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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: the comparison is against the genome's own usage (not a standard table), and the false discovery rate is controlled across genes, which materially affects how results should be read. It stops short of describing output shape, but an output schema exists to cover that.

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?

A single dense sentence, front-loaded with the operation and ending on the statistical guarantee. Every clause carries information, though the middle relative clause ('whose synonymous codon choice that average does not explain') is slightly tangled and could be split for readability.

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?

With 10 parameters, nested record objects, full schema coverage, an output schema and annotations, the description needs only to establish the analytical model, which it does precisely. Multi-genome handling (pool_files), per-file genetic codes, and short-gene exclusion are left entirely to the schema, which is acceptable given their description quality.

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 100% and the parameter descriptions are unusually rich (alpha, correction, pool_files, min_codons, genetic_code all explain themselves). The tool description adds no parameter-level meaning beyond what the schema already provides, so the baseline of 3 applies.

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?

States a specific verb and resource pair ('Score every coding sequence of a genome against the genome's own codon usage') plus the discriminating output ('genes whose synonymous codon choice that average does not explain'). That self-referential model distinguishes it from generic siblings like codon_usage_analysis or codon_optimality, though no sibling is named explicitly.

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?

Usage is implied by the model: the tool makes sense when a genome has enough CDSs to build its own reference usage. There is no explicit when-to-use vs alternatives (codon_usage_analysis, codon_usage_distance, codon_optimality) and no stated prerequisites or exclusions, so the agent must infer selection from the description alone.

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