A Model Context Protocol (MCP) server that provides structured workflows and tools for AI agents working with software development projects, with
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. The railagent mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 7 defined tools rather than through you.
Installation goes through your MCP client rather than a global install: point it at cestbalez/railagent on a container image and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Everything the assistant can do here goes through one of these:
build_functional_requirements — Collects comprehensive project requirementsbuild_prd — Generates Product Requirements Documents from functional requirementsbuild_frd — Creates Feature Requirements Documents for specific featuresbuild_tdd — Breaks down features into detailed implementation plans with commit-level subtasksbuild_architecture — Generates comprehensive architecture documentationinitialize_task — Sets up implementation subtasks from TDD plansexecute_task — Executes individual tasks with review checkpointsPlenty of knowledge and memory servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Railagent's toolset — build_functional_requirements, build_prd, build_frd and 4 more — is a fair guide to whether it matches your workflow. It is maintained by railwave-labs; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| 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"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.