xpay✦ Media Studio

black_forest_labs_flux_dev

FLUX Dev — balanced quality and speed for image generation

Input Schema

TableJSON Schema

Name	Required	Description	Default
`seed`	No	Random seed. Set for reproducible generation
`image`	No	Input image for image to image mode. The aspect ratio of your output will match this image
`prompt`	Yes	Prompt for generated image
`go_fast`	No	Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16. Note that outputs will not be deterministic when this is enabled, even if you set a seed.
`guidance`	No	Guidance for generated image
`megapixels`	No	Approximate number of megapixels for generated image	1
`num_outputs`	No	Number of outputs to generate
`aspect_ratio`	No	Aspect ratio for the generated image	1:1
`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
`prompt_strength`	No	Prompt strength when using img2img. 1.0 corresponds to full destruction of information in image
`num_inference_steps`	No	Number of denoising steps. Recommended range is 28-50, and lower number of steps produce lower quality outputs, faster.
`disable_safety_checker`	No	Disable safety checker for generated images.

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It offers only a vague assertion about quality/speed balance and provides no information about side effects, safety, determinism, or other behavioral traits. The extensive schema details are not reflected in the description, leaving behavior opaque.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no wasted words, earning 'conciseness' credit. However, it is also under-specified to the point of being a tagline rather than useful documentation. It is not misleading, but it lacks the structure to convey necessary context.

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?

For a tool with 13 parameters, no output schema, and no annotations, the description fails to provide essential context. It does not explain return values, usage prerequisites, or any behavior beyond a vague quality/speed balance. This is completely inadequate for an agent to correctly select and invoke the tool.

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 fully describes all 13 parameters (100% coverage), so the baseline of 3 applies. The description adds no additional parameter semantics, but the schema already provides sufficient detail for an agent to understand each parameter's purpose.

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 identifies the tool as an image generation model and positions it with a quality/speed trade-off. While it lacks an explicit verb like 'generate', the resource and purpose are unambiguous. The mention of 'balanced quality and speed' helps distinguish it from sibling models despite not naming them.

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

Usage Guidelines3/5

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

The phrase 'balanced quality and speed' implicitly suggests a middle-ground use case between faster and higher-quality alternatives, but it does not explicitly state when to choose this tool over siblings like flux_pro or flux_schnell. No exclusions or alternative guidance is provided.

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.

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