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generate_image_async

Start an asynchronous image generation task from a text prompt. Get a task ID to poll for results, ideal for long-running or batch generation.

Instructions

Start an asynchronous image generation task. Returns a task ID to poll for results. Use this for long-running generations or when you need to process multiple images.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoImage size (e.g., "1280x1280", "1568x1056")1280x1280
modelNoModel to use (only glm-image supports async)glm-image
promptYesText description of the image to generate
qualityNoQuality level (only "hd" supported for async)hd
user_idNoUnique end user ID for abuse prevention (6-128 characters)
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the async behavior and the need to poll for results. It could add details about polling mechanics or error handling, but it is sufficient for most use cases.

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 sentences: first defines the tool's action, second provides usage guidance. No fluff, front-loaded information.

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 no output schema, the description adequately mentions the return value (task ID). It could explicitly reference 'get_async_result' for polling, but the context of async generation is clearly set.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already explains all parameters. The description does not add additional meaning to individual parameters; it sets the overall context for the async task.

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 it starts an asynchronous image generation task and returns a task ID. It uses specific verbs ('Start', 'Returns') and resource ('task ID'). The async nature distinguishes it from siblings like 'generate_image'.

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?

Explicitly says 'Use this for long-running generations or when you need to process multiple images.' This provides clear context for when to use, though it does not explicitly mention alternatives or when not to use.

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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