train_submit

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 idempotency (same id resubmission is idempotent) and that it generates a trainJobId, which is useful. However, it does not mention that this will actually launch a training job, incur costs, or require specific permissions. It also does not explain what happens on budget exceedance (though the schema mentions it, the description does not). This is insufficient for a submission tool.

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

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

The description is a single, compact sentence that front-loads the core purpose and key inputs. It is extremely concise with no fluff, effectively communicating the essential function.

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

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

The tool has 7 parameters, nested objects, and many related siblings, yet the description does not explain the response structure, error conditions, or how to use the generated trainJobId. It also does not differentiate it from train_dryrun or train_budget. The absence of an output schema makes the description even more critical, but it leaves significant gaps.

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

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

Schema description coverage is 100%, so the schema fully documents each parameter. The description's summary of inputs adds no extra meaning beyond what is already in the schema. It does not clarify parameter relationships or provide additional context, so a baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: submitting a training task. It enumerates the inputs (data, base model, algorithm, hyperparameters, budget) and the output (trainJobId). This distinguishes it from sibling tools like train_status or train_report, which are about checking or reporting, not submission.

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

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

The description implies usage for training job submission, but it does not explicitly state when to use it versus alternatives like train_dryrun or train_budget. There is no mention of exclusions or conditions. The purpose is clear enough that an agent can infer when to use it, but it lacks explicit routing guidance.

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