Generate an image from a text prompt. Use whenever the user wants a picture/illustration/photo created.
Args:
prompt: description of the image (English works best).
size: 1024x1024 (square) / 1024x1536 (portrait) / 1536x1024 (landscape).
Returns the generated PNG image.
| Name | Required | Description | Default |
|---|---|---|---|
| size | No | 1024x1024 | |
| prompt | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It discloses that the tool 'Returns the generated PNG image,' which is a behavioral trait. However, it does not mention other behaviors such as whether the operation is synchronous, any API key requirements, rate limits, or potential side effects. For a simple image generation tool, the return type and size options provide some transparency, but not a full picture.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a single-purpose first sentence, followed by a compact args list. Every sentence earns its place with no extraneous detail. The format is easy to scan and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (2 params, 1 required) and absence of an output schema, the description covers the essentials: purpose, parameter semantics, and return format. It does not address edge cases or failures, but for a simple generation tool this is reasonable and not a critical gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description fully compensates. It explains 'prompt' as 'description of the image' and adds the tip that 'English works best.' For 'size,' it enumerates the exact allowed values and their orientation meanings (square, portrait, landscape), which is not present in the schema. This significantly helps the agent select appropriate parameter values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's verb ('Generate') and resource ('an image from a text prompt'), which distinguishes it from sibling tools like generate_video and text_to_speech. The phrase 'picture/illustration/photo' further clarifies the scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'Use whenever the user wants a picture/illustration/photo created.' This is clear positive guidance. It does not explicitly mention exclusions or alternatives, but the context of sibling tools implies that for non-image generation, other tools are appropriate.
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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Each tool maps to a distinct modality or lifecycle step: chat, image generation, video generation, video status polling, model listing, and speech synthesis. The async video pair (generate_video/get_video_status) is clearly separated, and no two tools plausibly do the same thing.
Most tools follow a clear verb_noun pattern: generate_image, generate_video, get_video_status, list_models. The deviations are minor: 'chat' is a bare verb and 'text_to_speech' uses a noun_phrase style instead of generate_speech or synthesize_speech, but the overall naming is still predictable.
Six tools is well-scoped for a multimodal AI gateway covering chat, image, video, speech, and model discovery. Each tool earns its place and the count is comfortably within the ideal range.
The core workflows are covered: chat has a direct call, video generation has an async create-and-poll lifecycle, image and speech have generation entry points, and list_models aids discovery. Minor gaps exist—no image-to-video, speech status polling, or model selection for image/video/speech—but agents can accomplish the primary stated purposes.