update_dataset

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

With no annotations, the description carries the full burden. It discloses that updating SQL causes charts to refresh on next refresh and explains the dry_run behavior. Missing authorization needs or potential column metadata loss, but the provided details are valuable.

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 description is front-loaded with the purpose, then a usage hint, a behavioral note, and a clear parameter list. Every sentence adds value, no redundancy, and it's appropriately sized for the tool's complexity.

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 covers purpose, prerequisite, side effect, and all parameters. The output schema exists, so return value documentation is not required. Minor gaps like permissions or reversibility are missing, but overall it's thorough given the complexity.

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 input schema has 0% description coverage, but the description's Args section fully explains each of the 6 parameters, including purpose and recommendations (e.g., override_columns recommended when changing SQL). This adds significant meaning beyond the 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 clearly states it updates an existing dataset's SQL, name, or description. This verb+resource combination distinguishes it from create_dataset (creates new) and get_dataset (reads), and it is specific among sibling update tools.

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 advises using list_datasets to find the dataset_id, providing a clear prerequisite. However, it does not explicitly mention when not to use or list alternatives, though the context of updating versus creating or querying is implied.

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