Laravel MCP (Model Context Protocol)

Laravel MCP is a dedicated server implementation of the Model Context Protocol, designed to bridge Laravel applications with AI assistants such as Claude. By exposing application data and functionality through a standardized, language‑agnostic API, the server removes the friction that typically surrounds integrating AI into existing codebases. Developers can now treat their Laravel models, custom data structures, and business logic as first‑class citizens in an AI workflow without rewriting services or building bespoke adapters.

The core value of this package lies in its resource and tool abstractions. Resources allow you to publish Eloquent models or arbitrary data structures via and . Once exposed, an AI assistant can query or retrieve these resources using the MCP protocol’s declarative language, enabling dynamic data access during conversation. Tools represent executable actions—such as the built‑in or a custom —that an assistant can invoke to trigger side effects in the Laravel application. This pattern mirrors the “tool‑use” paradigm popularized by OpenAI’s function calling, giving AI agents a controlled interface to the backend.

Key capabilities include:

• Standardized API: All interactions conform to MCP, ensuring compatibility with any client that implements the protocol.
• Zero‑configuration transport: The current implementation uses STDIO, simplifying local development and testing. HTTP support is slated for future releases.
• Extensible resource providers: Eloquent models can be exposed with minimal boilerplate, while the in‑memory provider supports ad‑hoc data such as documents or configuration blobs.
• Tool interface: Custom tools are created by implementing a simple interface, allowing developers to expose complex business logic or external service calls.

Typical use cases span from conversational ticketing systems that fetch user records and update statuses, to AI‑driven analytics dashboards that query real‑time metrics via MCP resources. In a continuous integration pipeline, an AI assistant could automatically review pull requests, access repository data through resources, and trigger build tools. The server’s design also facilitates sandboxing: only the explicitly exposed resources and tools are visible to the assistant, mitigating accidental data leaks.

By integrating Laravel MCP into an AI workflow, teams gain a transparent, secure, and maintainable bridge between their existing Laravel infrastructure and powerful language models. The protocol‑based approach ensures that as new AI assistants emerge, they can interact with the same set of resources and tools without requiring additional code changes.