Train Dpo

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

With no annotations provided, the description carries the full burden. It does disclose that the operation spends credits, which is useful behavioral context, but omits other important traits such as expected duration, async/background behavior, idempotency, or resource requirements.

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?

Two short sentences, front-loaded with the core action and data requirement, with no wasted words. The credit warning is also concise and relevant.

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?

For a complex DPO training tool with a large nested input schema and no annotations, the description is far too sparse. An output schema exists so return values need not be explained, but the agent gets almost no help constructing the required request object.

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 0% across a deeply nested request object with dozens of fields. The description only implies the dataset should contain chosen/rejected pairs and gives no guidance on model, dataset type, training hyperparameters, checkpointing, or background mode.

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 names a specific verb (Train) and method (DPO) with the required data shape (chosen/rejected pairs), making the core operation clear. It does not explicitly differentiate itself from siblings like train_sft or train_rl, so it falls short of a 5.

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 phrase 'Train on chosen/rejected pairs' implies the data precondition for using this tool, and 'Spends credits' gives a practical cost warning. However, it never states when to prefer this over train_sft, train_rl, or train_distill, nor any exclusions.

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