createImage
Creates an image given a prompt.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| createImageBody | Yes | ||
| x-hapi-auth-state | No |
Creates an image given a prompt.
| Name | Required | Description | Default |
|---|---|---|---|
| createImageBody | Yes | ||
| x-hapi-auth-state | No |
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 states the basic action and does not mention the output format, whether the operation is long-running, potential rate limits, cost implications, or any other behavioral traits beyond the literal action. This is a significant gap for a tool that likely generates network-based results.
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 a single, concise sentence with no filler or redundancy. It is appropriately front-loaded and every word adds value, though its brevity is a trade-off against comprehensiveness.
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 tool's complexity (nested object with 5 parameters), absence of annotations, and lack of an output schema, the description is insufficient. It does not explain what the endpoint returns, how it behaves with different parameter values, or how it differs from the sibling image-editing and variation tools. The one-line description leaves too much to inference.
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 top-level parameters 'createImageBody' and 'x-hapi-auth-state' lack schema descriptions, and the tool description only references 'prompt' without covering other parameters like 'n', 'size', or 'response_format'. Although the nested schema provides descriptions for these fields, the description itself does not compensate for the low schema coverage at the top level, failing to clarify how the body should be used or what defaults apply.
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 function with a specific verb ('creates') and resource ('an image'), and mentions the key input ('prompt'). However, it does not differentiate between 'createImage' and the sibling tools 'createImageEdit' and 'createImageVariation', which also create images but in different modes, so it misses the opportunity to fully distinguish itself.
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?
No guidance is provided on when to use this tool versus alternatives. The sibling tools 'createImageEdit' and 'createImageVariation' suggest related but distinct use cases, yet the description gives no exclusions or contextual hints. The phrase 'given a prompt' only weakly implies the tool is for generating an image from scratch.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action: image creation vs. editing vs. variation, audio transcription vs. translation, moderation, and model management. Even the closely related image tools are clearly differentiated by their function (generate, edit, vary).
All tool names follow a consistent camelCase verb_noun pattern: create*, deleteModel, listModels, retrieveModel. The verbs are specific to the action (create, delete, list, retrieve) and the nouns correspond to the target resource.
The 9 tools are well-scoped for the domains they cover: image generation, image manipulation, audio processing, moderation, and model management. Each tool serves a distinct purpose without unnecessary overlap or bloat.
The tool set lacks the core OpenAI functionality of text generation (e.g., chat/completions or completions). This is a major gap for a server named 'OpenAI Tools', as agents cannot perform basic language model requests. The image, audio, and moderation tools are reasonably complete, but the overall surface is severely incomplete.