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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool is a forward model and that uncertainty bounds are part of the output, but it does not disclose side effects, reliance on a trained model, determinism, failure modes, or limitations. The safe, read-only nature is only inferred from the verb 'predict'.

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 focused sentence with no filler. The core concept ('forward world model') is front-loaded, and the operational detail ('predict future state trajectories and uncertainty bounds under actions') follows directly. Every phrase earns its place.

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 absence of annotations, low schema coverage, multiple plausible sibling tools, and nested/opaque parameters, this description is not complete enough. It does not explain how the world model is obtained, what state/action formats are expected, or what the uncertainty bounds represent. The presence of an output schema does not excuse the missing parameter and usage 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?

Schema description coverage is 0%, so the description must compensate by explaining parameters. It only vaguely relates 'actions' to the prediction context; it does not define the 'state' object, the meaning of 'dt', or the 'timescale' values. The description adds minimal semantic value over the raw schema.

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 operation ('predict future state trajectories and uncertainty bounds') and a resource ('world model'), so an agent understands the core function. It does not explicitly contrast with siblings like cognitive.simulate_actions, but 'forward world model' and 'uncertainty bounds' signal a distinct predictive behavior.

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?

There is no guidance on when to use this tool versus alternatives such as cognitive.simulate_actions, cognitive.counterfactual_what_if, or cognitive.learn_world_model. The description implies a predictive use case but does not state conditions, prerequisites, or exclusions.

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