optimize_hyperparams

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

With no annotations, the description must disclose behavioral traits. It does not state whether the tool is read-only or modifies state, required permissions, rate limits, or side effects. The inference that it only suggests adjustments is implicit.

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 single-sentence description is concise but under-specified. It could include additional useful information (e.g., output format, typical input shape) without becoming overly verbose.

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?

Despite having an output schema (not shown), the description omits details about the return value, adjustable hyperparameters, and algorithm used. For a tool with one parameter and no annotation guidance, this level of detail is insufficient for correct agent invocation.

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 description adds minimal meaning to the 'metrics' parameter: 'from local training metrics' implies the input should contain training metrics, but does not specify expected keys, format, or structure. Schema coverage is 0%, so the description should compensate more.

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 the tool's function: 'Suggest hyperparameter adjustments from local training metrics.' It distinguishes from siblings like 'compute_metrics' by focusing on adjustments rather than just calculation, but lacks specificity on the nature of suggestions.

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?

No guidance on when to use this tool versus alternatives like evaluate_on_validation_set or early_stopping_check. The description does not mention prerequisites, context, or exclusions.

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