run_batch_prediction

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

With no annotations, the description carries full burden. It explains the fire-and-poll default, auto-resolution behavior, billing implications, and wait option. While it doesn't mention auth or rate limits, it covers the key behavioral traits for a batch prediction tool.

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?

Three well-structured sentences convey purpose, key parameter subtleties, and asynchronous behavior without redundancy. Every sentence adds value, and the description is front-loaded with the core action.

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 (7 parameters, output schema exists, 23 siblings), the description covers the core workflow, billing, and async mechanics. The only minor gap is no explicit explanation for model_version_id in parameter context, but overall it's sufficient for an agent to invoke correctly.

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 coverage is 0%, so description must compensate. It explains project_id/target_feature auto-resolution, dataset_version_id's dual role, and wait_for_result effect. However, model_version_id is not described beyond the tool's purpose (though it's implied as the trained model). Still adds significant value over the empty schema.

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 starts with a clear verb+resource phrase: 'Score an entire dataset against a trained model version.' It explicitly distinguishes from sibling tools like run_prediction and get_prediction, making the tool's purpose unambiguous.

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?

Provides explicit guidance on when to omit optional parameters (auto-resolved from model version), how dataset_version_id drives billing, and how to choose between fire-and-poll vs blocking. References get_prediction as the status-check alternative, giving clear usage context.

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