Export event windows as a LeRobot training dataset (beta)

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

Annotations already disclose idempotency and non-destructiveness; the description adds valuable context beyond that: data is resampled to uniform fps, windows become episodes, and feature vectors are composed. The beta caveat ('awaiting validation by real training runs') is honest about maturity. No contradiction with annotations.

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 tightly written sentences deliver purpose, mechanism, workflow, and beta status with no filler. The most important information is front-loaded in the first sentence.

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?

The description is strong on the high-level transformation and workflow, and an output schema exists, but the tool is complex with nine parameters and nested objects. Given 0% schema description coverage, the description does not fully compensate by explaining required argument semantics, so completeness is only adequate.

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 0%, yet the description does not compensate for the nine parameters. It mentions fps and signals/features at a high level, but does not explain required parameters like path, topics, episodes, features, task, or optional ones like args and robot_type. The agent would have to guess at the intended meanings and formats.

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 a specific verb and resource: 'Export time windows as a LeRobotDataset v3.0 for robot-learning training.' It further details the transformation semantics (each window becomes an episode, resampled to uniform fps, signals become feature vectors), which distinguishes it from sibling export tools like export_for_plotjuggler and export_for_rerun.

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 provides explicit workflow context: 'Use after preview_pipeline to turn detected events into a curated dataset.' This clearly indicates when the tool should be invoked. However, it does not mention when-not-to-use or name alternatives for different export formats.

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