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Ask Antigravity (new conversation)

antigravity_ask

Submit a prompt to the Antigravity CLI agent in a new conversation; returns the model's answer as text using your existing authentication.

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
modelNoOptional model label to run this conversation on (agy's --model), e.g. "Gemini 3.1 Pro (High)" or "Claude Sonnet 4.6 (Thinking)". Omit to use the model set in agy's settings.json (Gemini 3.5 Flash (High) by default). Must be one of `agy models` — an unknown label is rejected up front (agy would otherwise silently ignore it and fall back to the default). See antigravity_status / `agy models` for the valid labels.
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.
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
Behavior4/5

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

Adds valuable context beyond annotations: explains authentication method (silent-auth via Windows Credential Manager), return type (text), and watch behavior (opens browser). Annotations indicate openWorldHint=true and readOnlyHint=false, which description aligns with. No contradictions.

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?

Description is concise (4 sentences) and front-loaded with purpose and key behavioral info. Every sentence adds value including auth, return type, and usage recommendation.

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?

Given 5 parameters, output schema, and sibling tools, description covers essential aspects: purpose, auth, return, and usage guidance. Could mention timeout_s and workspace, but schema already provides those. Overall sufficient for correct invocation.

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

Parameters4/5

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

Schema coverage is 100%, so baseline 3. Description adds extra guidance on model parameter ('for heavier reasoning pick a bigger model'), enhancing understanding beyond schema descriptions. No other param details needed as schema covers them.

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?

Description clearly states 'Ask Antigravity... a question in a NEW conversation', specifying verb, resource, and distinguishing from sibling tools like antigravity_continue (which continues an existing conversation).

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?

Indicates tool is 'Good for fast tool-calling and short tasks' and advises 'for heavier reasoning pick a bigger model or use the host model directly', providing context for when to use vs alternatives. Could be more explicit about when not to use, but sufficient.

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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