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amanharshx
by amanharshx

training_start

Start a cloud training job for your model by providing model, project, dataset, and GPU type; cost confirmation is required.

Instructions

Start a cloud training job (state-changing, may cost credits). Requires confirm_cost=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
batchNo
imgszNo
modelYes
epochsNo
datasetYes
projectYes
gpu_typeYes
confirm_costNo
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It discloses state-changing and potential cost, but lacks details on side effects, reversibility, or specific behaviors that would help an agent understand full implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no unnecessary words. It efficiently conveys the core action and a critical requirement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters, 4 required, and no output schema, the description is too brief. It does not explain the training job lifecycle, what the tool returns, or how to interpret results, leaving significant gaps for an agent.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no meaning beyond the schema for any parameter except 'confirm_cost'. The required and optional parameters (model, dataset, gpu_type, etc.) are not explained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Start a cloud training job') and adds context ('state-changing, may cost credits'), which distinguishes it from monitoring tools. However, it does not explicitly differentiate from all sibling tools.

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

Usage Guidelines3/5

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

The description provides a key requirement ('Requires confirm_cost=true') but no guidance on when to use this tool versus alternatives, such as when to start vs. monitor a training job.

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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