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Ask Antigravity (new conversation)

antigravity_ask

Ask Antigravity CLI a question in a new conversation, using existing AI Pro authentication to return the model's final text response—ideal for short tasks and fast tool-calling.

Instructions

Ask Antigravity (agy CLI, Gemini by default) a question in a NEW conversation.

Uses your existing AI Pro authentication (silent-auth via Windows Credential Manager). Returns the model's final response as text. Good for fast tool-calling and short tasks; for heavier reasoning pick a bigger model or use the host model directly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
planNoIf true, run agy in PLAN mode (agy 1.1.12+): it investigates and writes an implementation plan instead of touching anything. Verified on 1.1.20 that a file write and a shell command are both refused and diverted into a plan document under agy's own directory — even when the prompt insists, and even though the bridge still passes --dangerously-skip-permissions — while file READS answer normally. Use it to point Antigravity at a repo you don't want it editing. Two caveats. It is agent-enforced, not an OS sandbox: it constrains agy's agent loop, so treat it as a strong default rather than a boundary you'd rely on against a hostile prompt (Codex has the real one — see codex_ask's sandbox, and its Windows caveat: as of codex 0.149.1 a sandboxed run there refuses every command and answers anyway). And it is exclusive with the bridge's slash-command shield, because agy silently disables plan mode when that shield is on; a prompt whose first token is a slash command is therefore rejected up front rather than run. Raises on agy older than 1.1.12, which ignores --mode in print mode, rather than silently running your prompt unrestricted. Default false.
modelNoOptional model slug to run this conversation on (agy's --model), e.g. "gemini-3.1-pro-high" or "claude-sonnet-4-6". Omit to use the model set in agy's settings.json (gemini-3.7-flash-high by default). Must be one of `agy models` — an unknown slug is rejected up front (agy would otherwise silently ignore it and fall back to the default). agy 1.1.5 replaced the old human labels ("Gemini 3.1 Pro (High)") with these slugs, and the default has since moved to the gemini-3.7-flash family; the old form is no longer accepted. See antigravity_status / `agy models` for the valid slugs.
watchNoIf true, open a live "watch" view in your browser that streams agy's steps (narration + the real commands it runs) as it works. agy still runs headless; the same final text is returned. Best- effort and cross-platform — if the browser can't open, the run completes normally. Default false.
promptYesQuestion or instruction for Antigravity.
schemaNoOptional JSON Schema (an object, or its JSON text). When given, agy is asked to produce output matching it (agy 1.1.8's --json-schema) and this tool returns the VALIDATED OBJECT as JSON text instead of prose — json.loads it. What comes back is agy's own `structured_output`, which carries exactly the declared fields; agy's prose `response` on the same run also picks up its internal toolAction/toolSummary keys and can be prefixed with a sentence, so the two are NOT interchangeable. If agy produces no structured output the call RAISES rather than handing back prose you would have to parse anyway. Needs agy 1.1.8+. IMPORTANT — write the prompt so the ANSWER is in the turn, and let the schema only shape it. agy fills the schema in a finishing pass that does not re-reason about the content, so a field the turn never established gets guessed from the schema itself. Measured on 1.1.20 with "this broke my build and wasted my whole afternoon": with enum ["positive","negative"] it answered "positive" 3 times out of 4, and simply REVERSING the enum to ["negative","positive"] flipped it to "negative" 2 out of 2 — it was following field order, not the sentence. Adding a `reason` field did not help; the reason came back "Completed sentiment classification task." Asking the prompt to state the verdict and why, and keeping the same biased enum, was correct 3 out of 3. So: extraction of what the model has already worked out is reliable; a judgment delegated to the schema is not.
timeout_sNoMax seconds to wait for agy to complete. Default 180.
workspaceNoWorking directory for the conversation. Defaults to cwd. Choose an existing project dir for context-aware responses.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already carry the safety profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false), and the description adds useful context about silent authentication via Windows Credential Manager and the text response format. It does not explicitly state in the top-level text that normal runs can execute commands or mutate the workspace, though 'tool-calling' and the schema's plan/watch caveats hint at that behavior; there is no contradiction with the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The top-level description is four short sentences, front-loaded with purpose and scope, with no filler. It adds auth, output-format, and usage-context information without repeating the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With rich parameter descriptions, annotations, and an output schema, the definition gives an agent what it needs to select and call the tool correctly. The remaining gaps are that the top-level description does not summarize the potential side-effect/no-sandbox behavior of normal mode, and the choice versus `antigravity_continue` is left implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameter descriptions already carry the semantics: plan-mode caveats, model-slug validation, structured-output behavior, workspace, and timeout. The top-level description adds only the 'bigger model for heavier reasoning' hint, which is useful but not necessary beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear action ('Ask Antigravity'), the underlying CLI (agy, Gemini by default), and the scoping trait 'NEW conversation', which separates it from `antigravity_continue`. It also names the expected output (final response as text) and typical use ('fast tool-calling and short tasks'), so an agent can tell what this tool is without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit context: use it for fast tool-calling and short tasks, and for heavier reasoning pick a bigger model or use the host model directly. The 'new conversation' phrasing implies it is not for continuing an existing thread, but it never names `antigravity_continue` or states a hard exclusion, so alternatives are only implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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