Great roundup @italojs — a lot of these threads clicked together for me too.
I’ve been working on something that directly addresses several of the points raised here, especially around making Meteor servers MCP-capable. It’s called meteor-wormhole, and it does exactly what a few people in this thread have been asking for: it turns your existing Meteor app into an MCP server, automatically.
What it does
meteor-wormhole is a Meteor 3 package that exposes your Meteor.methods as MCP tools — no extra server, no manual wiring. You add the package, call Wormhole.init(), and AI agents can immediately discover and call your methods via the standard MCP protocol.
import { Wormhole } from 'meteor/wreiske:meteor-wormhole';
Wormhole.init({ mode: 'all', path: '/mcp' });
That’s it. Every method you define with Meteor.methods is now a tool that Claude, GPT, or any MCP-compatible agent can find and invoke.
How it connects to this discussion
On the “MCP server by default” proposal — This is the core idea behind wormhole. It hooks into Meteor.methods at registration time and automatically exposes them. Internal Meteor methods (login, logout, DDP internals, etc.) are excluded by default so it’s safe out of the box. You can also run it in opt-in mode if you want fine-grained control over what gets exposed.
On the streaming gap — The MCP bridge uses streamable HTTP transport (not WebSocket), with proper session management. It embeds directly into Meteor’s WebApp layer, so it respects your existing deployment setup. This gives AI agents a clean, stateless way to interact with your app.
On the WebMCP angle — Since wormhole exposes a standard MCP endpoint at /mcp, it would be compatible with Chrome’s WebMCP once that lands in browsers. Your Meteor methods would become discoverable by in-browser AI agents without any additional work.
On security — You can optionally require an API key (Bearer token) for the MCP endpoint, and in opt-in mode you declare exactly which methods are available with descriptions and JSON Schema input validation. Schemas are validated via Zod under the hood so AI agents get proper type contracts.
What it looks like from the AI side
An agent connects to http://localhost:3000/mcp, calls listTools(), and sees your methods with descriptions and parameter schemas. It calls callTool('createTodo', { title: 'Buy milk' }) and gets back the result as structured content. Your existing method code doesn’t change at all.
Where it’s at
The package works today on Meteor 3.4+. It uses the official @modelcontextprotocol/sdk, supports both all-in and opt-in exposure modes, and has a full test suite. The repo is at github.com/wreiske/meteor-wormhole.
Also check the deployed site here: https://wormhole.meteorapp.com/
I think this sits nicely alongside the other work happening — @dchafiz’s meteor-mcp for development-time AI assistance and @nachocodoner’s AGENTS.md for codebase understanding. Wormhole is the runtime piece: it makes your running Meteor app something AI agents can actually interact with.