predict

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

No annotations are provided, so the description carries the full burden; it explains the in_domain/out_of_domain result, warns that out-of-domain estimates are unreliable, and directs the agent to surface say_to_user. This is meaningful behavioral context beyond the schema.

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 first sentence states purpose; the second gives usage and an example; the final block covers the critical reliability caveat. The prose is a bit detailed on out_of_domain, but every sentence earns its place and the important information is front-loaded.

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?

For a two-parameter prediction tool with an output schema, the description covers invocation, expected feature shape, and the reliability caveat. It is complete enough for an agent to call it correctly and decide how to present the result.

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 compensates by explaining that model_id comes from list_models, that features must match the model's expected features, and by giving a concrete JSON example. It clarifies both required parameters beyond their names and types.

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 opening sentence identifies a specific verb ('Predict'), a concrete resource ('target value ... for a single part'), and an example target ('price'). By explicitly limiting to a single part, it separates itself from siblings like predict_batch and predict_from_csv.

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 instructs the agent to pass the model_id from list_models and a features dictionary matching the model's expected features, giving a concrete invocation context. It does not name when-not conditions or alternatives (e.g., when to prefer predict_batch), so it stops short of a full exclusionary guide.

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