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AI CLI MCP Server

AI CLI MCP Server

by kiranmaya
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Python
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antigravity_run

Execute coding tasks non-interactively with Google Antigravity, returning JSON with exit status, execution time, and logs.

Instructions

Executes a coding prompt non-interactively using Google Antigravity CLI (agy).

Args: prompt: Detailed instruction or task description for the Antigravity agent. working_directory: Target project root directory where the task executes. model: Optional model override (e.g. gemini-2.5-pro, gemini-2.5-flash). timeout: Execution timeout in seconds (default: 300, max: 1800). yolo: When true, runs in YOLO mode (--dangerously-skip-permissions) for automated non-blocking execution.

Returns: JSON string containing success status, exit code, execution time, stdout, and stderr.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>",
      "working_directory": "<working_directory>"
    },
    "name": "antigravity_run"
  }
}

Input Schema

NameRequiredDescriptionDefault
yoloNo
modelNo
promptYes
timeoutNo
working_directoryYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and discloses key behaviors: YOLO mode as '--dangerously-skip-permissions' for non-blocking execution, timeout limits, non-interactive operation, and the returned JSON structure. It does not mention prerequisites like authentication or explicit side effects on the working directory, but it is substantially transparent.

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 opens with one clear purpose sentence, then uses a structured Args list and a Returns section. Every sentence adds value with no redundancy or filler, making it efficient and well organized.

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?

The description covers all parameters, the return value, execution mode, and the YOLO flag, making it highly usable. It lacks explicit environmental prerequisites (e.g., AGY installed, authentication) and doesn't explicitly state that the tool may modify files in working_directory, but these are minor gaps given the output schema and detailed Args section.

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

Parameters5/5

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

The description includes a dedicated Args section that explains all five parameters, including defaults and semantics (e.g., timeout default 300 with max 1800, yolo default true, model override). Since schema description coverage is 0%, this fully compensates and adds meaning beyond the input schema.

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 states a specific verb ('executes'), resource ('coding prompt'), and mode ('non-interactively using Google Antigravity CLI'). This clearly distinguishes it from sibling tools like codex_run (different CLI) and antigravity_review (review vs. run), so an agent can tell them apart without opening schemas.

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?

The description provides clear context that this is a non-interactive Antigravity execution tool, and the phrase 'using Google Antigravity CLI' implicitly differentiates it from codex_run. However, it does not explicitly name alternatives or state when not to use this tool, though the context is strong enough to guide selection.

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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