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Image generation, generate image from a text prompt: create a picture from a description with an AI image model

image
Read-only

Text-to-image generation with FLUX.1 schnell. Send a prompt, get a 1024x1024 JPEG back as base64 in a few seconds. Use for illustrations, thumbnails, mockups, avatars and social media images. Optional steps parameter (1-8, default 4) trades speed for detail. Example: prompt=a lighthouse on a cliff at sunset, watercolor. Price $0.03/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input Schema

NameRequiredDescriptionDefault
stepsNoDiffusion steps, 1-8 (default 4)
promptYesWhat the image should show (3-800 characters)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

It goes far beyond the annotations by providing technical context: model name (FLUX.1), output format (1024x1024 JPEG base64), price ($0.03), processing time ('a few seconds'), and explicitly stating its read-only, non-mutating nature.

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 definition is highly structured, leading with the core function, providing constraints in the middle, and ending with operational context (cost/safety) efficiently.

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?

Since no output schema is provided, the description is remarkably complete, detailing the input constraints, output format, cost, and behavioral guarantees (no state change).

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?

While the schema is complete, the description provides crucial semantic context for the 'steps' parameter, clarifying the default value (4) and the acceptable range (1-8).

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 tool is explicitly defined as performing 'Text-to-image generation' using the FLUX.1 model, clearly differentiating it from the list of text and language model siblings.

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?

The description provides specific use cases (illustrations, thumbnails, avatars, social media) which helps the agent decide if this tool is appropriate for a given user intent compared to other utilities.

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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