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Simpler.Asia

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image.generate

[paid: $0.042/image max] Generate 1-3 images from a text prompt on the first lane that delivers, tried in order: gemini-2.5-flash-image via the Cloudflare AI Gateway, square requests only ($0.0406/image), then @cf/black-forest-labs/flux-2-dev via Cloudflare Workers AI ($0.00/image), then google/nano-banana-2-lite via fal ($0.042/image). Each lane settles its own price per delivered image, so an image never costs more than the fal price, the credit a call must have available before it runs; price.charged_micro_usd in the result is the actual charge. Each image is stored in this workspace's image library and returned as JSON {images:[{url, asset_key, download_url?, width, height}], price, next} plus a preview; never a provider link. A result with no download_url says why in download_url_unavailable. Refuses: VALIDATION_ERROR on an empty prompt (describe the image), INSUFFICIENT_CREDITS (add credits), SIMPLER_IMAGE_PROMPT_REFUSED names the label (sexual, minor, real_person or other_unsafe): rewrite the prompt without it, for example no real person's name or likeness; SIMPLER_IMAGE_PROMPT_CHECK_UNAVAILABLE (the safety check is down, nothing charged: retry later); SIMPLER_IMAGE_OUTPUT_FLAGGED (the output failed the content check and was refunded: change the prompt); SIMPLER_IMAGE_OUTPUT_SCAN_UNAVAILABLE (outputs cannot be checked right now, nothing charged: retry later). A partial result lists refunded outputs in output.withheld; SIMPLER_IMAGE_RESULT_UNDELIVERED means nothing was charged: retry. For canvas-sized generation inside an edit session use image.generate_for_edit. NEXT: pass images[0].asset_key as image_asset_key to image.variation or image.upscale.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoNumber of images (1-3, default 1).
widthNoDesired width px (256-2048; the model outputs 1K-class at the nearest aspect ratio).
heightNoDesired height px (256-2048; the model outputs 1K-class at the nearest aspect ratio).
promptYesImage description (max 1500 chars).
request_refNoOptional idempotency key (1-64 chars: letters, digits, . _ : -). Retrying with the same request_ref and the same arguments within 24 hours returns the first result at $0 with replayed: true; the same request_ref with different arguments is refused (SIMPLER_IMAGE_REQUEST_REF_CONFLICT), and so is a retry whose first result was deleted since (SIMPLER_IMAGE_REQUEST_REF_RESULT_DELETED): send a new request_ref to run it again.
preview_formatNoPreview block after the JSON, at most 384px on the long edge and never charged: png, jpeg or none. Left out, a PNG result gets a png preview and a JPEG or WebP result a jpeg one. A result with no preview says why in preview_unavailable.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=false; the description carries the rest and then some: per-lane pricing with a $0.042 ceiling, the pre-flight credit requirement, actual charge via price.charged_micro_usd, idempotent replay at $0, refund behavior for flagged/withheld outputs, and images stored in the workspace library rather than provider links. This is far beyond what a single annotation conveys.

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?

Front-loaded with price, fallback lane order, and output shape before the error catalog, which is the right ordering. It is dense and runs as a single paragraph with a long error enumeration, but for a paid, failure-prone tool nearly every clause 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?

No output schema exists, yet the description fully specifies the return JSON ({images:[{url, asset_key, download_url?, width, height}], price, next}), explains download_url_unavailable and output.withheld, and enumerates every refusal code with a recovery action. Nothing an agent needs to invoke or recover 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, but the description adds real value: the cost implication of the 1-3 count, and the idempotency/conflict behavior of request_ref (replay at $0, SIMPLER_IMAGE_REQUEST_REF_CONFLICT on mismatched args). It doesn't restate width/height/preview_format semantics beyond what the schema documents.

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?

Opens with a specific verb+resource+scope: 'Generate 1-3 images from a text prompt', and immediately distinguishes itself from image.generate_for_edit for canvas-sized generation inside edit sessions. An agent can tell exactly what this does versus its nearest sibling.

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

Usage Guidelines5/5

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

Explicitly routes to image.generate_for_edit for edit-session canvas generation, and gives downstream guidance ('pass images[0].asset_key as image_asset_key to image.variation or image.upscale'). Prerequisites (credit availability) and retry semantics via request_ref are also spelled out.

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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