createTranscription
Transcribes audio into the input language.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| x-hapi-auth-state | No | ||
| createTranscriptionBody | Yes |
Transcribes audio into the input language.
| Name | Required | Description | Default |
|---|---|---|---|
| x-hapi-auth-state | No | ||
| createTranscriptionBody | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It merely states 'Transcribes audio into the input language' and fails to mention return format, permissions, limitations, or side effects. This is a significant gap for a tool with no annotation safety hints.
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, focused sentence with no redundancy, front-loading the core action. It is concise and efficiently states the purpose, though it may be too terse given the tool's complexity.
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 tool has a nested schema with multiple parameters, no annotations, and no output schema, yet the description is only one sentence. It omits essential context like supported audio formats, output formats, model options, and when to use this tool, making it incomplete for correct invocation.
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?
Schema description coverage is 0%, so the description must compensate, but it does not explain any parameters. It only loosely hints at 'audio' and 'input language' which relate to the file and language parameters, but provides no details on file formats, model ID, response_format, or other inputs.
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 identifies the action 'Transcribes audio' and the resource (audio), with 'into the input language' indicating the output is in the audio's original language. However, it does not explicitly distinguish this from the sibling tool createTranslation, leaving some ambiguity for an agent choosing between them.
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 provides no guidance on when to use this tool versus alternatives like createTranslation, nor does it mention exclusions, prerequisites, or appropriate contexts. It only states what the tool does, offering no usage direction.
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.