Mcp Rails Template

Overview

The Mcp Rails Template demonstrates how a Ruby on Rails application can expose an ActionMCP server that serves as a bridge between AI assistants and the underlying codebase. By integrating the ActionMCP gem, the template turns a conventional Rails project into an interactive AI‑enabled environment where models can query resources, invoke tools, and receive contextual prompts—all through the Model Context Protocol. This eliminates the need for custom API wrappers or manual data pipelines, allowing developers to focus on business logic while still enabling sophisticated AI interactions.

At its core, the server mounts an ActionMCP engine on a configurable route (). Once active, the server exposes three primary categories of MCP components: prompts, resource templates, and tools. Prompts generate dynamic textual content based on user input; for example, the creates a short narrative using a hero name and genre. Resource templates provide structured data from the application itself—such as the Gemfile dependencies exposed via a URI. Tools perform executable actions: retrieving dependency lists, fetching live weather data from Open‑Meteo, analyzing Ruby code with RuboCop, or indexing project classes and methods. Each tool returns JSON resources that can be consumed directly by an AI assistant, streamlining workflows like code review automation or real‑time documentation generation.

Developers benefit from the template in several practical scenarios. When building an AI‑powered code assistant, the allows the model to surface up‑to‑date gem versions and potential security issues without manual inspection. The can be invoked to enforce style guidelines on snippets generated by the model, ensuring consistency across a codebase. For non‑technical users, the weather tool demonstrates how external APIs can be wrapped as MCP tools, letting a model answer location‑based queries without exposing raw endpoints. Moreover, the provides a lightweight static analysis layer that can be queried for class hierarchies or method signatures, useful in documentation generation or refactoring support.

Integration into AI workflows is straightforward: an assistant sends a structured request to the endpoint, specifying the desired prompt or tool. The server executes the requested component and returns a JSON payload that the assistant can parse and present to the user. The MCP Inspector (a lightweight CLI utility) lets developers test these interactions locally, ensuring that prompts produce coherent output and tools return valid data. Because the server is built on Rails, it inherits robust authentication, routing, and database support, making it suitable for production deployments where AI assistants need to interact with complex back‑ends.

Unique advantages of this template include its tight coupling between Rails’ domain logic and the MCP interface, allowing developers to expose any model or service as a tool with minimal boilerplate. The resource template system provides an elegant, URI‑based way to surface internal data structures—something rarely seen in standard MCP examples. Finally, the inclusion of both narrative prompts and code‑analysis tools showcases how a single server can support diverse AI use cases, from creative content generation to rigorous code quality enforcement.