list_datasets

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

No annotations are provided, so the description carries the full burden. It clearly conveys the tool's scope and examples, but does not explicitly state the return format or pagination behavior. However, for a straightforward list operation the behavior is largely self-evident, so this is a minor gap rather than a critical omission.

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 sentences long, front-loads the core action, and every phrase serves a purpose. It is concise yet packed with useful guidance and sibling references.

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?

Given the low complexity (single parameter, no output schema), the description fully covers what a user needs to know: what it does, when to use it, and how it fits with other tools. The absence of an output schema is mitigated by the clear implication that the tool returns a list of datasets for further action.

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 coverage is 100% and the parameter is fully described with an enum and examples. The tool description adds only marginal context (e.g., 'all ABS datasets') without introducing new parameter semantics, so the baseline of 3 is appropriate.

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 ('enumerate') and resource ('every curated dataset a source exposes'), making the tool's function unmistakable. It also distinguishes itself from siblings by explicitly naming search_datasets, describe_dataset, and get_data as alternatives for different tasks.

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 explicitly states when to use this tool ('for surveying what's available before picking one to describe or fetch') and contrasts it with sibling tools ('Pairs with search_datasets (free-text discovery), describe_dataset (schema), and get_data (fetch)'). This gives clear guidance on selection.

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