list_datasets

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

With no annotations provided, the description carries the full behavioral burden. It does disclose two useful traits—results are filtered by policy and the project parameter defaults to the configured project—but it does not clarify whether the operation is strictly read-only, how errors are handled, or how policy filtering behaves.

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 two short sentences with no filler. The core action is front-loaded and the parameter default is stated immediately after, making it easy to scan and parse.

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?

This is a low-complexity tool with one optional parameter and an output schema, so the description does not need to explain return values. It provides the essential invocation facts—what is listed and what happens when project is omitted—though it could briefly mention the distinction from list_tables.

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%, so the description must compensate. It adds meaning by stating that project defaults to the configured project when omitted, which is not evident from the schema, but it does not explain what 'configured project' means or how explicit null behaves.

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 uses a specific verb ('List') and resource ('datasets') plus the policy constraint, so the agent knows exactly what the tool returns. It is also naturally differentiated from siblings like list_tables and describe_table without needing to open the schema.

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 explicit guidance is given about when to choose list_datasets over list_tables, describe_table, dry_run, or run_query. The only usage hint is the project-default behavior, which concerns the parameter rather than tool selection.

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