Skip to main content
Glama

Generate Image

imageat_generate_image

Turn text prompts into images, or edit existing images using reference photos, and receive CDN URLs for instant use.

Instructions

Generate an image from a text prompt (text-to-image), optionally with reference images for image-to-image editing. Returns CDN image URL(s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel id. One of: "nano-banana-pro" (default), "nano-banana-2", "nano-banana", "gpt-image-2" (OpenAI GPT Image 2), "seedream-5.0-lite", "krea-2-large", "krea-2-medium". Use these exact ids — do not invent variants like gpt-image-2.0.
imagesNoReference images as base64 data URLs or http(s) URLs for image-to-image.
promptYesWhat to generate.
numImagesNo
resolutionNo
aspectRatioNoe.g. "1:1", "16:9", "9:16", "4:5".
outputFormatNo
enableWebSearchNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing behavior, and it does reveal the return type: 'Returns CDN image URL(s).' It also clarifies that reference images enable image-to-image editing. However, it does not mention whether generation is asynchronous, consumes credits, has content policy limits, or what happens if generation fails.

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?

Two sentences with no filler: the first states the core operation and optional mode, the second states the return behavior. The most important information is front-loaded and every sentence earns its place.

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?

For a simple generation call with only 'prompt' required, the description provides enough to get started and specifies the return format. However, it lacks guidance on sibling-tool selection, default behaviors, and API-specific constraints such as credit usage or async behavior, leaving some operational gaps for an agent.

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 description adds some conceptual meaning by framing 'prompt' as the text-to-image input and 'images' as reference inputs for image-to-image editing, which maps to the schema. With 8 parameters and only 50% schema description coverage, it does not compensate for undocumented parameters like numImages limits, resolution defaults, or enableWebSearch semantics.

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 states the specific action ('Generate an image from a text prompt') and resource (text-to-image, with optional reference images for image-to-image editing), which clearly exceeds a tautology. It differentiates from imageat_generate_video, but the overlap with imageat_edit_image is not explicitly addressed.

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 gives no guidance on when to use this tool versus alternatives such as imageat_edit_image. It does not state conditions, exclusions, or the preferred tool for pure editing vs. generation with reference images, leaving the choice to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.