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agy-image

agy-image

by ahmadarif-lab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Antigravity Status

antigravity_status

Verify Antigravity image-generation auth and configuration when generate_image fails. Inspects local token, model, and host without consuming quota.

Instructions

Check Antigravity image-generation configuration & auth status. Only inspects local state (token file, model, host) and makes sure the token can be refreshed — it doesn't call the image endpoint, so it doesn't use any quota. Use this when generate_image fails.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "antigravity_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does so well: it discloses that it only inspects local state (token file, model, host), does not call the image endpoint, and therefore consumes no quota. This side-effect and cost disclosure is exactly what an agent needs. It stops short of describing what a failure vs. success result looks like, which is a minor gap given there is no output schema.

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?

Three tight sentences, front-loaded with the tool's purpose and immediately following with scope, cost implications, and the usage trigger. Every sentence earns its place with no filler.

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?

For a diagnostic tool with no parameters and no output schema, the description is nearly complete: it explains what is inspected, that no external call is made, and when to invoke it. The only omission is an indication of what the returned diagnostic information contains, which the agent would otherwise have to discover empirically.

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 tool takes zero parameters, so the parameter-semantics baseline is 4. The description adds useful framing about the internal state it inspects (token file, model, host), which orients the agent even though there is nothing to configure.

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 specific verb (Check) and resource (Antigravity image-generation configuration & auth status), and explicitly scopes it as a local-state inspection rather than an image operation. This cleanly separates it from the generate_image sibling, which performs the actual generation.

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

Usage Guidelines5/5

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

Explicitly names the trigger condition: 'Use this when generate_image fails.' By naming the sibling and the failure scenario that selects this diagnostic tool, it gives the agent a clear routing rule with no ambiguity about when this tool applies.

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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