wm_train_step

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

With no annotations, the description must disclose behavioral traits. It only says 'training step', not whether it modifies model state, is safe, or has side effects. The action parameter hints at neuro-inspired injection, but behavior is not explained.

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 extremely short (one sentence), but this brevity omits critical information. It is not that conciseness is achieved; rather, it is under-specified.

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?

Given the nested parameter object and lack of output schema or annotations, the description is grossly incomplete. It does not explain return values, training process, or side effects, leaving the agent without essential context.

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?

The input schema has 100% coverage with descriptions for all parameters (e.g., 'Action applied between observations'). The description adds no additional semantic context, meeting the baseline of 3.

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 'Online World Model Training Step' indicates this tool is for training a world model, which distinguishes it from inference tools like wm_predict or wm_encode. However, it does not specify what a 'training step' entails (e.g., weight update, gradient computation), leaving the purpose vague.

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?

No guidance is provided on when to use this tool, prerequisites, or alternatives. The description neither states when training steps should be invoked nor contrasts with sibling tools like wm_encode or wm_plan.

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