MCP for Laravel
- other
About
What is MCP for Laravel?
MCP for Laravel is a PHP package for Laravel 11 that provides a framework for building intelligent AI agents using the Model Context Protocol (MCP). It is designed for developers who want to integrate LLM-powered agents with tool calling, memory, and RAG capabilities into Laravel applications.
How to use MCP for Laravel?
Install the package via Composer with composer require settled/mcp-laravel. Then create an agent by extending the Settled\MCP\Agent class, configure an LLM provider (e.g., Anthropic, Ollama, OpenAI, Mistral, Deepseek), and define instructions using the SystemPrompt class. Send prompts to the agent with $agent->run(new UserMessage(...)). Connect MCP servers by using the McpConnector component inside the agent’s tools array.
Key features of MCP for Laravel
- Pre-built Agent class with automatic memory and tool management
- MCP server connector to import external tools via McpConnector
- Support for multiple LLM providers (Anthropic, Ollama, OpenAI, Mistral, Deepseek)
- SystemPrompt helper for consistent instruction building
- RAG (Retrieval-Augmented Generation) support with vector stores and embeddings
Use cases of MCP for Laravel
- Creating a YouTube video summarizer agent with transcription tools
- Building an SEO analysis agent that uses MCP server tools
- Implementing a RAG chatbot that queries a vector database
FAQ from MCP for Laravel
What LLM providers does MCP for Laravel support?
It supports Anthropic, Ollama, OpenAI, Mistral, and Deepseek. Switching providers requires only a single line of code.
How do I connect an MCP server to my agent?
Use the McpConnector class inside the agent’s tools() method, providing the command and arguments for the MCP server (e.g., 'npx -y @modelcontextprotocol/server-everything'). The connector automatically exposes the server’s tools.
What are the system requirements for MCP for Laravel?
PHP ^8.0 and Laravel ^11.0 are required.
Does the agent have built-in memory?
Yes, the Agent class automatically retains conversation history, as shown in the sample where the agent remembers the user’s name from a previous message.
Can I build a RAG system with this package?
Yes, by extending the RAG class and attaching an embeddings provider (e.g., VoyageEmbeddingProvider) and a vector store (e.g., PineconeVectoreStore).
Details
- Author
- settledco
- GitHub stars
- 4
- Category
- other
- Repository
- settledco/mcp-for-laravel
MCP for Laravel
> MCP (Model Context Protocol) for Laravel - A powerful AI framework for building intelligent applications. Based on inspector-apm/neuron-ai
Requirements
- PHP: ^8.0
- Laravel: ^11.0
Official documentation
Go to the official documentation
Install
Install the latest version of the package:
composer require settled/mcp-laravel
Create an Agent
MCP for Laravel provides you with the Agent class you can extend to inherit the main features of the framework,
and create fully functional agents. This class automatically manages some advanced mechanisms for you such as memory,
tools and function calls, up to the RAG systems. Let's create the first agent, extending the Settled\MCP\Agent class:
use Settled\MCP\Agent;
use Settled\MCP\SystemPrompt;
use Settled\MCP\Providers\AIProviderInterface;
use Settled\MCP\Providers\Anthropic\Anthropic;
class YouTubeAgent extends Agent
{
public function provider(): AIProviderInterface
{
return new Anthropic(
key: 'ANTHROPIC_API_KEY',
model: 'ANTHROPIC_MODEL',
);
}
public function instructions(): string
{
return new SystemPrompt(
background: ["You are an AI Agent specialized in writing YouTube video summaries."],
steps: [
"Get the url of a YouTube video, or ask the user to provide one.",
"Use the tools you have available to retrieve the transcription of the video.",
"Write the summary.",
],
output: [
"Write a summary in a paragraph without using lists. Use just fluent text.",
"After the summary add a list of three sentences as the three most important take away from the video.",
]
);
}
}
The SystemPrompt class is designed to take your base instructions and build a consistent prompt for the underlying model
reducing the effort for prompt engineering.
Talk to the Agent
Send a prompt to the agent to get a response from the underlying LLM:
$agent = YouTubeAgent::make();
$response = $agent->run(new UserMessage("Hi, I'm Valerio. Who are you?"));
echo $response->getContent();
// I'm a friendly YouTube assistant to help you summarize videos.
$response = $agent->run(
new UserMessage("Do you know my name?")
);
echo $response->getContent();
// Your name is Valerio, as you said in your introduction.
As you can see in the example above, the Agent automatically has memory of the ongoing conversation. Learn more about memory in the documentation.
Supported LLM Providers
With Neuron you can switch between LLM providers with just one line of code, without any impact on your agent implementation.
Supported providers:
- Anthropic
- Ollama (also available as an embeddings provider)
- OpenAI
- Mistral
- Deepseek
Tools & Function Calls
You can add the ability to perform concrete tasks to your Agent with an array of Tool:
use NeuronAI\Agent;
use NeuronAI\SystemPrompt;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\Tools\Tool;
use NeuronAI\Tools\ToolProperty;
class YouTubeAgent extends Agent
{
public function provider(): AIProviderInterface
{
return new Anthropic(
key: 'ANTHROPIC_API_KEY',
model: 'ANTHROPIC_MODEL',
);
}
public function instructions(): string
{
return new SystemPrompt(
background: ["You are an AI Agent specialized in writing YouTube video summaries."],
steps: [
"Get the url of a YouTube video, or ask the user to provide one.",
"Use the tools you have available to retrieve the transcription of the video.",
"Write the summary.",
],
output: [
"Write a summary in a paragraph without using lists. Use just fluent text.",
"After the summary add a list of three sentences as the three most important take away from the video.",
]
);
}
public function tools(): array
{
return [
Tool::make(
'get_transcription',
'Retrieve the transcription of a youtube video.',
)->addProperty(
new ToolProperty(
name: 'video_url',
type: 'string',
description: 'The URL of the YouTube video.',
required: true
)
)->setCallable(function (string $video_url) {
// ... retrieve the video transcription
})
];
}
}
Learn more about Tools on the documentation.
MCP server connector
Instead of implementing tools manually, you can connect tools exposed by an MCP server with the McpConnector component:
use NeuronAI\Agent;
use NeuronAI\MCP\McpConnector;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\Tools\Tool;
use NeuronAI\Tools\ToolProperty;
class SEOAgent extends Agent
{
public function provider(): AIProviderInterface
{
return new Anthropic(
key: 'ANTHROPIC_API_KEY',
model: 'ANTHROPIC_MODEL',
);
}
public function instructions(): string
{
return new SystemPrompt(
background: ["Act as an expert of SEO (Search Engine Optimization)."]
steps: [
"Analyze a text of an article.",
"Provide suggestions on how the content can be improved to get a better rank on Google search."
],
output: ["Structure your analysis in sections. One for each suggestion."]
);
}
public function tools(): array
{
return [
// Connect an MCP server
...McpConnector::make([
'command' => 'npx',
'args' => ['-y', '@modelcontextprotocol/server-everything'],
])->tools(),
// Implement your custom tools
Tool::make(
'get_transcription',
'Retrieve the transcription of a youtube video.',
)->addProperty(
new ToolProperty(
name: 'video_url',
type: 'string',
description: 'The URL of the YouTube video.',
required: true
)
)->setCallable(function (string $video_url) {
// ... retrieve the video transcription
})
];
}
}
Learn more about MCP connector on the documentation.
Implement RAG systems
For RAG use case, you must extend the NeuronAI\RAG\RAG class instead of the default Agent class.
To create a RAG you need to attach some additional components other than the AI provider, such as a vector store,
and an embeddings provider.
Here is an example of a RAG implementation:
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\RAG\Embeddings\EmbeddingsProviderInterface;
use NeuronAI\RAG\Embeddings\VoyageEmbeddingProvider;
use NeuronAI\RAG\RAG;
use NeuronAI\RAG\VectorStore\PineconeVectoreStore;
use NeuronAI\RAG\VectorStore\VectorStoreInterface;
class MyChatBot extends RAG
{
public function provider(): AIProviderInterface
{
return new Anthropic(
key: 'ANTHROPIC_API_KEY',
model: 'ANTHROPIC_MODEL',
);
}
public function embeddings(): EmbeddingsProviderInterface
{
return new VoyageEmbeddingProvider(
key: 'VOYAGE_API_KEY',
model: 'VOYAGE_MODEL'
);
}
public function vectorStore(): VectorStoreInterface
{
return new PineconeVectoreStore(
key: 'PINECONE_API_KEY',
indexUrl: 'PINECONE_INDEX_URL'
);
}
}
Learn more about RAG on the documentation.
Official documentation
Go to the official documentation
Contributing
We encourage you to contribute to the development of Neuron AI Framework!
Please check out the Contribution Guidelines about how to proceed. Join us!
LICENSE
This bundle is licensed under the MIT license.
