optimize_ga_securely

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

With no annotations provided, the description carries the behavioral transparency burden. It discloses the sandboxed secure execution environment, timeout risks, and performance/compute trade-offs via the given constraints. It does not describe error behavior or output details, but the output schema covers return structure.

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 concise and well-structured: a one-sentence purpose, a short ideal-use case, and a bulleted list of guidelines. Every sentence contributes value with no redundancy or fluff.

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 is moderately complex, with 3 params, an output schema, and security/sandbox context. The description covers purpose, use case, and key constraints, making it fairly complete. It does not detail the GA internals or failure modes, but these are non-essential given the schema and scope.

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?

While the input schema already covers all parameters (100% coverage), the description adds meaningful constraints beyond the schema: actuals size limit (5000), generations upper bound (1000), and confirmation of strict model_type enum. This enriches the agent's understanding of practical invocation limits.

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 the tool 'Executes a Genetic Algorithm in a secure remote Azure sandbox to minimize Mean Absolute Percentage Error (MAPE)' against ground-truth targets. This specific verb-resource pair clearly distinguishes it from sibling tools like 'execute_code_securely' or 'generate_plot_securely'.

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 a clear use case ('Ideal for driving down error metrics in complex time-series predictions') and specific operational limits (e.g., actuals ≤ 5000, generations < 1000). However, it does not explicitly mention alternative tools or exclusion scenarios, missing the 'when-not-to-use' guidance.

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