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

With no annotations, the description must disclose behavior itself. It states that the tool runs iterative UCT search and returns a trajectory, but omits practical traits such as stochasticity, computational expense, stopping conditions, or whether the result is guaranteed optimal (MCTS typically returns an approximation).

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?

One sentence, front-loaded with the algorithm name and objective; no filler. It is appropriately short, though the brevity comes at the cost of content captured by other dimensions.

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 has three optional parameters and an output schema, but with zero annotation coverage and no parameter explanation, an agent cannot confidently know what initial_state/valid_actions should look like or how max_iterations affects behavior. The presence of an output schema helps only with return interpretation.

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 needed to explain initial_state, valid_actions, and max_iterations. It only says 'action sequences' and 'trajectory', which maps loosely to valid_actions but gives no format, default behavior, or iteration semantics.

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?

Description names a specific algorithm (Monte Carlo Tree Search/UCT), a resource (action sequences), and an outcome (optimal trajectory). It is clear and distinguishes from siblings like cognitive.hierarchical_plan, though it does not explicitly name an alternative.

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 mention of when tree_search should be chosen over sibling planning/search tools, nor any exclusions or prerequisites. The only guidance is implicit in the algorithm name, which is not enough to route an agent.

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