simulate_tree

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It explains that the tree is stored in obst[key_added] and is a simulation, but does not discuss side effects (e.g., overwriting), permissions, error conditions, or the fact that it creates a new dataset.

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 well-structured, starting with a clear purpose, then detailing methods and output, and ending with an arg list. It is concise but includes necessary examples. The arg list is slightly redundant with the schema but acceptable.

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 tool complexity (three parameters, two methods, output schema exists), the description covers purpose, methods, parameters, and the follow-up workflow. It does not detail return values but the output schema fills that gap. Minor omission: no mention of how to retrieve the stored tree.

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?

With 0% schema description coverage, the description effectively explains the meaning of all three parameters: method options with examples, key_added as the storage key, and extra_options as keyword arguments with sample inputs. This adds significant value beyond the bare 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 clearly states the tool simulates a ground-truth tree topology and registers it as a new dataset, listing two specific methods with examples. It distinguishes itself from sibling tools like simulate_characters by stating that the tree is stored and can be followed by character simulation.

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 implies usage for generating synthetic trees and hints at a workflow with simulate_characters, but does not explicitly state when to use this tool versus alternatives such as import_character_matrix or reconstruct_tree. No exclusions or caveats are provided.

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