list_datasets

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

Annotations already mark this as read-only/idempotent, and the description adds valuable behavioral context: the output is deliberately small and cheap, it returns a Markdown table plus caveats, and it notes failure returns 'Error: <message>'. This goes well beyond the schema.

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 concise yet complete: a clear one-sentence summary, a 'Start here' hint, a structured Returns section, and use/non-use examples. Every sentence adds value without redundancy.

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 zero-parameter discovery tool, the description covers purpose, when to use, output format, failure behavior, and even the caveats section. It is fully sufficient for an agent to invoke correctly.

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 tool has zero parameters, so there are no parameter semantics to explain. Per the rubric, a baseline of 4 applies; the description correctly omits parameter details since none exist.

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 opens with a specific verb and resource: 'List every table in the warehouse with its row count, grain, and caveats.' It clearly distinguishes from siblings by contrasting with describe_table ('Don't use when... need its columns').

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 gives explicit when-to-use guidance: 'Start here' and examples for use ('What data do I have access to?') and non-use ('use describe_table instead'). It names the alternative tool, making the decision clear.

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