Get Validation Data Statistics

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds no additional behavioral context, such as that the tool returns aggregated statistics without 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?

Description is a single concise sentence with no wasted words. Appropriate for a simple tool, though lacks structured sections.

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?

Description adequately conveys the tool's purpose but does not mention return format or any conditions (e.g., what happens if validation data is missing). Absence of output schema increases need for more detail.

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 100%, with both parameters (id and type) fully described. The description adds no extra meaning beyond the schema for these parameters.

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?

Description clearly states the tool retrieves statistics about validation data for a model, distinguishing it from related tools like get_validation_data or get_validation_data_docs. However, it does not specify what types of statistics are returned.

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 such as transkribus_model_get_validation_data or transkribus_model_get_train_data_stats. Missing prerequisites or context (e.g., model must exist, validation set must be present).

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