Laravel MCP Server

Laravel MCP: A Unified AI‑Ready Backbone for Laravel Applications

The Model Context Protocol (MCP) is the lingua franca that lets AI assistants, such as Claude or other LLMs, talk to external systems in a standardized way. Laravel MCP bridges this gap by turning any Laravel application into an MCP‑compliant server with minimal effort. The result is a single, well‑documented endpoint that can register tools, expose resources, manage prompts, and provide real‑time progress updates—all in the familiar Laravel ecosystem.

At its core, the server solves a practical pain point: how to expose application logic and data to an AI model without writing custom adapters. Developers can simply declare a tool, resource, or prompt in Laravel code, and the MCP server handles serialization, transport negotiation, and lifecycle events. This eliminates boilerplate, reduces integration risk, and guarantees that the AI can reliably invoke application functions or fetch data in a predictable format.

Key capabilities are grouped into five main pillars. Transport flexibility lets the server speak HTTP, WebSocket, or even command‑line stdio, so it can run in a microservice, behind a reverse proxy, or inside a Docker container. Tool registration supports declarative definition of callable functions with typed parameters, automatic validation, and structured JSON responses. Resource management offers URI‑based access to files or database rows, with templating and content‑type handling. Prompt orchestration allows developers to craft reusable prompt templates that inject dynamic arguments and maintain conversation history. Finally, progress tracking gives AI models real‑time feedback on long‑running operations through token counts or percentage updates, improving user experience in conversational interfaces.

In real‑world scenarios this translates to a host of use cases: an e‑commerce site can expose a “create order” tool that the AI uses to place purchases; a content platform can surface a “search articles” resource for knowledge‑base queries; an analytics dashboard can provide prompt templates that format charts and tables. Because the server is built on Laravel, developers benefit from existing authentication, caching, queueing, and event systems, allowing AI workflows to coexist seamlessly with the rest of the application stack.

What sets Laravel MCP apart is its complete documentation and developer ergonomics. Every class is PHPDoc‑rich, the command line tool offers graceful shutdown and signal handling, and optional Redis scaling ensures that WebSocket connections can grow with traffic. With MCP’s standardized interface, once a Laravel application is exposed as an AI server, any LLM—whether hosted locally or in the cloud—can interact with it using a single, well‑defined protocol.