Boost is a Laravel-focused MCP server designed to enhance your AI-powered local development experience. It streamlines workflows and integrates AI capabilities, making it an essential tool for developers working with Laravel.
claude install laravel/boosthttps://laravel.com/docs/boost
["1. **Install Boost**: First, install Boost on your local development machine following the official documentation. Ensure it's properly configured with your Laravel project.\n\n2. **Define Your Task**: Clearly outline what you need help with. The more specific you are about your Laravel project structure and goals, the better Boost can assist you.\n\n3. **Use the AI Interface**: Access Boost's AI interface through your terminal or IDE. Start with simple commands to get familiar with its capabilities.\n\n4. **Iterate and Refine**: Boost can generate code and suggestions, but you'll need to review and refine them. Use Laravel's testing tools to verify the AI's suggestions work as intended.\n\n5. **Leverage Boost's Integrations**: Boost works best when integrated with your entire Laravel workflow. Use it for code generation, testing, and even deployment tasks.","Tip: Boost works particularly well with Laravel's built-in features like Eloquent ORM and Blade templating. Focus your prompts on how to leverage these features when seeking AI assistance."]
Enhancing local Laravel development workflows by automating repetitive tasks.
Integrating AI features into Laravel applications for smarter functionality.
Streamlining development processes with an MCP server to reduce setup time.
Facilitating rapid prototyping of Laravel applications with AI-generated code.
claude install laravel/boostgit clone https://github.com/laravel/boostCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
I'm working on a Laravel project and need help with [SPECIFIC_TASK]. The current codebase is structured with [DESCRIBE_STRUCTURE]. I need to [EXPLAIN_GOAL]. Can you suggest the best approach using Laravel best practices and Boost's capabilities?
Based on your Laravel project focused on an e-commerce platform with a modular structure, here's how to implement a dynamic product recommendation system using Boost's AI integration:
1. **Data Preparation**: First, ensure your product data is well-structured in your database. Boost can help analyze your schema and suggest optimizations. For example, it might recommend adding a 'product_relationships' table to track which products are frequently viewed together.
2. **AI Model Integration**: Use Boost's AI capabilities to create a simple recommendation model. You can start with a basic collaborative filtering approach. Boost can generate the initial Laravel controller and service classes for this. Here's a sample controller method it might create:
```php
public function getRecommendations(Product $product, $limit = 5)
{
$relatedProducts = Product::whereHas('viewedWith', function($query) use ($product) {
$query->where('product_id', $product->id);
})->limit($limit)->get();
return $relatedProducts;
}
```
3. **Testing**: Boost can help generate test cases for your new recommendation system. It might create tests that verify the system works with different product categories and viewing patterns.
4. **Deployment**: Once tested, Boost can assist in creating deployment scripts specific to your Laravel environment, whether it's a shared hosting setup or a cloud platform.Manage microservices traffic and enhance security with comprehensive observability features.
Orchestrate workloads with multi-cloud support, job scheduling, and integrated service discovery features.
Monitor frontend performance and debug effectively with session replay and analytics.
Design, document, and generate code for APIs with interactive tools for developers.
Manage CI/CD processes efficiently with build configuration as code and multi-language support.
Enhance performance monitoring and root cause analysis with real-time distributed tracing.
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