Skip to main content
Glama

Check an RVC training job

get_training_job
Read-only

Use this to check truthful stage, completed epochs, ETA, and artifact readiness for one private NiceVois training job. Poll reasonably; do not call more often than every 15 seconds.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
linksYes
epochsNo
statusYes
uploadYes
etaTextNo
messageNo
artifactsYes
createdAtNo
expiresAtNo
modelNameNo
startedAtNo
updatedAtNo
completedAtNo
currentEpochNo
durationSecondsNo
estimatedCompletionAtNo
estimatedRemainingSecondsNo

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by explicitly stating it is for a 'private' training job and that the tool is meant for polling (implying it is non-mutating and safe to call repeatedly, with a rate hint). No contradictions found.

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 with zero waste. All information is relevant and front-loaded: first sentence states the tool's purpose and scope, second adds a crucial usage constraint. Every word 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?

Given it has an output schema (so return structure is documented), only 1 required parameter with clear purpose, and annotations already cover safety, the description covers all necessary ground. It explains what the tool does, how to use it (polling with rate limit), and distinguishes it from siblings. No gaps remain.

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?

Schema coverage is 0%, meaning the description must compensate. It does not describe the 'jobId' parameter explicitly, but the tool's purpose ('check one training job') strongly implies jobId identifies which job. With only one required parameter, the context is sufficient. A brief clarification of what jobId looks like would merit a 5.

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 ('check'), states a clear resource ('one private NiceVois training job'), and explicitly lists what it retrieves ('stage, completed epochs, ETA, and artifact readiness'). This effectively distinguishes it from sibling tools like create_training_job or list_training_jobs.

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 explicitly tells when to use this tool ('to check one training job') and provides proactive usage guidance ('poll reasonably; do not call more often than every 15 seconds'). This helps the agent avoid misuse or excessive calls.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources