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

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

Given that annotations already declare readOnlyHint and idempotentHint, the description adds useful behavioral context: it explains that the output includes the ranking, delta to the next model, and whether AIC/AICc/BIC agree, and that the seed fixes the engine's search. This goes beyond the annotations but is not exhaustive (e.g., no mention of error handling or performance), making a 4 appropriate.

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 appropriately sized with a clear opening statement followed by a concise, well-organized Args list. Every sentence adds value: the first sentence states the purpose, the second explains what makes the output meaningful, and the Args section describes parameters without verbosity. It is front-loaded and efficient.

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?

The tool has an output schema, so the description does not need to detail return values, yet it still provides key context about the output (ranking, delta, AIC/BIC agreement). With 5 parameters and one required, the description covers all necessary aspects including sequence_type comparability and the role of the seed. It is complete for an AI agent to select and invoke correctly.

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?

The schema has zero description coverage (0%), so the description must fully compensate. It does this effectively by listing every parameter and adding meaning: it explains what fasta is, defines the criterion values with a note about BIC, states that seed fixes the search, clarifies top_n as the number of ranked models, and elaborates on sequence_type with the comparability caveat. This fully covers the schema's gaps.

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 opens with a specific verb phrase, 'Compare substitution models and report how much the winner won by,' which clearly states the tool's function and output. It distinguishes itself from sibling tools like infer_tree and compare_trees by focusing on substitution model ranking rather than tree inference or comparison.

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 provides clear context for when to use the tool (when comparing substitution models) and includes a usage-relevant caveat: nucleotide and protein models are not comparable, so sequence_type must be chosen appropriately. However, it does not explicitly mention alternative tools or define when NOT to use this tool, so it scores a 4 rather than a 5.

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