Let Your AI Agent Scaffold Apps With seed4j-mcp

Seed4j-mcp is a new open-source Model Context Protocol (MCP) server that allows AI agents like Claude Code and Cursor to control the seed4j application generator, enabling them to scaffold Spring Boot and Vue projects automatically. The tool acts as a thin translator between MCP tool calls and seed4j's REST API, allowing developers to describe their desired project in natural language while the AI agent handles the orchestration of modules, dependencies, and configuration. Currently in alpha, seed4j-mcp is available as an npm package and requires a running seed4j server on localhost.

If you've ever bootstrapped a Spring Boot + Vue project by hand, you know the routine: pick a build tool, glue in a frontend, add JPA, choose a database driver, wire Liquibase, remember the Maven wrapper, look up that one annotation for the seventh time this year. By the time you reach initial commit , half your motivation is gone. seed4j https://github.com/seed4j already solves most of that. It's an open source application generator that applies modules to a project folder — click click click, project done. But seed4j is still a tool a human drives . You read the docs, you pick the module slugs, you remember whether the property is packageName or basePackage this release. Fine, but not exactly thrilling. seed4j-mcp https://github.com/avdev4j/seed4j-mcp is a Model Context Protocol https://modelcontextprotocol.io server that hands seed4j to your AI agent instead. Plug it into Claude Code, Claude Desktop, or Cursor, and the agent can: - browse the seed4j catalog, - pick a coherent stack, - validate properties before applying anything, - and scaffold a project end to end. You stay in the conversation. The agent does the orchestration. Heads up — this is an alpha. seed4j-mcp is a fresh first release. The tool surface, defaults, and behavior may change as feedback comes in. It's stable enough to play with, but don't wire it into anything critical yet. Bug reports and suggestions are very welcome. How the pieces fit together seed4j-mcp is intentionally thin. Three components: - A running seed4j instance — the actual generator, reachable over HTTP default http://localhost:1339 . - — a Node.js MCP server that translates MCP tool calls into seed4j REST calls. seed4j-mcp - Your MCP client — Claude Code, Claude Desktop, Cursor, etc., speaking MCP over STDIO to seed4j-mcp . Claude Code ⇄ STDIO ⇄ seed4j-mcp ⇄ HTTP ⇄ seed4j server That separation matters: seed4j-mcp does not embed seed4j as a library. It's a translator. seed4j can evolve on its own and the MCP server keeps doing its job — no version-pinning gymnastics. One implementation detail worth knowing: seed4j-mcp speaks MCP over STDIO . The MCP framing lives on stdout, which means anything else writing to stdout will corrupt the stream and your MCP client will hang. The server routes its startup errors to stderr for that reason. If you fork the project and add logging, use console.error or write to a file — never console.log . Tutorial Prerequisites Three things on your machine: - Node.js 20+ — seed4j-mcp ships as an npm package and runs under Node yes, even if you're a Java person . - A running seed4j instance — see the seed4j docs https://github.com/seed4j for how to start one. Default URL: http://localhost:1339 . - An MCP-aware client — I'll use Claude Code https://claude.com/claude-code , but Claude Desktop and Cursor work the same way. Quick sanity checks: node --version v20.x or newer curl http://localhost:1339/api/modules | head -c 200 If the curl call returns JSON, you're ready. Getting started You don't need to install seed4j-mcp globally. The package is published on npm and the recommended pattern is to let your MCP client launch it on demand via npx . First run pulls it down, later runs use the cache. If you'd rather install it globally: npm install -g seed4j-mcp Or build from source if you want to contribute: git clone https://github.com/avdev4j/seed4j-mcp.git cd seed4j-mcp npm install npm run build Run it with Claude Code Claude Code has an mcp subcommand built for exactly this. Pick a scope based on how widely you want the server available: Local: just you, this project default scope claude mcp add seed4j -- npx -y seed4j-mcp Project: committed to .mcp.json, shared with collaborators claude mcp add seed4j --scope project -- npx -y seed4j-mcp User: available across all your projects on this machine claude mcp add seed4j --scope user -- npx -y seed4j-mcp Running seed4j on a non-default URL? Pass it as an env var: claude mcp add seed4j --env SEED4J BASE URL=http://localhost:7471 -- npx -y seed4j-mcp Verify the server is wired up: claude mcp list Restart Claude Code and the tools become available to the agent automatically. Run it with Claude Desktop or Cursor Both clients read a JSON config. Add an entry pointing at the npx entrypoint: { "mcpServers": { "seed4j": { "command": "npx", "args": "-y", "seed4j-mcp" , "env": { "SEED4J BASE URL": "http://localhost:1339" } } } } Restart the client and you're done. Example usages The real test is what the agent does once the tools are available. A few prompts that exercise different flows. 1. "Scaffold a Spring Boot + Vue webapp" The curated stack path. You name the vibe, the agent picks a preset. Under the hood it will: - Call list presets to see what's on offer. - Pick the one that matches e.g. "Webapp: Vue + Spring Boot" . - Call get preset details for the ordered module list. - Call create project to initialize the folder. - Call apply preset to apply every module in order with a shared property map. Your prompt is one line: Scaffold a new project at /tmp/my-webapp using a Vue + Spring Boot preset. Use com.example.webapp as the Java package and mywebapp as the base name. 2. "I want a custom stack, not a preset" The custom stack path. The agent will: - search modules to find candidates matching your description. - get module dependencies to learn the prerequisite ordering and any feature choices yes, you have to pick a datasource flavor — the agent won't guess for you . - validate properties to dry-run the property map before touching the filesystem. - apply modules to apply the full ordered list in one batch, stopping at the first failure. Example prompt: Create a Java library project at /tmp/my-lib with Maven, Jacoco, and SonarQube wired up. Package com.example.lib . 3. "What's already in this project?" When the folder exists, get project status tells the agent which modules have been applied and what properties are in play — so it suggests sensible next steps instead of stomping over existing state: Look at /tmp/my-webapp and tell me what's wired up. What would you suggest adding next? 4. "Show me the catalog" You can also use the agent as a guided browser. It will reach for search modules and get module details and summarize the catalog: What persistence modules does seed4j support? Compare Postgres, MySQL, and MongoDB options. Available tools The agent gets a small but expressive toolbox covering module discovery, preset lookup, property validation, and project application. Rather than reproducing the full list here and watching it drift the next time a tool is added , check the up-to-date catalog directly in the repo: The README lists every tool with the description the agent actually sees. See it in action If you'd like to see what a project scaffolded through seed4j-mcp actually looks like, here's one I generated: It's the output of a real agent session — useful as a reference for the kind of structure, files, and module combination you can expect when you hand the wheel to your AI agent. Wrapping up Application generators have always traded flexibility for speed: faster than handcrafting, less flexible than writing it yourself. Putting an LLM in front of one tips the balance — the agent handles orchestration, and you stay at the level of "build me a Java library with Jacoco and SonarQube" . No memorizing slugs, no fighting property names. And the scaffolded project isn't a dead drop. seed4j writes a documentation/ folder alongside the code with the conventions, code style, and recommendations specific to the modules you applied. That folder is a goldmine for the next step: pointing your AI agent at it while you write the business code keeps the generated structure and the hand-written code coherent — same package layout, same patterns, same style — instead of the usual drift between bootstrapped scaffolding and the features you add on top. seed4j-mcp is Apache 2.0. Source at github.com/avdev4j/seed4j-mcp https://github.com/avdev4j/seed4j-mcp , published on npm as seed4j-mcp https://www.npmjs.com/package/seed4j-mcp . Issues, PRs, and creative stack requests welcome. If you ship something with it, I'd love to see it.