optimize_evolve

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

Adds context beyond annotations by disclosing stochastic nature ('same input gives different best chromosome each run') and that it is 'Free'. Annotations already provide readOnlyHint, so description supplements with behavioral traits.

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?

Three sentences: purpose, usage guidelines, and stochastic disclosure. Each sentence adds value without redundancy. Front-loaded with most important information.

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?

Covers purpose, domain, alternatives, and stochasticity. Given output schema exists, return values are not required. Minor gap: explicit description of how to set up multi-objective optimization could be helpful, but phrase 'with optional Pareto frontier' sufficiently hints at it.

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 89%, so baseline is 3. Description does not elaborate on parameters beyond mentioning 'optional Pareto frontier' which is implied behavior, not parameter-specific. Adequate but not additional value.

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 clearly states it is a 'genetic algorithm for combinatorial / discrete optimization with optional Pareto frontier for multi-objective problems', using specific verb and resource. It distinguishes from sibling optimize_cmaes by domain (discrete vs continuous).

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?

Explicitly says 'Use when the search space is discrete or mixed' and directs to optimize_cmaes for continuous problems. Also mentions stochastic behavior, providing clear when-to-use and when-not-to-use advice.

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