The whole cloud, drivable by an agent.
Because the project is files and every operation is an explicit command, a coding agent can stand the backend up, verify it, publish it, and read back what happened — the same lifecycle you drive by hand.
Files stay authoritative
Automation edits the project you review; it can't invent a setting that lives nowhere.
MCP follows the CLI
Agent tools share the schemas, errors, and dry runs of the command-line workflow.
The docs come with it
An agent reads these pages over the same connection, so it follows the patterns rather than guessing.
Agents, running
local developmentA project an agent can actually read
The backend is a folder of committed files rather than a console someone has to click through, so an agent proposes changes the same way you do — as a diff you review, with the configuration sitting next to the code that depends on it.
- Every resource is declared in one manifest it can validate
- Validation reports the offending file and line, not a stack trace
- Nothing important lives in a setting the repository can't see
$ snoozestack check --json
{ "valid": true, "files": 18 }
$ snoozestack publish --preview review
✓ preview review → https://review-my-app.snoozestack.comWrite, then verify — the loop that finishes work
The useful part isn't automating the clicking. It's that an agent can observe what it did: run the project locally, call the function it just wrote, read the signals back, and correct itself before handing anything over.
- The CLI for you and CI, MCP for AI clients, one API underneath
- Tools are annotated read-only or destructive, so clients can confirm
- These docs are served over the same connection the tools are
"snoozestack-my-app": {
"type": "http",
"url": "https://snoozestack.com/api/mcp/my-app",
"headers": { "Authorization": "Bearer sb_mcp_..." }
}Built for real applications
What teams build with Agents.
Building the backend alongside the app
The agent writing your checkout page defines the table, writes the function, sets the key, and publishes — in the same pass as the client code.
Debugging what it just shipped
Deploy, call, read the logs, fix, redeploy. Every step is a tool, so it iterates on real output instead of asking you what the error said.
Reproducible environments
Provisioning a project per customer, or a throwaway one per pull request, is a script against the same operations you run by hand.
Developers, CI, and coding agents share the same project model and operational interfaces.
Why Snoozestack →