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musharna
by musharna

Infer a phylogenetic tree with bootstrap support

infer_tree
Read-onlyIdempotent

Build a maximum-likelihood tree and measure branch support via bootstrapping. Input aligned FASTA sequences and choose a substitution model.

Instructions

Build a maximum-likelihood tree and measure how well the data support it.

Always bootstraps. There is deliberately no option to skip it: an unsupported topology is the failure mode this server exists to prevent.

Args: fasta: Aligned nucleotide sequences in FASTA. All sequences must be the same length — this server does not align. model: Substitution model, e.g. "JC", "HKY", "GTR+G". Run select_model first if you do not have a reason to prefer one. replicates: Bootstrap replicates (20-1000). Cost is roughly linear in this, so 100 is a reasonable default and 1000 is for a final answer. seed: Fixes both the resampling and the engine's search. sequence_type: "dna" (default) or "protein". DECLARED, never sniffed: an alignment of only A/C/G/T is a valid protein alignment too, so guessing would silently fit a nucleotide model to protein data. A protein alignment also needs a protein model — "LG+G" or "WAG", not the nucleotide default — so run select_substitution_model with the same sequence_type first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
fastaYes
modelNoGTR+G
replicatesNo
sequence_typeNodna

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
engineYes
newickYes
supportYes
warningsYes
alignmentYes
log_likelihoodYes
reproducibilityYes
branch_length_unitsYes
newick_with_supportYes
Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations (readOnlyHint=true, idempotentHint=true): it always bootstraps with no option to skip, cost scales linearly with replicates, seed fixes both resampling and engine search, and sequence_type is 'DECLARED, never sniffed' to avoid silent model mismatches. These are not captured by the annotations and provide valuable transparency.

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

Conciseness5/5

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

The description is front-loaded with the core purpose, then uses a clear Args list for parameter details. Every sentence earns its place—even the 'DECLARED, never sniffed' note is relevant. It is appropriately sized for a tool with 5 parameters and nuanced bioinformatics caveats, with no filler.

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

Completeness5/5

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

Given the tool's complexity and that an output schema exists (so return values need no explanation), the description covers all critical context: prerequisites (aligned sequences), model selection guidance, bootstrap behavior, cost implications, and cross-tool dependencies (select_substitution_model). It is fully sufficient for an agent to understand when and how to invoke the tool.

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?

Schema description coverage is 0%, but the description compensates fully by explaining every parameter: fasta must be aligned and same-length, model examples and recommendation, replicates range and cost, seed's dual role, and sequence_type with a warning about protein alignments needing protein models. This is essential meaning that the schema alone lacks.

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 opens with 'Build a maximum-likelihood tree and measure how well the data support it,' which is a specific verb+resource statement that clearly differs from sibling tools like select_substitution_model (model selection) and compare_trees (tree comparison). The title reinforces the purpose, and the description explicitly notes the tool always bootstraps, making it the right choice for supported phylogenetic inference.

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?

The description provides explicit when-to-use guidance: 'Run `select_model` first if you do not have a reason to prefer one' and 'run select_substitution_model with the same sequence_type first' for protein data. It also states a key prerequisite: 'this server does not align,' implying the tool should not be used with unaligned sequences. These are clear usage directives and alternatives.

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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