Your AI Agent Can Deploy to a Server You Own Now (and to Tor)

Your AI assistant can write the whole app. It scaffolds the project, writes the Dockerfile, fixes the failing test, and explains the bug it just introduced. Then it stops at the one step that actually puts the thing on the internet, and hands you back to the terminal. You copy the build command, SSH into a box, fight with a reverse proxy, wire up TLS, and come back an hour later to tell the AI it worked.

That last gap is closing in 2026, and most people have not noticed because the word “MCP” got attached to something else first.

What MCP actually did, and what it mostly did not

The Model Context Protocol gave AI assistants a standard way to call external tools. The ecosystem exploded, and if you read the coverage you would think MCP is mainly about letting an agent read your Salesforce, your Microsoft 365, your Google Workspace. That is most of what got built: connectors that let the AI see and act inside SaaS you already pay for. Useful, and also a giant surface of someone else’s data.

Almost none of it is about the thing developers actually want from an agent, which is not “read my CRM” but “ship the code you just wrote.” Taking an agent from suggesting a deploy to performing one is a much smaller club. The handful of tools doing it are mostly narrow: one well known option will deploy a Next.js app to a domain, on infrastructure it manages, and that is the shape of the offering. Fine if you write Next.js and you are happy on their platform. Not much help if you wrote a Go service, or a Python worker, or you want the server to be yours.

The other kind of deploy MCP

There is a second model that almost nobody is talking about: an MCP server that deploys any app to a server you own.

That is what impreza-mcp does. It exposes a set of tools, thirteen of them, that let Claude, Cursor, Codex, Continue, or Zed do the full deploy loop against infrastructure that belongs to you. Not a managed sandbox with your project on it. A VPS or Dedicated Server that is yours, that you paid for, that you can SSH into independently of the AI.

The tools cover the real workflow rather than a demo of it: list and launch apps from a catalog, deploy custom builds from a public image, a local Dockerfile, or a git repo, manage domains and TLS, read logs and status, redeploy, and tear down. The agent is not pretending to deploy. It is calling a real API against a real machine.

Setting it up

It is an npm package and a standard MCP server over stdio, so wiring it into any MCP client is the usual block of config. In Claude Code or Cursor it looks like this:

{
  "mcpServers": {
    "impreza": {
      "command": "npx",
      "args": ["-y", "impreza-mcp"],
      "env": {
        "IMPREZA_API_KEY": "imp_your_key_here",
        "IMPREZA_API_SECRET": "your_secret_here"
      }
    }
  }
}

You generate the key and secret once in the client area. From then on, the agent has the thirteen tools available, and you talk to it the way you already do. “Deploy this repo to my server, give it a domain, and turn on HTTPS” becomes a conversation instead of a checklist.

The part no other deploy tool can do

Here is the line that does not exist anywhere else in the AI deploy space: the agent can publish your app as a Tor onion service at the same time, by flipping a single flag.

Ask it to deploy with an onion mirror and it does both: the app comes up on its normal address, and a Tor v3 onion address serves the same thing, with no extra infrastructure to stand up and nothing for you to configure by hand. “Deploy this and give me a .onion” is now one sentence to an AI, where six months ago it was a weekend of reading Tor documentation.

And because the platform underneath is the same privacy first infrastructure Impreza runs, the whole chain inherits properties the managed AI deploy tools structurally cannot offer. You signed up with an email and nothing else, no KYC. You paid in crypto. The server lives in a jurisdiction you chose. The agent is deploying to a machine that was never tied to your name, and now it can put that machine on Tor for you in one step.

Being honest about the risks

An MCP server that can deploy and tear down infrastructure is, correctly, something to think about before you wire it in. The same property that makes it useful, that it can act and not just read, is the property that deserves caution.

Two practical rules. First, the key and secret you give the agent are real credentials with real power, so treat them like any production token: scope them, store them in the client config rather than in your repo, and rotate them if they leak. Second, an agent that can deploy can also deploy something wrong, so for anything that matters, keep yourself in the loop on what it is about to do rather than letting it run unattended against production. The tool gives the AI reach. You still own the judgment.

None of that is specific to this server. It is true of every MCP tool that writes instead of reads, and the wider ecosystem has spent 2026 learning it the hard way through SaaS connectors that returned far more than anyone intended. Deploy tools are no different, and a little discipline up front is the whole mitigation.

Where this goes

The interesting thing is not that an AI can deploy. Several tools can do a narrow version of that. The interesting thing is that the deploy target can be a server you own, in a jurisdiction you picked, paid for anonymously, reachable over Tor, and driven entirely from the same chat where you wrote the code.

That is a different stack than “let the AI push my Next.js app to a platform.” It is the self hosted, privacy-first version of agent driven deployment, and right now it is close to the only one of its kind. If your workflow already runs through an AI assistant, the last manual step just became another sentence in the conversation, and the box on the other end of it is yours.

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