev/llms.txt`
2. Or add it to your `.windsurfrules` file for persistent access

## ChatGPT and Claude

When using ChatGPT or Claude:

1. Mention that you're using Mantine v8
2. Reference the documentation URL: `https://mantine.dev/llms.txt`
3. The AI will fetch and use the documentation to provide accurate answers

### GitHub Copilot

While Copilot doesn't directly support external documentation, you can:

1. Include relevant documentation snippets in your comments
2. Reference component names and props accurately for better suggestions

## Skills

Mantine also provides skills for AI coding agents in the
[`mantinedev/skills`](https://github.com/mantinedev/skills) repository.

Currently available skills:

* `mantine-combobox` – Build custom select/autocomplete/multiselect components with `Combobox`
* `mantine-form` – Build forms with `@mantine/form`, validation, nested fields, and form context
* `mantine-custom-components` – Create custom components with Mantine factory APIs and Styles API

### Install skills

Install each skill from the repository:

```bash
npx skills add https://github.com/mantinedev/skills --skill mantine-combobox
npx skills add https://github.com/mantinedev/skills --skill mantine-form
npx skills add https://github.com/mantinedev/skills --skill mantine-custom-components
```

### Use skills

In your AI prompt, explicitly tell the agent to use one of the installed skills.

Examples:

* "Use `$mantine-form` and build a profile form with validation and nested fields"
* "Use `$mantine-combobox` and create a searchable multi-select with custom option rendering"
* "Use `$mantine-custom-components` and scaffold a polymorphic component with Styles API support"

If your agent does not support `$skill-name` mentions, reference the skill name in plain text and ask the agent to follow it.

## MCP server (experimental)

Mantine also provides an MCP server package:

* `@mantine/mcp-server`

The server reads Mantine static MCP data published on `mantine.dev` and exposes tools that AI agents can call directly:

* `list_items`
* `get_item_doc`
* `get_item_props`
* `search_docs`

### MCP server configuration

Most MCP-compatible tools support adding servers with a JSON configuration. Use this server definition:

```json
{
  "mcpServers": {
    "mantine": {
      "command": "npx",
      "args": ["-y", "@mantine/mcp-server"]
    }
  }
}
```

To use a different data source (for example, alpha docs or local static files), add env variables:

```json
{
  "mcpServers": {
    "mantine": {
      "command": "npx",
      "args": ["-y", "@mantine/mcp-server"],
      "env": {
        "MANTINE_MCP_DATA_URL": "https://mantine.dev/mcp"
      }
    }
  }
}
```

### Using MCP server with different tools

#### Claude Desktop

1. Open MCP settings in Claude Desktop
2. Add the `mantine` server configuration above
3. Start a new chat and ask for Mantine guidance, for example: "Find Button props and give me a usage example"

#### Cursor

1. Open Cursor MCP/server settings
2. Add the same `mantine` server config
3. Use agent mode and ask Mantine-specific questions – Cursor will call MCP tools automatically

#### Windsurf

1. Open Windsurf MCP/server settings
2. Register `@mantine/mcp-server` with the same config
3. Ask for component docs, props, and examples directly in chat

#### Other MCP clients (VS Code/Cline and others)

If the client supports custom MCP servers, add the same command and args:

* command: `npx`
* args: `["-y", "@mantine/mcp-server"]`

Then use prompts like:

* "List Mantine items related to input fields"
* "Get full docs for Button"
* "Search Mantine docs for color scheme and dark mode"

## Example prompts

Here are some example prompts you can use with AI tools:

* "Using Mantine v8, how do I create a dark mode toggle?"
* "Show me how to use the AppShell component with a collapsible navbar"
* "How can I customize the theme colors in MantineProvider?"
* "Create a form with validation using Mantine's form hooks"
* "How to align input with a button in a flex container?"

## Documentation Generation

The LLM documentation is automatically generated from our source files using a compilation script. It includes:

* Component documentation from MDX files
* Props tables and types
* Code examples and demos
* Styles API documentation
* FAQ content from help.mantine.dev

There are two generated formats:

* `llms.txt` – the default compact index that links to per-page `.md` files under the `/llms` path
* `llms-full.txt` – a single large file with all documentation content

To ensure you have the latest documentation, we regenerate these files with each release. The files follow the [LLMs.txt](https://llmstxt.org/) standard for better compatibility with AI tools.

## Contributing

If you find any issues with the LLM documentation or have suggestions for improvement, please [open an issue](https://github.com/mantinedev/mantine/issues) on our GitHub repository.