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Check NiceVois training balance

get_training_account
Read-only

Call before quoting or creating a training job. Returns the connected account's welcome-credit status, available and reserved balance, balance packs, and a private browser link that keeps checkout on this same agent identity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
balanceYes
freeRunsYes
productsNo
signedInNo
accountUrlNo
freeTrainingNo
checkoutEnabledYes
freeRunMaxEpochsNo
freeRunMaxDurationSecondsNo

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond that: it explains the account-scoped nature of the call and the private browser link that preserves agent identity through checkout, which is a meaningful side effect an agent needs to know about.

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?

Two sentences with no filler: the usage precondition is front-loaded, and the returned data is enumerated without redundancy. Every clause earns its place.

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?

With an output schema present, return-field details are already structured. The description covers when to invoke it, what to expect, and the identity-preserving checkout-link behavior. Nothing an agent needs to call it correctly is missing.

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 there is no parameter syntax to document. The description clarifies that it operates on the connected account's identity, which is useful context and merits the baseline 4 for a parameterless tool.

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 states a specific purpose: a preflight check of the connected account's training balance and credits, returning welcome-credit status, available/reserved balance, balance packs, and a checkout link. It clearly distinguishes this account-level balance tool from siblings like get_training_job, which operate on individual jobs.

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?

It explicitly tells the agent when to call this tool — before quoting or creating a training job — which is clear, actionable guidance. However, it does not name alternatives or state when not to use it (e.g., when checking an existing job's status).

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource-action pair (training vs image training vs conversion vs requirements/quote/start/download). The consistent 'training' vs 'image_training' qualifiers prevent overlap between the two domains.

Naming Consistency5/5

All tools follow a clear verb_noun snake_case pattern (create_, get_, list_, quote_, start_, download_). The parallel naming for voice training and image training (e.g., create_training_job vs create_image_training_job) is predictable and consistent.

Tool Count4/5

18 tools is slightly above the typical 3-15 range, but each tool serves a necessary step in the training/conversion workflows (requirements, quote, create, start, poll, download). The count is justified by the server covering both voice and image training plus conversion.

Completeness4/5

The full lifecycle for training and conversion is covered: requirements gathering, quoting, job creation, upload, start, status checks, and output download. Minor gaps exist, such as no cancel/delete job operations and no dedicated list for image training jobs, but these are workable.

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