xpay✦ Media Studio

black_forest_labs_flux_schnell

FLUX Schnell — fastest image generation, 1-4 steps

Input Schema

TableJSON Schema

Name	Required	Description	Default
`seed`	No	Random seed. Set for reproducible generation
`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.
`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
`num_inference_steps`	No	Number of denoising steps. 4 is recommended, and lower number of steps produce lower quality outputs, faster.
`disable_safety_checker`	No	Disable safety checker for generated images.

TDQS

B3.2/5.0

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 only mentions speed and step count, but does not disclose non-determinism (e.g., go_fast's fp8 randomness), safety checker behavior, output details, or any limitations. This is insufficient for a generation tool.

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 a single concise phrase with no wasted words. It front-loads the key information (model name, function, speed advantage) and is appropriately structured as a tagline.

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

Completeness2/5

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 annotations, no output schema), the one-line description is inadequate. It does not explain return values, mention the output as an image, hint at parameter nuances like safety checker or reproducibility, or provide context for how this model compares to siblings beyond speed. The description leaves many operational aspects unaddressed.

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 covers all 10 parameters with detailed descriptions (100% coverage), so the baseline is 3. The description adds no parameter-specific meaning beyond what the schema already provides.

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 generates images ('image generation') and identifies the specific model variant (FLUX Schnell) with a distinguishing attribute ('fastest'). It implies a text-to-image function and differentiates from siblings by emphasizing speed, though it does not explicitly mention 'generate from prompt'.

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 description implies use when speed is a priority ('fastest image generation, 1-4 steps'), but provides no explicit when-to-use vs. alternatives, no exclusions, and no mention of quality trade-offs or use cases. It is an implied usage hint rather than clear guidance.

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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