xpay✦ Media Studio

ideogram_v3

Ideogram v3 Quality — AI image generation with best-in-class text rendering

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mask`	No	A black and white image. Black pixels are inpainted, white pixels are preserved. The mask will be resized to match the image size.
`seed`	No	Random seed. Set for reproducible generation
`image`	No	An image file to use for inpainting. You must also use a mask.
`prompt`	Yes	Text prompt for image generation
`resolution`	No	Resolution. Overrides aspect ratio. Ignored if an inpainting image is given.	None
`style_type`	No	The styles help define the specific aesthetic of the image you want to generate.	None
`aspect_ratio`	No	Aspect ratio. Ignored if a resolution or inpainting image is given.	1:1
`style_preset`	No	Apply a predefined artistic style to the generated image (V3 models only).	None
`magic_prompt_option`	No	Magic Prompt will interpret your prompt and optimize it to maximize variety and quality of the images generated. You can also use it to write prompts in different languages.	Auto
`style_reference_images`	No	A list of images to use as style references.

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 must disclose behavioral traits but only offers a marketing claim about text rendering. It omits key behaviors like inpainting support, style presets, aspect ratio handling, and magic prompt features, which are significant capabilities.

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, which is concise and front-loaded, but the content is mostly a tagline. The 'best-in-class text rendering' phrase is not operational guidance, so the description under-delivers for the tool's complexity, though it is not as sparse as a single verb.

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?

For a tool with 10 parameters and no output schema, the description is notably incomplete. It does not mention the inpainting/editing capabilities, style options, or other configurable aspects, leaving the agent without a complete picture of the tool's functionality.

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 schema covers 100% of parameters with descriptions, so the baseline is 3. The tool description adds no parameter-specific detail—'best-in-class text rendering' does not clarify any parameter usage—so it remains at baseline.

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 it is an AI image generation tool, with a specific focus on text rendering. It distinguishes itself from some siblings by highlighting 'best-in-class text rendering', though it could more explicitly differentiate from other image models like Flux or Recraft.

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?

No guidance is provided on when to use this tool versus alternatives. The description merely says 'AI image generation' without indicating specific use cases, such as text-heavy imagery or inpainting scenarios, leaving the agent without decision-support.

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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