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AceDataCloud

FluxMCP

by AceDataCloud
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Python
Hybrid

flux_generate_image

Generate AI images from text descriptions using Flux models. Choose from multiple models for different speed and quality tradeoffs, with customizable size and count.

Instructions

Generate AI images from a text prompt using Flux.

Flux is a family of fast, high-quality image generation models by Black Forest Labs.
Different models offer different tradeoffs between speed, quality, and capabilities.

Use this when:
- You want to create new images from a text description
- You need high-quality AI-generated artwork or photos
- You want fast image generation with good prompt following

For editing existing images, use flux_edit_image instead.

Returns:
    Task ID and generated image information including URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoImage size. For flux-dev: pixel dimensions like '1024x1024' (256-1440px, multiples of 32). For flux-2-flex/pro/max: pixel dimensions (x >= 64, multiples of 32). For kontext models: image ratios like '1:1', '16:9', '9:16', '4:3', '3:2', '2:3', '4:5', '5:4', '3:4', '21:9', '9:21'. Default varies by model.
countNoNumber of images to generate. Only supported for generate action. Default is 1.
modelNoFlux model to use for generation. Options: - flux-dev: Fast development model, good balance of speed and quality (default) - flux-pro: Higher quality production model - flux-2-flex: Flux 2 flexible model, pixel sizes (x >= 64, multiple of 32) - flux-2-pro: Flux 2 professional model, high quality - flux-2-max: Flux 2 maximum-quality model - flux-kontext-pro: Context-aware model for editing and style transfer - flux-kontext-max: Maximum context model for complex editing tasksflux-dev
promptYesDescription of the image to generate. Be descriptive about style, subject, lighting, and composition. Examples: 'A majestic mountain landscape at golden hour, photorealistic', 'Cyberpunk street scene with neon lights and rain, cinematic', 'Minimalist logo design of a phoenix, vector art style'
callback_urlNoWebhook callback URL for asynchronous notifications. When provided, the API will POST to this URL when the image is generated.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions that different models have different tradeoffs and returns a Task ID and image URLs, implying asynchronous behavior. However, it lacks details on rate limits, authentication, failure modes, or the exact format of the return value.

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 description is concise, with a clear one-line purpose, bulleted usage guidelines, and a separate 'Returns' section. Every sentence adds value with no redundancy.

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?

Given the presence of an output schema and sibling tools, the description covers the tool's purpose and distinctions adequately. However, it does not explain the asynchronous nature of generation or how to use the callback_url parameter, leaving gaps for a complete understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers all 5 parameters with detailed descriptions (100% coverage). The description adds no new parameter semantics beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting.

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 clearly states the verb 'Generate' and resource 'AI images from a text prompt using Flux.' It distinguishes from the sibling 'flux_edit_image' by explicitly stating 'For editing existing images, use flux_edit_image instead.'

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?

The description provides a structured 'Use this when' list covering creation needs and quality requirements. It explicitly mentions the alternative tool for editing. However, it does not address other scenarios like checking generation status with 'flux_get_task'.

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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