Railagent

Railagent - MCP Server for AI Agent Workflows

A Model Context Protocol (MCP) server that provides structured workflows and tools for AI agents working with software development projects, with specialized focus on Rails applications.

Railagent provides AI agents with a comprehensive workflow system for software development. It guides agents through the entire development lifecycle from requirements gathering to implementation, ensuring consistent and high-quality outcomes.
- Functional Requirements: Use the functional requirements tool to collect necessary information about the project (purpose, users, capabilities, workflows, context, edge cases)
- Product Requirements Document (PRD): Generate a complete PRD from the functional requirements, breaking down the project into features and user stories
- Feature Requirements Document (FRD): For each feature, create a detailed FRD that breaks large features into individual PR-sized units
- Task Design Document (TDD): Transform the FRD into an implementation plan with specific subtasks, where each subtask corresponds to a commit

- Skip directly to step 4 (TDD) above when you have a clear understanding of what needs to be built
- Initialize Tasks: Call the initialize tool to set up subtasks based on your TDD implementation plan (stored in.cursor/scratch/tasks/)
- Execute Tasks: Use the execute tool to work on each task individually. Each task represents exactly one commit, and the agent waits for review before committing
- Track Progress: Progress is maintained in.cursor/scratch/todo.mdwith notes and status updates

All documents and tasks are automatically stored in the.cursor/scratch/folder for easy access and organization.

- build_functional_requirements: Collects comprehensive project requirements
- build_prd: Generates Product Requirements Documents from functional requirements
- build_frd: Creates Feature Requirements Documents for specific features
- build_tdd: Breaks down features into detailed implementation plans with commit-level subtasks
- build_architecture: Generates comprehensive architecture documentation

- initialize_task: Sets up implementation subtasks from TDD plans
- execute_task: Executes individual tasks with review checkpoints

{ "mcpServers": { "Railagent": { "command": "docker", "args": [ "run", "-i", "--rm", "cestbalez/railagent:latest" ] } } }

Restart your MCP client to load the new server.

Note: The Docker image will be automatically pulled from DockerHub on first use. No manual installation or repository cloning required!

railagent/ ├── app.rb # Main application entry point ├── lib/ # Tool implementations │ ├── docs/ # Documentation tools │ └── workflow/ # Workflow management tools ├── prompts/ # Workflow prompts and templates │ ├── docs/ # Documentation workflow prompts │ └── workflow/ # Implementation workflow templates ├── Dockerfile # Docker configuration ├── Gemfile # Ruby dependencies └── README.md # This file

The easiest way to use Railagent is with the pre-built Docker image:

docker run -i --rm cestbalez/railagent:latest

If you want to contribute or modify the code:

git clone <your-repo-url> cd railagent bundle install ruby app.rb

git clone <your-repo-url> cd railagent docker build -t railagent .

- Fork the repository
- Create your feature branch (git checkout -b feature/amazing-feature)
- Commit your changes (git commit -m 'Add some amazing feature')
- Push to the branch (git push origin feature/amazing-feature)
- Open a Pull Request

This project is licensed under the terms specified in the LICENSE file.

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