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musharna
by musharna

Simulate an alignment from a known tree

simulate_alignment
Read-onlyIdempotent

Generate synthetic sequence alignments with a known true tree to test and validate phylogenetic inference methods.

Instructions

Generate sequences along a tree you specify, so the true answer is known.

This is the positive control for everything else here: infer a tree from the output and compare it back with compare_trees. If inference cannot recover a topology you generated from, the problem is the data or the settings, not the biology.

Args: newick: The true tree, with branch lengths. model: Substitution model to simulate under. length: Number of sites. seed: Fixes the simulation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
modelNoJC
lengthNo
newickYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedYes
fastaYes
modelYes
warningsYes
alignmentYes
true_newickYes
Behavior4/5

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

Annotations already declare idempotentHint=true and readOnlyHint=true, and the description adds context about being a simulation with known truth, without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise at about 6 sentences, front-loaded with purpose, but the Args listing as a paragraph could be more structured; still clear and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description fully covers usage, parameters, and purpose. It does not mention the exact output format but that is handled by the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema coverage, the description explains each parameter (newick, model, length, seed) with meaningful context beyond name/type, such as 'with branch lengths' for newick and 'fixes the simulation' for seed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'generate sequences along a tree you specify' with a specific verb and resource, and distinguishes from siblings like infer_tree and compare_trees by framing it as a positive control.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly positions this tool as a positive control, suggests a workflow with infer_tree and compare_trees, and explains how to interpret results if inference fails.

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

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