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pasie15
by pasie15

create_text_to_image_task

Generate images from text prompts by choosing an AI model, then set aspect ratio, pose mode, multi-view angles, or transparent background output.

Instructions

Generate an image from a text prompt. ai_model is required and accepts 'nano-banana', 'nano-banana-2', 'nano-banana-pro', or 'gpt-image-2'. aspect_ratio for the nano-banana family: '1:1', '16:9', '9:16', '4:3', or '3:4'; for gpt-image-2: '1:1', '3:2', or '2:3' (default '1:1'). pose_mode accepts 'a-pose' or 't-pose'. Set generate_multi_view=true to generate multi-angle views (cannot be combined with aspect_ratio). Set remove_background=true to return a transparent RGBA PNG.

Input Schema

NameRequiredDescriptionDefault
promptYes
ai_modelYes
pose_modeNo
aspect_ratioNo
remove_backgroundNo
generate_multi_viewNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.2

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose real behavioral facts (mutual exclusion between generate_multi_view and aspect_ratio, and that remove_background yields a transparent RGBA PNG). It is silent on whether the call is asynchronous, whether it returns a task ID, credit cost, and whether failures are retryable — significant omissions for a creation endpoint.

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?

Front-loaded with the core action, then dense per-parameter constraints with no filler or repeated information. Every clause adds a fact an agent would otherwise have to guess.

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?

Parameter-level coverage is excellent, and with no output schema the RGBA PNG note usefully hints at the return. But a create_*_task tool in a suite full of retrieve/stream/list siblings should tell the agent how to obtain the result, which it does not.

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?

Schema coverage is 0% and all six parameters are undocumented in the schema, so the description does the heavy lifting well: it enumerates ai_model values, gives per-model aspect_ratio valid sets with a default, lists pose_mode values, and explains the two booleans' effects. Only `prompt` is left uncharacterized, which keeps it short of a 5.

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?

States a specific verb and resource: "Generate an image from a text prompt." The phrase "from a text prompt" implicitly separates it from create_image_to_image_task, but it never names that sibling or any other alternative, so the differentiation is inferred rather than stated.

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?

The description says nothing about when to pick this over create_image_to_image_task, nor about the task lifecycle implied by the name — the many retrieve_/list_/stream_/delete_ siblings suggest an async job that must be polled, yet no guidance is given on that workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.