Rank substitution models, with the margin over the runners-up

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

Annotations already indicate the tool is read-only and idempotent. The description adds behavioral context: it ranks models, reports the winner's margin, and explains that BIC penalizes parameters more heavily. It also notes that seed fixes search, which is useful.

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 structured with a clear paragraph summarizing output and an Args list. It is concise but could be slightly tighter; the first paragraph is efficient however.

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 existence of an output schema (not shown), the description provides adequate context for a straightforward tool. It covers purpose, result interpretation, and all parameters. Minor gaps remain about output format but are covered by the output schema.

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 Args section fully compensates by describing each parameter: fasta (aligned nucleotide sequences), criterion (AIC/AICc/BIC with BIC penalizing more), seed (fixes search), top_n (number of models). This adds significant meaning beyond the 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 title and description clearly state that the tool ranks substitution models and reports the winner's margin. The verb 'compare' and 'rank' combined with the resource 'substitution models' uniquely identifies the tool. It is distinct from sibling tools 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 implies the tool is used for model selection in phylogenetics but does not explicitly state when to use it versus alternatives. No guidance on prerequisites or exclusions is provided.

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