Simulate an alignment from a known tree

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

Annotations already declare read-only and idempotent behavior. The description adds useful context beyond that: the output has a known true tree, the seed fixes the simulation, protein models produce sequences whose moltype is recorded, and every newick edge needs a branch length. There is no contradiction with annotations.

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 front-loaded with the core purpose, then gives the practical workflow context, then compactly documents each parameter. There is no filler or repetition of schema defaults; every sentence contributes.

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 simulation tool with an output schema and safety annotations already provided, the description covers the essential usage loop, parameter semantics, and expected result semantics. Nothing an agent needs to call it correctly or interpret its output is missing.

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 carries the full burden, and it succeeds. It explains newick (true tree with branch lengths), model (substitution model and moltype implication), length (number of sites), and seed (fixes simulation) in plain terms that add meaning beyond 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 states a specific verb and resource: 'Generate sequences along a tree you specify, so the true answer is known.' It also orients the tool as the 'positive control for everything else here,' distinguishing it clearly from inference and comparison siblings like infer_tree and compare_trees.

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 gives clear context for when to use the tool: as a positive control for validating inference workflows. It names the follow-up steps (infer_tree, compare_trees) and explains how to interpret failure, but it does not explicitly state when not to use it or name an alternative simulation approach.

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