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seeany_generate_product_image

Create product image generation tasks with custom prompts, models, and use cases, then retrieve results asynchronously.

Instructions

Create an asynchronous SeeAny product-image task, then use seeany_wait_generation to receive results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
modelNoseeany-quality
promptYesProduct image description, up to 2000 characters.
use_caseNoscene
resolutionNo1k
aspect_ratioNo1:1
reference_asset_idsNo
Behavior4/5

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

Beyond the annotations (which only indicate read/write hints), the description discloses the asynchronous nature of the task and directs the agent to a wait step. This is useful behavioral context that helps the agent understand the tool's execution model.

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?

A single, front-loaded sentence that conveys the core action and the required follow-up with zero filler. Every word earns its place.

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 7-parameter async task with no output schema, the description is too thin. It doesn't explain what the immediate response contains (e.g., a task ID), how the wait step connects, or what the parameters mean. The workflow hint is helpful but insufficient for correct invocation.

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

Parameters1/5

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

Schema description coverage is only 14%, so the description must compensate for undocumented parameters, but it doesn't mention any parameter semantics at all. The agent is left with raw enum values and defaults but no explanation of what count, model, or resolution actually affect.

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?

The description clearly states the tool creates an asynchronous SeeAny product-image task, with a specific verb and resource. It also points to seeany_wait_generation as the follow-up, which distinguishes it from the retrieval/wait siblings, though it doesn't explicitly differentiate it from seeany_create_product_visual.

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 gives a clear workflow context: create the async task, then wait for results. However, it provides no guidance on when to choose this tool over seeany_create_product_visual or other generation siblings, and no exclusions or alternative routing.

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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