predict_print_settings

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 describes the fallback logic (hash then geometric then defaults), which is helpful. However, it does not state whether the tool is read-only, what data it accesses, or potential side effects.

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, with a clear algorithm summary followed by an Args block. The Args block is necessary but slightly repetitive with the schema. Overall, every sentence adds value.

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?

No output schema is provided, and the description does not explain what the prediction returns (e.g., settings list, confidence scores). It also omits prerequisites, such as whether historical DNA data must exist, or what happens if no match is found.

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 schema has 0% description coverage, but the description's Args section provides brief explanations for each parameter (e.g., 'SHA-256 hash of the model file'), adding meaning beyond the schema titles. However, these are minimal and lack valid ranges or examples.

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 predicts optimal print settings from historical DNA data. It specifies the algorithm (hash match, geometric similarity, material defaults), which adds specificity. However, it does not explicitly differentiate from sibling tools like infer_print_settings.

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. The description does not mention prerequisites, such as needing historical data, or when to prefer this over infer_print_settings or recommend_settings.

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