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PostQuickAI

generateImage

Generate an AI image from a prompt and return a hosted imageUrl for createPost or patchPost. Use listModels with kind=image to choose a model and check its image allowance cost. Nothing is posted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoImage generation model: a model id from GET /v1/models?kind=image, or a built-in name ("gemini-flash", "gpt-image-1.5", "nano-banana-pro").
promptYesDetailed image prompt

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations declare a non-read-only, non-destructive, non-idempotent, open-world operation, and the description adds context those hints cannot carry: the call has a metered cost tied to the model's image allowance, the result is hosted (not posted to any account), and no publishing side effect occurs. It doesn't say whether repeated calls reuse or duplicate assets beyond the idempotency hint, leaving one small gap.

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?

Three short sentences, zero filler, with the primary action and return value front-loaded and the cost/selection guidance plus scope clarification trailing. Every sentence earns its place.

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

Completeness5/5

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

With no output schema, the description carries the return-value burden and does so ('hosted imageUrl'), and it also covers selection guidance, cost awareness, and the no-side-effect guarantee. Nothing an agent needs in order to call it correctly is missing.

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 100%, so the baseline is 3, and the description goes slightly beyond it by explaining where the model id comes from (GET /v1/models?kind=image / listModels) and that the choice affects cost. Prompt is left to the schema's own text, so this is not a full 5.

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?

States a specific verb and resource ('Generate an AI image from a prompt') plus the concrete output ('hosted imageUrl'), which separates it from editImage, customEdit, generateVideo and createVideo without opening a schema. The downstream consumers (createPost/patchPost) are named, so its place in the workflow is unambiguous.

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?

Gives a clear pre-step ('Use listModels with kind=image to choose a model and check its image allowance cost') and an explicit boundary ('Nothing is posted'). It doesn't contrast against the obvious alternative editImage for modifying an existing image, so it stops short of 5.

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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