Why it works

Good agentic tooling isn't an accident. The dcupl CLI is agent-friendly because of deliberate design choices that remove ambiguity, make state inspectable, and give the agent a way to discover the truth instead of guessing. Here's what matters and why.

How this maps to the three pillars. Two of the pillars are CLI design choices you'll see below — --json everywhere and the stage → build lifecycle. The daemon and schema discovery are two more CLI choices that keep the agent fast and accurate. The third pillar — the dcupl skill — is the other half: it's what teaches the agent to use all of them.

--json everywhere

Every command accepts --json. An agent that parses structured output knows precisely what happened — which records changed, what a query returned, whether a deploy succeeded — without scraping human-formatted text. Reliable parsing means reliable next steps. Errors come back on stdout as { "success": false, "error": "…", "code": "MODEL_NOT_FOUND" } with exit code 1, so the agent can branch on code. The exceptions are dcupl config set, which accepts the flag but prints text, and the long-running dcupl serve.

An explicit stage → build lifecycle

Data mutations stage changes; dcupl app build applies them (or --auto-update applies them immediately). Because the lifecycle is explicit, the agent can see that changes are pending (every staged mutation reports "built": false, and dcupl app status --json reports "pendingChanges": true), reason about what's about to change, and recover cleanly when something looks wrong. Nothing happens by surprise.

Stateful daemon reuse

A daemon here is a background process the CLI starts with dcupl app create — a live, in-memory instance of the dcupl SDK that holds your data and answers queries. It stays alive across calls (until dcupl app destroy, or after 30 idle minutes by default — --idle-timeout), so the agent creates it once, then runs facets, aggregations, and queries against the same in-memory data — fast follow-ups instead of re-ingesting on every question. Conversations about your data stay responsive. (Full lifecycle: Local data apps.)

Schema discovery, not guessing

When the agent needs to author against a model or workflow shape, it doesn't invent field names. It runs dcupl schemas get <Name> --example to see the real structure first. This single escape hatch eliminates a whole class of silent failures — the wrong field name returning empty results.