list_tables

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions the action (listing) and the input format, but does not disclose whether the operation is read-only, any permission requirements, or what happens if the dataset does not exist. It is a minimal disclosure, offering no insight into side effects or error behavior.

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 a single, concise sentence that front-loads the action and resource. It contains no filler or redundant information, and every word contributes to understanding. It is an example of appropriately sized, efficient writing.

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?

For a simple tool with one parameter and an output schema, the description provides the essential action and input format. However, it lacks context about return values (though an output schema exists), error conditions, or any special behavior. It is adequate but not thorough; an agent would need to rely on the output schema for return details and might be uncertain about edge cases.

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 provides only a title 'Dataset' with no description, so schema coverage is 0%. The description compensates by clarifying that the dataset parameter can be given as `dataset` or `project.dataset`, which adds meaning beyond the schema. However, it does not elaborate on the exact format (e.g., project ID requirements) or any constraints, so it only partially covers the parameter semantics.

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 the verb 'List' and the resource 'tables in a dataset', and specifies the input format (`dataset` or `project.dataset`). It is distinguishable from siblings: list_datasets lists datasets, describe_table describes a single table, etc. The purpose is unambiguous and specific, though it does not explicitly contrast with siblings.

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 no guidance on when to use this tool versus alternatives. It only states what it does without any context about selecting it over list_datasets or describe_table. There is no mention of when not to use it or any conditions for use.

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