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send_spark_to_antigravity_task

Creates a structured task envelope with objective, context, and step-by-step actions for Antigravity IDE to execute, providing a clear task brief for guided implementation.

Instructions

Sends a crystal-clear, structured task brief from Gemini Spark to Antigravity IDE. Automatically generates a formatted markdown instruction envelope with Task ID, objectives, context, step-by-step actions, and reporting instructions.

Parameters: objective: Clear 1-2 sentence primary goal. context: Optional background, architectural details, or file paths. required_actions: Optional ordered list of specific steps (e.g. ["write tests", "run pytest", "fix bugs"]). conversation_id: Target Antigravity conversation UUID (if None, targets most recent active). working_dir: Target working folder on disk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNo
contextNo
objectiveYes
working_dirNo
conversation_idNo
required_actionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that it automatically generates a formatted markdown instruction envelope, which is a useful behavioral detail, but it does not mention side effects, permissions, reversibility, or error handling. This is a moderate disclosure but not comprehensive.

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 description is concise, front-loaded with the primary purpose, and structured with a parameter list. Every sentence adds value, and there is no redundancy with the schema. It efficiently conveys both function and parameter usage.

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 the tool's complexity (6 parameters, 1 required) and the presence of an output schema (implied), the description covers most essential aspects: it explains the core behavior, provides parameter details, and notes the default behavior for conversation_id. It lacks guidance on edge cases or how to obtain a conversation UUID, but is otherwise sufficient.

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?

The description adds meaningful semantics for 5 of 6 parameters, compensating for the 0% schema description coverage. Each described parameter includes purpose and examples (e.g., required_actions lists ordered steps). However, the 'source' parameter is missing from the description, leaving a gap.

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?

The description clearly states the tool sends a structured task brief from Gemini Spark to Antigravity IDE and describes the generated markdown envelope. It is specific about the verb, resource, and scope, and distinguishes it from siblings by its cross-IDE purpose.

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

Usage Guidelines3/5

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

The description implies usage for sending task briefs but provides no explicit guidance on when to use this tool versus alternatives like run_agent_task or inject_message. It lacks exclusions or alternative routing, leaving the agent to infer the use case.

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