AI-NATIVE

Your AI assistant, your deployments, your bill.

Jaws ships with a built-in Model Context Protocol server. Point Claude Code, Claude Desktop, Cursor, Codex Desktop, Codex CLI — or anything else that speaks MCP — at your Jaws workspace, and the AI can create projects, assemble deployment pipelines from step templates, read deploy logs, author new templates, and set variables on your behalf.

You pay your AI provider once. We don't charge per token, we don't proxy your model calls, and we don't tie you to a specific vendor. If you switch from Claude Code to Cursor next month, your Jaws setup keeps working.

What you can do today

Build deployment pipelines from a description Tell your AI what you need to deploy and how. It creates the project, adds the right steps from your template library, and fills in each step's properties. Everything lands in Jaws ready to review.
Diagnose failed deploys Hand your AI a deployment ID. It reads the logs (optionally errors-only), the rendered script, and the step properties, and explains in plain English what broke and how to fix it.
Manage variables in bulk Paste a config dump or environment definition, ask the AI to set the corresponding Jaws variables, and watch it happen. Secret-typed variables are intentionally blocked — you still set those by hand.
Navigate and organise by conversation List my workspaces. Move all staging projects into a folder called Staging. Lookups and reorganisation that used to take six clicks become one sentence.

A three-step setup

1. Create a service account in your Jaws workspace under Settings → Service Accounts. Generate an API key. 2. Add the Jaws server to your AI client's MCP config. Copy-pasteable snippets for each major client are in the quickstart guide. 3. Start asking questions. Your AI now sees your workspaces, projects, step templates, variables, and deployment logs.

We could've charged you per token.

Most deploy tools that ship AI features quietly become token resellers. They wrap a model provider, mark up the inference, and bake it into a new pricing tier. That's fine for them and bad for you — your costs scale with conversations, you're locked into whichever model they chose, and your spend on AI is now split across two bills.

We chose MCP instead.

By exposing Jaws as an MCP server, we let your existing AI subscription do the work. You bring your Claude, Cursor, or Codex plan. We bring the deployment surface. No inference markup. No new vendor to evaluate. When models get cheaper and better — which they will — the savings go straight to you.

Built so the AI can't break things on its own

  • No deploy triggers without confirmation. Triggering a deployment is split into preview_deploy (dry run) and trigger_deploy (consumes a short-lived token). Your AI cannot deploy without you seeing a preview first.
  • No secrets in transcripts. Secret-typed variable values are masked when read and rejected when written. Secrets are set by hand in the Jaws UI — they never travel through an AI conversation.
  • Every call is permission-checked. Tool calls go through the same WorkspaceGuard as the REST API. A service account that can't see a workspace can't see it via MCP either.
  • Auditable. Every MCP call is a regular HTTP request to your Jaws hub — shows up in your existing access logs.