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seeany_plan_product_visual

Read-only

Plan an ecommerce product visual job and estimate its cost before generating. Provide a natural language description and get a plan_id to execute later.

Instructions

Plan a complete product-visual job and estimate its cost without creating a paid generation task. Returns a plan_id for explicit execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
modelNoseeany-quality
requestYesThe desired ecommerce product visual in natural language.
use_caseNoscene
output_dirNoLocal directory used after successful generation../seeany-output
resolutionNo1k
aspect_ratioNo1:1
reference_file_pathsNoOptional local product reference images. They are validated now and uploaded only during execution.
Behavior4/5

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

Annotations already mark the operation read-only and non-destructive; the description adds that no paid generation task is created and that the result is a plan_id for later execution. This is useful behavioral context beyond the annotations, though it doesn't detail plan validity or side effects.

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 entire description is one efficient sentence with the action and key distinction front-loaded. Every clause carries useful information, with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter planning endpoint without an output schema, the description gives a minimal but viable picture: plan, estimate cost, get plan_id. It omits how plan_id should be used to execute the job and what plan details/cost breakdowns the agent should expect.

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

Parameters2/5

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

Schema coverage is only 38%, and the description adds no parameter-level explanations. Most of the eight parameters (count, model, use_case, resolution, aspect_ratio) must be understood from their names/enums; the description does not compensate for the low coverage.

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 opens with a specific action ('Plan... product-visual job'), clarifies the non-paid nature, and names the return value (plan_id). This distinguishes it from paid execution siblings like 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 Guidelines4/5

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

It clearly positions the tool as the estimation/planning step before 'explicit execution,' and the phrase 'without creating a paid generation task' signals when not to use this. It does not explicitly name the sibling to use for execution, but the workflow context is clear.

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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