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 mentions 'uncertainty bounds' and 'trajectories', which implies a probabilistic or ensemble output, but it does not explain whether the model must be already trained, whether it mutates state, or whether it requires a specific format for 'state' or 'actions'. The output schema exists, but the description leaves key behavioral aspects (e.g., determinism, need for prior learning) unclear.

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, concise sentence that is front-loaded with the core concept ('Forward world model') and immediately states the output ('future state trajectories and uncertainty bounds'). It avoids fluff and is readable. However, it leaves out critical details, but that is a completeness issue, not a conciseness issue.

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 likely requires a learned world model (implied by 'Forward world model' and sibling 'learn_world_model'), but this prerequisite is not stated. The description does not explain the expected format of `state` and `actions`, nor the role of `dt` and `timescale`. Even with an output schema, the agent may not know whether to call this tool for a given task, and the lack of usage guidelines makes it incomplete for a complex prediction tool.

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 coverage is 0%, so the description must compensate for the lack of parameter documentation. It mentions 'actions' as input but does not define its structure (e.g., list of action objects, types). It also mentions 'state' but not its required fields. The parameters 'dt' and 'timescale' are not explained in the description, leaving the agent to guess their semantics. The description adds minimal value beyond the parameter names.

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 states a clear purpose: to predict future state trajectories and uncertainty bounds under given actions. It uses a specific verb ('predict') and resource ('world state'). However, it does not explicitly differentiate from sibling tools like cognitive.simulate_actions or cognitive.counterfactual_what_if, which may also involve prediction, but the phrase 'forward world model' and 'trajectories' suggests a unique focus.

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 provides no guidance on when to use this tool versus alternatives. It does not state prerequisites, such as whether a learned world model is required (hinting at a relationship with cognitive.learn_world_model), nor does it mention exclusions like when simulation is more appropriate than prediction. The agent must infer usage from the tool name and sibling list.

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