Export event windows as a LeRobot training dataset (beta)

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

Discloses behaviors beyond annotations: confirms it writes a new dataset (consistent with readOnlyHint false), implies idempotency (consistent with idempotentHint), describes LOCF resampling, mentions return values (dataset directory, counts, loading instructions), and notes beta status with a specific caveat. These details add substantial transparency not captured in structured fields.

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 sentences, front-loaded with purpose and process, then returns, then usage guidance and beta caveat. Every sentence adds value without redundancy or irrelevant detail.

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?

While the description covers high-level behavior and return values, it omits any explanation of the 9 parameters, including nested structures like 'features' and 'episodes'. Given the complexity and zero schema coverage, the agent lacks essential input semantics. The beta note and usage guidance are helpful but do not compensate for this gap.

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 description provides zero parameter-specific semantics. It mentions 'scalar signals grouped into feature vectors' and 'uniform fps' but does not explain how parameters like 'features', 'episodes', 'topics', 'task', or 'path' map to the schema. With 0% schema coverage, this is a critical gap for an agent to construct valid inputs.

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?

States a specific verb ('Write') and resource ('event windows as a LeRobotDataset v3.0'), details the conversion process (episode per window, feature vector grouping, LOCF resampling), and differentiates from sibling export tools by naming the target format and use case. An agent can clearly distinguish this from export_for_plotjuggler, export_for_rerun, and export_for_lichtblick.

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?

Explicitly instructs when to use ('Choose this for robot-learning dataset preparation') and when not ('not interactive viewing or model training'). While it doesn't name alternative tools, the guidance is unambiguous and sufficient to route the agent correctly.

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