oruk_list_modelsList oruk’s speech models with lifecycle and per-task pricing (USD per audio minute), the five API tasks, the 15 emotion and 16 speaking-style labels, and audio limits. No API key required. Use this to choose a model, estimate cost before analyzing long audio, or see which labels exist.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds useful behavioral context beyond the annotations by stating that no API key is required and by detailing the contents (lifecycle, pricing per audio minute, tasks, labels, audio limits). This gives the agent a clear picture of what the tool returns and its accessibility, though it doesn't describe the exact response format.
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 two sentences, front-loads the main action, and every clause adds value: what is listed, pricing details, auth requirement, and use cases. No filler or redundancy.
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?
Even though there is no output schema, the description covers purpose, content, use cases, and authentication status. For a zero-parameter list-oriented tool with readOnlyHint true and no destructive behavior, this is fully complete for an agent to select and invoke it appropriately.
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 tool has zero parameters, so the baseline is 4. The description doesn't need to explain parameter semantics, and the empty schema is fully consistent with the description's 'no input needed' implication.
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 uses a specific verb ('List') with a clear resource ('oruk's speech models') and enumerates the exact scope: lifecycle, per-task pricing, tasks, labels, and audio limits. This clearly distinguishes it from sibling tools like oruk_analyze_speech or oruk_transcribe_audio, which perform different actions.
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 explicit use cases: 'choose a model, estimate cost before analyzing long audio, or see which labels exist.' It also notes 'No API key required,' which helps with pre-auth usage. However, it doesn't explicitly mention when not to use it or name alternative tools, so it stops short of a full when/when-not guide.
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 audio tool has a clearly distinct mode—words only, tone only, or both—and the descriptions explicitly cross-reference which tool to use for each need. The support tools (credits, trial keys, model listing, onboarding) are also cleanly separated and unlikely to be confused.
Tools consistently use an oruk_<verb>_<object> pattern, such as oruk_transcribe_audio, oruk_list_models, and oruk_check_credits. oruk_get_started is the one mild deviation, using an idiomatic verb phrase rather than a clear object, but it remains recognizable and does not harm usability.
Seven tools is well-scoped for this server: three core speech capabilities, plus four practical setup/billing/configuration utilities. Every tool serves a distinct and justified purpose with no missing or redundant surface.
The core speech workflow is fully covered—transcription alone, tone analysis alone, and combined transcript-plus-tone analysis—along with setup, key management, credit checking, and model selection. There are no obvious dead ends in using the API from first setup to running analyses.