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submit_job

Queue an asynchronous Antigravity task in the caller's current workspace and get a job ID immediately, so work runs in the background without blocking.

Instructions

Queue one asynchronous Antigravity task in the caller current workspace. Returns immediately with a job id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNo
agentNo
modelNoExact model id returned by list_models, not a display label.
effortNo
promptYesTask prompt for the Antigravity worker.
accountNoAccount id, email, or alias. Omit for quota-aware scheduling.
projectNo
add_dirsNo
workspaceYesRequired absolute current cwd/workspace of the calling AI session. Never use the MCP server installation directory as a task workspace.
read_pathsNoConcrete dependency paths read while running. Shared reads can run together; reads wait for overlapping writers.
json_schemaNo
timeout_secNo
write_pathsNoConcrete output paths relative to workspace. Omit to lock the entire workspace. [] is read-only; no globs.
include_activeNo
conversation_idNo
permission_modeNo
estimated_duration_secNoPlanning estimate for critical-path scheduling; not a deadline or measured prediction.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It does disclose two important traits: the task is asynchronous and the call returns immediately with a job id (non-blocking). However, it says nothing about scheduling/quota behavior, permission implications, failure modes, or how the job's workspace locking interacts with other calls.

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?

Two short sentences, zero filler, with the core action and the return contract front-loaded. Nothing to trim.

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

Completeness2/5

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

For a 17-parameter mutation tool with no annotations, no output schema, and only 41% parameter coverage, the description is far too thin. Behaviors like workspace locking via write_paths, permission_mode semantics, and quota-aware account selection are exactly what an agent needs and none of it is addressed.

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

Parameters2/5

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

17 parameters with only 41% schema description coverage, so the description needs to compensate and does not. It never mentions any of the semantically load-bearing params (permission_mode, write_paths locking, effort, account quota-aware scheduling, estimated_duration_sec), leaving nearly half the parameters documented nowhere.

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

Purpose4/5

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

States a specific verb and resource ('Queue one asynchronous Antigravity task') plus scope ('in the caller current workspace') and return behavior ('returns immediately with a job id'). The word 'one' implicitly distinguishes it from submit_batch/submit_workflow, though the description never names those siblings explicitly.

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

Usage Guidelines2/5

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

No when-to-use or when-not-to-use guidance. Nothing tells the agent to prefer submit_batch for multiple tasks or submit_workflow for orchestrated pipelines, despite those siblings existing. Only the implied async/non-blocking nature hints at context.

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