tune_hyperparameters

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

No annotations are provided, so the description must fully disclose behavior. It only mentions 'perform comprehensive hyperparameter tuning' without detailing side effects, return values, or whether it modifies state (e.g., datasets, experiments). The behavioral traits required for safe invocation are largely missing.

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 a single sentence with no unnecessary words. It front-loads the primary purpose, making it immediately clear what the tool does. Every part is relevant and concise.

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 the complexity (9 parameters, no output schema, no annotations), the description is insufficient. It does not explain what happens after tuning (e.g., output, logging), how errors are handled, or what state changes occur. This leaves significant gaps for an AI agent to safely invoke the tool.

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%, so the schema already documents all parameters. The description adds no additional meaning beyond the schema, such as relationships between parameters or usage patterns. Baseline of 3 is appropriate.

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 that the tool performs hyperparameter tuning for a model using various search strategies. It uses a specific verb ('tune') and resource ('hyperparameters'), and the purpose is distinct from sibling tools like 'train_model' and 'evaluate_model'.

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 implies that this tool should be used for hyperparameter tuning rather than model training, but it does not explicitly state when to use it vs alternatives or provide guidance on when not to use it. No context on prerequisites or configurations is given.

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