xpay✦ Media Studio

black_forest_labs_flux_pro

FLUX 1.1 Pro — highest quality photorealistic image generation

Input Schema

TableJSON Schema

Name	Required	Description	Default
`seed`	No	Random seed. Set for reproducible generation
`width`	No	Width of the generated image in text-to-image mode. Only used when aspect_ratio=custom. Must be a multiple of 32 (if it's not, it will be rounded to nearest multiple of 32). Note: Ignored in img2img and inpainting modes.
`height`	No	Height of the generated image in text-to-image mode. Only used when aspect_ratio=custom. Must be a multiple of 32 (if it's not, it will be rounded to nearest multiple of 32). Note: Ignored in img2img and inpainting modes.
`prompt`	Yes	Text prompt for image generation
`aspect_ratio`	No	Aspect ratio for the generated image	1:1
`image_prompt`	No	Image to use with Flux Redux. This is used together with the text prompt to guide the generation towards the composition of the image_prompt. Must be jpeg, png, gif, or webp.
`output_format`	No	Format of the output images.	webp
`output_quality`	No	Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs
`safety_tolerance`	No	Safety tolerance, 1 is most strict and 6 is most permissive
`prompt_upsampling`	No	Automatically modify the prompt for more creative generation

TDQS

C2.8/5.0

Behavior2/5

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

With no annotations, the description carries full burden, but it only says 'image generation' without disclosing safety settings, output formats, or side effects. It does not mention the ability to work with image prompts or other behavioral nuances.

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?

The description is extremely concise, using a single phrase that communicates the model version and capability. It is not a tautology, though it may be too brief to be considered fully structured.

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

Completeness1/5

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

Given the tool's complexity (10 parameters, no output schema, no annotations), the description is far too minimal. It fails to provide any context about what the output looks like, when to use this variant, or any caveats, making it inadequate for an AI agent to make informed decisions.

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 has 100% coverage for all 10 parameters with detailed descriptions. The tool description adds no extra parameter context, but the baseline of 3 applies because the schema is already comprehensive.

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?

The description clearly states the tool's purpose as photorealistic image generation and distinguishes it as a high-quality variant of FLUX. However, it is not explicit about the text-to-image functionality, though that is implied by the required prompt parameter.

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 provides no guidance on when to use this tool versus the sibling tools like black_forest_labs_flux_dev or ideogram_v3. It merely claims highest quality photorealistic generation, which implies a use case but offers no alternatives or exclusions.

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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TDQS

B3.2/5.0

Disambiguation2/5

Six of eight tools are for image generation, with overlapping capabilities and only subtle differentiators (speed, quality, text rendering). An agent cannot easily determine which model to use for a generic image generation request, leading to high ambiguity.

Naming Consistency5/5

All tool names follow the same snake_case pattern using provider_model format (e.g., black_forest_labs_flux_pro, stability_ai_sdxl). The convention is uniform across the entire set, making it predictable and easy to parse.

Tool Count4/5

The count of 8 tools is within the ideal range for a media studio, but the heavy redundancy of image generation models makes the set slightly bloated. Still, the overall scope is clear and manageable.

Completeness4/5

The tool surface covers the primary media generation modalities—image, video, and text-to-speech—which aligns with the 'Media Studio' purpose. Minor gaps exist (e.g., no image editing or audio-to-text), but core generation workflows are well covered.

Resources

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