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Check an RVC vocal conversion

get_voice_conversion
Read-only

Use this when checking one private NiceVois conversion. Poll no more than once every 10 seconds. Report the returned stage without inventing progress. When complete, present the NiceVois cleaned version first and use download_voice_conversion_output for the version the user requests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversionIdYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
errorNo
stageNo
statusYes
uploadYes
outputsYes
timingsNo
fileNameNo
createdAtNo
modelNameNo
transposeNo
restorationNo
stageMessageNo
providerStateNo

TDQS

A4.6/5.0
Behavior5/5

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

With annotations already marking this as read-only and non-destructive, the description adds meaningful behavior beyond that: it discloses polling cadence, instructs the agent to report the actual returned stage without inventing progress, and prescribes output ordering. These are non-obvious behaviors not available from annotations or schema.

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?

Three short sentences, each earning its place: one sets the use case, one sets polling and accuracy expectations, one prescribes the post-completion action. The most important instruction is front-loaded, and there is no filler.

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?

The task is a simple read/status check with a single parameter. An output schema is present, so return-value documentation is already covered. The description adds all necessary operational guidance: polling limit, honest reporting, and the correct downstream tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage and the prose does not mention conversionId, its origin, or how it relates to create_voice_conversion. Though the tool name and the word 'conversion' hint at the identifier's meaning, the description provides almost no value beyond the schema's property name and constraints.

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 names a specific verb ('check') and resource ('one private NiceVois conversion'), and the title reinforces 'Check an RVC vocal conversion.' It clearly separates this polling/status tool from related creation and download operations, so an agent knows what it does without inspecting the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use context ('Use this when checking') and a hard usage rule ('Poll no more than once every 10 seconds'). It also names the companion tool to use after completion, download_voice_conversion_output, providing a clear alternative and handoff.

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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