genetic_algorithm_optimization

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

With no annotations, the description explains the GA process (selection, crossover, mutation), error handling, and return structure (JSON with best solution, convergence history). This provides good behavioral insight beyond basic functionality.

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?

Description is well-structured with sections (Args, Returns, Raises, Note) and front-loaded purpose. While slightly lengthy, every paragraph adds value without redundancy.

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 an output schema exists, description adequately covers return format and algorithm internals. However, it could detail the expected structure of the optimization_problem object (e.g., required keys).

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 coverage is 0%, but the description adds meaning to both parameters: 'optimization_problem: Problem definition (objective, constraints, variables)' and 'ga_parameters: Optional GA parameters (population_size, generations, etc.)'. This compensates for the missing schema descriptions.

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 explicitly states 'Optimize spacecraft trajectory using genetic algorithm', which is a specific verb-resource pair and clearly distinguishes from siblings like particle_swarm_optimization.

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 lacks guidance on when to use GA vs other optimization tools (e.g., particle swarm). No context on problem types suited for GA or when not to use it.

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