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text_to_image

Generate images from text prompts using Wan models. Submit a prompt and receive a task id, status, and output image URLs.

Instructions

Create a Wan task on RunAPI (text to image). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
waitNoPoll until the task reaches a terminal status.
modelNoRunAPI model slug for this model line.
promptNo
bbox_listNo
watermarkNo
timeout_msNo
aspect_ratioNo
callback_urlNo
output_countNo
color_paletteNo
thinking_modeNo
poll_interval_msNo
enable_sequentialNo
output_resolutionNo
source_image_urlsNo
enable_safety_checkerNo
Behavior3/5

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

No annotations are provided, so the description carries the transparency burden. It reveals the async task-based nature and mentions the return fields (task id, status, output URLs). However, it omits important behavioral context such as cost implications, authentication requirements, safety checker defaults, or rate limiting.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no filler or repetition. It clearly states purpose and return values, though it could be more expansive given the tool's complexity. As conciseness measures efficiency, it scores well despite being underspecified.

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?

With 17 parameters, no annotations, and no output schema, the description is far from complete. It does not explain the many optional parameters, default behavior, or potential pitfalls, making it insufficient for an agent to use this tool correctly in a real scenario.

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 12% (only wait and model have descriptions). The description itself adds no parameter semantics for the other 15 parameters, leaving the agent to guess the meaning and usage of fields like bbox_list, color_palette, thinking_mode, and enable_sequential.

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 action ('Create a Wan task on RunAPI') with a clear resource ('text to image') and return values (task id, status, output URLs). It distinguishes this from sibling tools like text_to_video or image_to_video by explicitly naming the text-to-image use case.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives such as text_to_video or animate. No prerequisites, exclusions, or typical use cases are mentioned, leaving the agent to infer usage from the name alone.

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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