createTranslation
Translates audio into English.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| x-hapi-auth-state | No | ||
| createTranslationBody | Yes |
Translates audio into English.
| Name | Required | Description | Default |
|---|---|---|---|
| x-hapi-auth-state | No | ||
| createTranslationBody | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but it only says 'translates audio into English'—no mention of supported languages, output formats, or processing limitations. It adds minimal behavioral context beyond the name.
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 (one short sentence) and front-loaded, but it's under-specified for a tool with 5 parameters and a nested object. It's not fluff, but it's too sparse to be considered well-structured.
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?
The description is severely incomplete: no mention of output schema, return format, or when to use translation vs transcription. Given the tool's complexity (nested body, multiple formats), this is inadequate.
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 description provides no parameter-level information. Schema coverage is 0%, and while the schema itself has detailed parameter descriptions, the tool description does not compensate or highlight which parameters matter most (e.g., file, model).
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 states a specific verb ('Translates') and resource ('audio') with a clear target ('into English'), which distinguishes it from sibling createTranscription (which presumably transcribes without translating). However, it omits that the output is text/transcript, so it's not fully explicit.
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 given on when to use this tool vs alternatives, e.g., when audio is in a non-English language and English output is needed. It does not mention exclusions or prerequisites.
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.