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

With no annotations, the description carries the behavioral burden. 'Simulate' conveys that this is a hypothetical, non-executing operation, and the ranking criteria describe what the tool computes. However, it does not disclose side effects, state immutability, determinism, or any operational constraints.

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, front-loads the core action, and contains no filler. Every word contributes to understanding the tool's purpose.

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 tool with three parameters, nested objects, and no annotations, the description is too thin. It does not explain the required state structure, the dt semantics, or when to use this tool, so an agent would struggle to invoke it correctly.

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. It names 'candidate_actions' indirectly and the ranking criteria, but it provides no meaning for 'state' or 'dt', and the candidate_actions items are untyped in the schema. This leaves substantial ambiguity in how to construct valid inputs.

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 specific verb and resource: 'Simulate and rank candidate actions' with explicit ranking criteria (feasibility, reward, constraint safety). This is clear and identifiable, though it does not explicitly differentiate itself from sibling tools like tree_search or predict_world_state.

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 about when to use this tool versus alternatives. The description implies it is for comparing candidate actions, but it does not state when to prefer it over cognitive.tree_search or cognitive.predict_world_state, nor does it give exclusions.

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