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List models, pricing, and labels

oruk_list_models
Read-only

List 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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TDQS

A4.4/5.0
Disambiguation5/5

The three audio-processing tools are sharply differentiated: transcribe_audio is words-only, analyze_tone is tone-only, and analyze_speech combines both. The supporting tools are also distinct in purpose, and the descriptions cross-reference each other to prevent misselection.

Naming Consistency5/5

All tool names follow a consistent oruk_ + verb + noun snake_case pattern, such as analyze_speech, list_models, and create_trial_key. The only mild exception is get_started, but it still clearly follows the verb-led style and does not create confusion.

Tool Count5/5

Seven tools is well-scoped for a speech analysis API MCP server: three core audio-processing tools plus four supporting tools for setup, keys, credits, and model information. Each tool has a clear purpose and none feel redundant or extraneous.

Completeness5/5

The tool surface covers the full user journey for the stated domain: getting started, obtaining a trial key, checking credits, listing models, transcribing audio, analyzing tone, and doing combined transcription plus tone analysis. There are no obvious dead ends or missing core operations for this API's scope.

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